Merge pull request #535 from rustsim/sparse

Limited, experimental, sparse matrix support

Cargo.toml

@@ -23,8 +23,10 @@ stdweb          = [ "rand/stdweb" ]
 arbitrary       = [ "quickcheck" ]
 serde-serialize = [ "serde", "serde_derive", "num-complex/serde" ]
 abomonation-serialize = [ "abomonation" ]
+sparse = [ ]
 debug = [ ]
 alloc = [ ]
+io = [ "pest", "pest_derive" ]
 
 [dependencies]
 typenum        = "1.10"
@@ -33,13 +35,15 @@ rand           = { version = "0.6", default-features = false }
 num-traits     = { version = "0.2", default-features = false }
 num-complex    = { version = "0.2", default-features = false }
 approx         = { version = "0.3", default-features = false }
-alga           = { version = "0.7", default-features = false }
+alga           = { version = "0.8", default-features = false }
 matrixmultiply = { version = "0.2", optional = true }
 serde          = { version = "1.0", optional = true }
 serde_derive   = { version = "1.0", optional = true }
 abomonation    = { version = "0.7", optional = true }
 mint           = { version = "0.5", optional = true }
-quickcheck     = { version = "0.7", optional = true }
+quickcheck     = { version = "0.8", optional = true }
+pest           = { version = "2.0", optional = true }
+pest_derive    = { version = "2.0", optional = true }
 
 [dev-dependencies]
 serde_json = "1.0"

ci/build.sh

@@ -11,7 +11,7 @@ if [ -z "$NO_STD" ]; then
         cargo build --verbose -p nalgebra --features "serde-serialize";
         cargo build --verbose -p nalgebra --features "abomonation-serialize";
         cargo build --verbose -p nalgebra --features "debug";
-        cargo build --verbose -p nalgebra --features "debug arbitrary mint serde-serialize abomonation-serialize";
+        cargo build --verbose -p nalgebra --all-features
     else
         cargo build -p nalgebra-lapack;
     fi

nalgebra-glm/Cargo.toml

@@ -23,5 +23,5 @@ abomonation-serialize = [ "nalgebra/abomonation-serialize" ]
 [dependencies]
 num-traits = { version = "0.2", default-features = false }
 approx = { version = "0.3", default-features = false }
-alga = { version = "0.7", default-features = false }
+alga = { version = "0.8", default-features = false }
 nalgebra = { path =  "..", version = "^0.16.13", default-features = false }

nalgebra-glm/src/ext/matrix_transform.rs

@@ -9,7 +9,7 @@ where DefaultAllocator: Alloc<N, D, D> {
     TMat::<N, D, D>::identity()
 }
 
-/// Build a right hand look at view matrix
+/// Build a look at view matrix based on the right handedness.
 ///
 /// # Parameters:
 ///

nalgebra-glm/src/lib.rs

@@ -110,6 +110,7 @@
    and keep in mind it is possible to convert, e.g., an `Isometry3` to a `Mat4` and vice-versa (see the [conversions section](#conversions)).
 */
 
+#![doc(html_favicon_url = "http://nalgebra.org/img/favicon.ico")]
 #![cfg_attr(not(feature = "std"), no_std)]
 
 extern crate num_traits as num;

nalgebra-lapack/Cargo.toml

@@ -25,7 +25,7 @@ intel-mkl  = ["lapack-src/intel-mkl"]
 nalgebra      = { version = "0.16", path = ".." }
 num-traits    = "0.2"
 num-complex   = { version = "0.2", default-features = false }
-alga          = { version = "0.7", default-features = false }
+alga          = { version = "0.8", default-features = false }
 serde         = { version = "1.0", optional = true }
 serde_derive  = { version = "1.0", optional = true }
 lapack        = { version = "0.16", default-features = false }
@@ -34,6 +34,6 @@ lapack-src    = { version = "0.2", default-features = false }
 
 [dev-dependencies]
 nalgebra   = { version = "0.16", path = "..", features = [ "arbitrary" ] }
-quickcheck = "0.7"
+quickcheck = "0.8"
 approx     = "0.3"
 rand       = "0.6"

nalgebra-lapack/src/lib.rs

@@ -68,8 +68,10 @@
 #![deny(unused_qualifications)]
 #![deny(unused_results)]
 #![deny(missing_docs)]
-#![doc(html_favicon_url = "http://nalgebra.org/img/favicon.ico",
+#![doc(
-       html_root_url = "http://nalgebra.org/rustdoc")]
+    html_favicon_url = "http://nalgebra.org/img/favicon.ico",
+    html_root_url = "http://nalgebra.org/rustdoc"
+)]
 
 extern crate alga;
 extern crate lapack;

src/base/componentwise.rs

@@ -1,4 +1,4 @@
-// Non-conventional componentwise operators.
+// Non-conventional component-wise operators.
 
 use num::{Signed, Zero};
 use std::ops::{Add, Mul};

src/base/conversion.rs

@@ -12,8 +12,10 @@ use typenum::Prod;
 use base::allocator::{Allocator, SameShapeAllocator};
 use base::constraint::{SameNumberOfColumns, SameNumberOfRows, ShapeConstraint};
 use base::dimension::{
-    Dim, DimName, Dynamic, U1, U10, U11, U12, U13, U14, U15, U16, U2, U3, U4, U5, U6, U7, U8, U9,
+    Dim, DimName, U1, U10, U11, U12, U13, U14, U15, U16, U2, U3, U4, U5, U6, U7, U8, U9,
 };
+#[cfg(any(feature = "std", feature = "alloc"))]
+use base::dimension::Dynamic;
 use base::iter::{MatrixIter, MatrixIterMut};
 use base::storage::{ContiguousStorage, ContiguousStorageMut, Storage, StorageMut};
 #[cfg(any(feature = "std", feature = "alloc"))]

src/base/dimension.rs

@@ -364,7 +364,8 @@ impl<
         G: Bit + Any + Debug + Copy + PartialEq + Send + Sync,
     > IsNotStaticOne
     for UInt<UInt<UInt<UInt<UInt<UInt<UInt<UInt<UTerm, B1>, A>, B>, C>, D>, E>, F>, G>
-{}
+{
+}
 
 impl<U: Unsigned + DimName, B: Bit + Any + Debug + Copy + PartialEq + Send + Sync> NamedDim
     for UInt<U, B>
@@ -405,4 +406,5 @@ impl<U: Unsigned + DimName, B: Bit + Any + Debug + Copy + PartialEq + Send + Syn
 
 impl<U: Unsigned + DimName, B: Bit + Any + Debug + Copy + PartialEq + Send + Sync> IsNotStaticOne
     for UInt<U, B>
-{}
+{
+}

src/base/edition.rs

@@ -1,13 +1,18 @@
 use num::{One, Zero};
 use std::cmp;
 use std::ptr;
+#[cfg(any(feature = "std", feature = "alloc"))]
 use std::iter::ExactSizeIterator;
+#[cfg(any(feature = "std", feature = "alloc"))]
+use std::mem;
 
 use base::allocator::{Allocator, Reallocator};
 use base::constraint::{DimEq, SameNumberOfColumns, SameNumberOfRows, ShapeConstraint};
 use base::dimension::{
-    Dim, DimAdd, DimDiff, DimMin, DimMinimum, DimName, DimSub, DimSum, Dynamic, U1,
+    Dim, DimAdd, DimDiff, DimMin, DimMinimum, DimName, DimSub, DimSum, U1,
 };
+#[cfg(any(feature = "std", feature = "alloc"))]
+use base::dimension::Dynamic;
 use base::storage::{Storage, StorageMut};
 #[cfg(any(feature = "std", feature = "alloc"))]
 use base::DMatrix;
@@ -35,6 +40,7 @@ impl<N: Scalar + Zero, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
     }
 
     /// Creates a new matrix by extracting the given set of rows from `self`.
+    #[cfg(any(feature = "std", feature = "alloc"))]
     pub fn select_rows<'a, I>(&self, irows: I) -> MatrixMN<N, Dynamic, C>
         where I: IntoIterator<Item = &'a usize>,
               I::IntoIter: ExactSizeIterator + Clone,
@@ -65,6 +71,7 @@ impl<N: Scalar + Zero, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
     }
 
     /// Creates a new matrix by extracting the given set of columns from `self`.
+    #[cfg(any(feature = "std", feature = "alloc"))]
     pub fn select_columns<'a, I>(&self, icols: I) -> MatrixMN<N, R, Dynamic>
         where I: IntoIterator<Item = &'a usize>,
               I::IntoIter: ExactSizeIterator,
@@ -295,6 +302,7 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
 
     /// Removes `n` consecutive columns from this matrix, starting with the `i`-th (included).
     #[inline]
+    #[cfg(any(feature = "std", feature = "alloc"))]
     pub fn remove_columns(self, i: usize, n: usize) -> MatrixMN<N, R, Dynamic>
     where
         C: DimSub<Dynamic, Output = Dynamic>,
@@ -377,6 +385,7 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
 
     /// Removes `n` consecutive rows from this matrix, starting with the `i`-th (included).
     #[inline]
+    #[cfg(any(feature = "std", feature = "alloc"))]
     pub fn remove_rows(self, i: usize, n: usize) -> MatrixMN<N, Dynamic, C>
     where
         R: DimSub<Dynamic, Output = Dynamic>,
@@ -454,6 +463,7 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
 
     /// Inserts `n` columns filled with `val` starting at the `i-th` position.
     #[inline]
+    #[cfg(any(feature = "std", feature = "alloc"))]
     pub fn insert_columns(self, i: usize, n: usize, val: N) -> MatrixMN<N, R, Dynamic>
     where
         C: DimAdd<Dynamic, Output = Dynamic>,
@@ -531,6 +541,7 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
 
     /// Inserts `n` rows filled with `val` starting at the `i-th` position.
     #[inline]
+    #[cfg(any(feature = "std", feature = "alloc"))]
     pub fn insert_rows(self, i: usize, n: usize, val: N) -> MatrixMN<N, Dynamic, C>
     where
         R: DimAdd<Dynamic, Output = Dynamic>,
@@ -596,6 +607,29 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         self.resize_generic(Dynamic::new(new_nrows), Dynamic::new(new_ncols), val)
     }
 
+    /// Resizes this matrix vertically, i.e., so that it contains `new_nrows` rows while keeping the same number of columns.
+    ///
+    /// The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more
+    /// rows than `self`, then the extra rows are filled with `val`.
+    #[cfg(any(feature = "std", feature = "alloc"))]
+    pub fn resize_vertically(self, new_nrows: usize, val: N) -> MatrixMN<N, Dynamic, C>
+        where DefaultAllocator: Reallocator<N, R, C, Dynamic, C> {
+        let ncols = self.data.shape().1;
+        self.resize_generic(Dynamic::new(new_nrows), ncols, val)
+    }
+
+    /// Resizes this matrix horizontally, i.e., so that it contains `new_ncolumns` columns while keeping the same number of columns.
+    ///
+    /// The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more
+    /// columns than `self`, then the extra columns are filled with `val`.
+    #[cfg(any(feature = "std", feature = "alloc"))]
+    pub fn resize_horizontally(self, new_ncols: usize, val: N) -> MatrixMN<N, R, Dynamic>
+        where DefaultAllocator: Reallocator<N, R, C, R, Dynamic> {
+        let nrows = self.data.shape().0;
+        self.resize_generic(nrows, Dynamic::new(new_ncols), val)
+    }
+
+
     /// Resizes this matrix so that it contains `R2::value()` rows and `C2::value()` columns.
     ///
     /// The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more
@@ -673,6 +707,61 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
     }
 }
 
+#[cfg(any(feature = "std", feature = "alloc"))]
+impl<N: Scalar> DMatrix<N> {
+    /// Resizes this matrix in-place.
+    ///
+    /// The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more
+    /// rows and/or columns than `self`, then the extra rows or columns are filled with `val`.
+    ///
+    /// Defined only for owned fully-dynamic matrices, i.e., `DMatrix`.
+    pub fn resize_mut(&mut self, new_nrows: usize, new_ncols: usize, val: N)
+        where DefaultAllocator: Reallocator<N, Dynamic, Dynamic, Dynamic, Dynamic> {
+        let placeholder = unsafe { Self::new_uninitialized(0, 0) };
+        let old = mem::replace(self, placeholder);
+        let new = old.resize(new_nrows, new_ncols, val);
+        let _ = mem::replace(self, new);
+    }
+}
+
+#[cfg(any(feature = "std", feature = "alloc"))]
+impl<N: Scalar, C: Dim> MatrixMN<N, Dynamic, C>
+    where DefaultAllocator: Allocator<N, Dynamic, C> {
+    /// Changes the number of rows of this matrix in-place.
+    ///
+    /// The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more
+    /// rows than `self`, then the extra rows are filled with `val`.
+    ///
+    /// Defined only for owned matrices with a dynamic number of rows (for example, `DVector`).
+    #[cfg(any(feature = "std", feature = "alloc"))]
+    pub fn resize_vertically_mut(&mut self, new_nrows: usize, val: N)
+        where DefaultAllocator: Reallocator<N, Dynamic, C, Dynamic, C> {
+        let placeholder = unsafe { Self::new_uninitialized_generic(Dynamic::new(0), self.data.shape().1) };
+        let old = mem::replace(self, placeholder);
+        let new = old.resize_vertically(new_nrows, val);
+        let _ = mem::replace(self, new);
+    }
+}
+
+#[cfg(any(feature = "std", feature = "alloc"))]
+impl<N: Scalar, R: Dim> MatrixMN<N, R, Dynamic>
+    where DefaultAllocator: Allocator<N, R, Dynamic> {
+    /// Changes the number of column of this matrix in-place.
+    ///
+    /// The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more
+    /// columns than `self`, then the extra columns are filled with `val`.
+    ///
+    /// Defined only for owned matrices with a dynamic number of columns (for example, `DVector`).
+    #[cfg(any(feature = "std", feature = "alloc"))]
+    pub fn resize_horizontally_mut(&mut self, new_ncols: usize, val: N)
+        where DefaultAllocator: Reallocator<N, R, Dynamic, R, Dynamic> {
+        let placeholder = unsafe { Self::new_uninitialized_generic(self.data.shape().0, Dynamic::new(0)) };
+        let old = mem::replace(self, placeholder);
+        let new = old.resize_horizontally(new_ncols, val);
+        let _ = mem::replace(self, new);
+    }
+}
+
 unsafe fn compress_rows<N: Scalar>(
     data: &mut [N],
     nrows: usize,
@@ -753,6 +842,7 @@ unsafe fn extend_rows<N: Scalar>(
 
 /// Extend the number of columns of the `Matrix` with elements from
 /// a given iterator.
+#[cfg(any(feature = "std", feature = "alloc"))]
 impl<N, R, S> Extend<N> for Matrix<N, R, Dynamic, S>
 where
     N: Scalar,
@@ -800,6 +890,7 @@ where
 
 /// Extend the number of rows of the `Vector` with elements from
 /// a given iterator.
+#[cfg(any(feature = "std", feature = "alloc"))]
 impl<N, S> Extend<N> for Matrix<N, Dynamic, U1, S>
 where
     N: Scalar,
@@ -820,6 +911,7 @@ where
     }
 }
 
+#[cfg(any(feature = "std", feature = "alloc"))]
 impl<N, R, S, RV, SV> Extend<Vector<N, RV, SV>> for Matrix<N, R, Dynamic, S>
 where
     N: Scalar,

src/base/matrix_alga.rs

@@ -6,7 +6,7 @@ use num::{One, Zero};
 use alga::general::{
     AbstractGroup, AbstractGroupAbelian, AbstractLoop, AbstractMagma, AbstractModule,
     AbstractMonoid, AbstractQuasigroup, AbstractSemigroup, Additive, ClosedAdd, ClosedMul,
-    ClosedNeg, Field, Identity, Inverse, JoinSemilattice, Lattice, MeetSemilattice, Module,
+    ClosedNeg, Field, Identity, TwoSidedInverse, JoinSemilattice, Lattice, MeetSemilattice, Module,
     Multiplicative, Real, RingCommutative,
 };
 use alga::linear::{
@@ -45,18 +45,18 @@ where
     }
 }
 
-impl<N, R: DimName, C: DimName> Inverse<Additive> for MatrixMN<N, R, C>
+impl<N, R: DimName, C: DimName> TwoSidedInverse<Additive> for MatrixMN<N, R, C>
 where
     N: Scalar + ClosedNeg,
     DefaultAllocator: Allocator<N, R, C>,
 {
     #[inline]
-    fn inverse(&self) -> MatrixMN<N, R, C> {
+    fn two_sided_inverse(&self) -> MatrixMN<N, R, C> {
         -self
     }
 
     #[inline]
-    fn inverse_mut(&mut self) {
+    fn two_sided_inverse_mut(&mut self) {
         *self = -self.clone()
     }
 }

src/base/matrix_slice.rs

@@ -4,9 +4,9 @@ use std::slice;
 
 use base::allocator::Allocator;
 use base::default_allocator::DefaultAllocator;
-use base::dimension::{Dim, DimName, Dynamic, U1};
+use base::dimension::{Dim, DimName, Dynamic, U1, IsNotStaticOne};
 use base::iter::MatrixIter;
-use base::storage::{Owned, Storage, StorageMut};
+use base::storage::{Owned, Storage, StorageMut, ContiguousStorage, ContiguousStorageMut};
 use base::{Matrix, Scalar};
 
 macro_rules! slice_storage_impl(
@@ -91,7 +91,8 @@ slice_storage_impl!("A mutable matrix data storage for mutable matrix slice. Onl
 
 impl<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> Copy
     for SliceStorage<'a, N, R, C, RStride, CStride>
-{}
+{
+}
 
 impl<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> Clone
     for SliceStorage<'a, N, R, C, RStride, CStride>
@@ -146,8 +147,6 @@ macro_rules! storage_impl(
                 }
             }
 
-
-
             #[inline]
             fn into_owned(self) -> Owned<N, R, C>
                 where DefaultAllocator: Allocator<N, R, C> {
@@ -199,6 +198,14 @@ unsafe impl<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> StorageMu
     }
 }
 
+unsafe impl<'a, N: Scalar, R: Dim, CStride: Dim> ContiguousStorage<N, R, U1> for SliceStorage<'a, N, R, U1, U1, CStride> { }
+unsafe impl<'a, N: Scalar, R: Dim, CStride: Dim> ContiguousStorage<N, R, U1> for SliceStorageMut<'a, N, R, U1, U1, CStride> { }
+unsafe impl<'a, N: Scalar, R: Dim, CStride: Dim> ContiguousStorageMut<N, R, U1> for SliceStorageMut<'a, N, R, U1, U1, CStride> { }
+
+unsafe impl<'a, N: Scalar, R: DimName, C: Dim + IsNotStaticOne> ContiguousStorage<N, R, C> for SliceStorage<'a, N, R, C, U1, R> { }
+unsafe impl<'a, N: Scalar, R: DimName, C: Dim + IsNotStaticOne> ContiguousStorage<N, R, C> for SliceStorageMut<'a, N, R, C, U1, R> { }
+unsafe impl<'a, N: Scalar, R: DimName, C: Dim + IsNotStaticOne> ContiguousStorageMut<N, R, C> for SliceStorageMut<'a, N, R, C, U1, R> { }
+
 impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
     #[inline]
     fn assert_slice_index(
@@ -859,3 +866,25 @@ impl<N: Scalar, R: Dim, C: Dim, S: StorageMut<N, R, C>> Matrix<N, R, C, S> {
         self.slice_range_mut(.., cols)
     }
 }
+
+
+impl<'a, N, R, C, RStride, CStride> From<MatrixSliceMut<'a, N, R, C, RStride, CStride>>
+for MatrixSlice<'a, N, R, C, RStride, CStride>
+    where
+        N: Scalar,
+        R: Dim,
+        C: Dim,
+        RStride: Dim,
+        CStride: Dim,
+{
+    fn from(slice_mut: MatrixSliceMut<'a, N, R, C, RStride, CStride>) -> Self {
+        let data = SliceStorage {
+            ptr:       slice_mut.data.ptr,
+            shape:     slice_mut.data.shape,
+            strides:   slice_mut.data.strides,
+            _phantoms: PhantomData,
+        };
+
+        unsafe { Matrix::from_data_statically_unchecked(data) }
+    }
+}

src/base/ops.rs

@@ -761,7 +761,7 @@ where
 }
 
 impl<N: Scalar + PartialOrd + Signed, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
-    /// Returns the absolute value of the coefficient with the largest absolute value.
+    /// Returns the absolute value of the component with the largest absolute value.
     #[inline]
     pub fn amax(&self) -> N {
         let mut max = N::zero();
@@ -777,7 +777,7 @@ impl<N: Scalar + PartialOrd + Signed, R: Dim, C: Dim, S: Storage<N, R, C>> Matri
         max
     }
 
-    /// Returns the absolute value of the coefficient with the smallest absolute value.
+    /// Returns the absolute value of the component with the smallest absolute value.
     #[inline]
     pub fn amin(&self) -> N {
         let mut it = self.iter();
@@ -796,4 +796,42 @@ impl<N: Scalar + PartialOrd + Signed, R: Dim, C: Dim, S: Storage<N, R, C>> Matri
 
         min
     }
+
+    /// Returns the component with the largest value.
+    #[inline]
+    pub fn max(&self) -> N {
+        let mut it = self.iter();
+        let mut max = it
+            .next()
+            .expect("max: empty matrices not supported.");
+
+        for e in it {
+            let ae = e;
+
+            if ae > max {
+                max = ae;
+            }
+        }
+
+        *max
+    }
+
+    /// Returns the component with the smallest value.
+    #[inline]
+    pub fn min(&self) -> N {
+        let mut it = self.iter();
+        let mut min = it
+            .next()
+            .expect("min: empty matrices not supported.");
+
+        for e in it {
+            let ae = e;
+
+            if ae < min {
+                min = ae;
+            }
+        }
+
+        *min
+    }
 }

src/geometry/isometry.rs

@@ -166,7 +166,7 @@ where DefaultAllocator: Allocator<N, D>
     /// ```
     #[inline]
     pub fn inverse_mut(&mut self) {
-        self.rotation.inverse_mut();
+        self.rotation.two_sided_inverse_mut();
         self.translation.inverse_mut();
         self.translation.vector = self.rotation.transform_vector(&self.translation.vector);
     }

src/geometry/isometry_alga.rs

@@ -1,6 +1,6 @@
 use alga::general::{
     AbstractGroup, AbstractLoop, AbstractMagma, AbstractMonoid, AbstractQuasigroup,
-    AbstractSemigroup, Id, Identity, Inverse, Multiplicative, Real,
+    AbstractSemigroup, Id, Identity, TwoSidedInverse, Multiplicative, Real,
 };
 use alga::linear::Isometry as AlgaIsometry;
 use alga::linear::{
@@ -30,18 +30,18 @@ where
     }
 }
 
-impl<N: Real, D: DimName, R> Inverse<Multiplicative> for Isometry<N, D, R>
+impl<N: Real, D: DimName, R> TwoSidedInverse<Multiplicative> for Isometry<N, D, R>
 where
     R: Rotation<Point<N, D>>,
     DefaultAllocator: Allocator<N, D>,
 {
     #[inline]
-    fn inverse(&self) -> Self {
+    fn two_sided_inverse(&self) -> Self {
         self.inverse()
     }
 
     #[inline]
-    fn inverse_mut(&mut self) {
+    fn two_sided_inverse_mut(&mut self) {
         self.inverse_mut()
     }
 }

src/geometry/isometry_construction.rs

@@ -16,7 +16,7 @@ use base::{DefaultAllocator, Vector2, Vector3};
 
 use geometry::{
     Isometry, Point, Point3, Rotation, Rotation2, Rotation3, Translation, UnitComplex,
-    UnitQuaternion,
+    UnitQuaternion, Translation2, Translation3
 };
 
 impl<N: Real, D: DimName, R: AlgaRotation<Point<N, D>>> Isometry<N, D, R>
@@ -129,6 +129,18 @@ impl<N: Real> Isometry<N, U2, Rotation2<N>> {
             Rotation::<N, U2>::new(angle),
         )
     }
+
+    /// Creates a new isometry from the given translation coordinates.
+    #[inline]
+    pub fn translation(x: N, y: N) -> Self {
+        Self::new(Vector2::new(x, y), N::zero())
+    }
+
+    /// Creates a new isometry from the given rotation angle.
+    #[inline]
+    pub fn rotation(angle: N) -> Self {
+        Self::new(Vector2::zeros(), angle)
+    }
 }
 
 impl<N: Real> Isometry<N, U2, UnitComplex<N>> {
@@ -152,6 +164,18 @@ impl<N: Real> Isometry<N, U2, UnitComplex<N>> {
             UnitComplex::from_angle(angle),
         )
     }
+
+    /// Creates a new isometry from the given translation coordinates.
+    #[inline]
+    pub fn translation(x: N, y: N) -> Self {
+        Self::from_parts(Translation2::new(x, y), UnitComplex::identity())
+    }
+
+    /// Creates a new isometry from the given rotation angle.
+    #[inline]
+    pub fn rotation(angle: N) -> Self {
+        Self::new(Vector2::zeros(), angle)
+    }
 }
 
 // 3D rotation.
@@ -189,6 +213,18 @@ macro_rules! isometry_construction_impl(
                     $RotId::<$($RotParams),*>::from_scaled_axis(axisangle))
             }
 
+            /// Creates a new isometry from the given translation coordinates.
+            #[inline]
+            pub fn translation(x: N, y: N, z: N) -> Self {
+                Self::from_parts(Translation3::new(x, y, z), $RotId::identity())
+            }
+
+            /// Creates a new isometry from the given rotation angle.
+            #[inline]
+            pub fn rotation(axisangle: Vector3<N>) -> Self {
+                Self::new(Vector3::zeros(), axisangle)
+            }
+
             /// Creates an isometry that corresponds to the local frame of an observer standing at the
             /// point `eye` and looking toward `target`.
             ///

src/geometry/isometry_ops.rs

@@ -200,8 +200,8 @@ isometry_binop_assign_impl_all!(
     DivAssign, div_assign;
     self: Isometry<N, D, R>, rhs: R;
     // FIXME: don't invert explicitly?
-    [val] => *self *= rhs.inverse();
+    [val] => *self *= rhs.two_sided_inverse();
-    [ref] => *self *= rhs.inverse();
+    [ref] => *self *= rhs.two_sided_inverse();
 );
 
 // Isometry × R

src/geometry/mod.rs

@@ -37,6 +37,7 @@ mod translation_alga;
 mod translation_alias;
 mod translation_construction;
 mod translation_conversion;
+mod translation_coordinates;
 mod translation_ops;
 
 mod isometry;

src/geometry/quaternion_alga.rs

@@ -2,7 +2,7 @@ use num::Zero;
 
 use alga::general::{
     AbstractGroup, AbstractGroupAbelian, AbstractLoop, AbstractMagma, AbstractModule,
-    AbstractMonoid, AbstractQuasigroup, AbstractSemigroup, Additive, Id, Identity, Inverse, Module,
+    AbstractMonoid, AbstractQuasigroup, AbstractSemigroup, Additive, Id, Identity, TwoSidedInverse, Module,
     Multiplicative, Real,
 };
 use alga::linear::{
@@ -42,9 +42,9 @@ impl<N: Real> AbstractMagma<Additive> for Quaternion<N> {
     }
 }
 
-impl<N: Real> Inverse<Additive> for Quaternion<N> {
+impl<N: Real> TwoSidedInverse<Additive> for Quaternion<N> {
     #[inline]
-    fn inverse(&self) -> Self {
+    fn two_sided_inverse(&self) -> Self {
         -self
     }
 }
@@ -173,14 +173,14 @@ impl<N: Real> AbstractMagma<Multiplicative> for UnitQuaternion<N> {
     }
 }
 
-impl<N: Real> Inverse<Multiplicative> for UnitQuaternion<N> {
+impl<N: Real> TwoSidedInverse<Multiplicative> for UnitQuaternion<N> {
     #[inline]
-    fn inverse(&self) -> Self {
+    fn two_sided_inverse(&self) -> Self {
         self.inverse()
     }
 
     #[inline]
-    fn inverse_mut(&mut self) {
+    fn two_sided_inverse_mut(&mut self) {
         self.inverse_mut()
     }
 }

src/geometry/rotation_alga.rs

@@ -1,6 +1,6 @@
 use alga::general::{
     AbstractGroup, AbstractLoop, AbstractMagma, AbstractMonoid, AbstractQuasigroup,
-    AbstractSemigroup, Id, Identity, Inverse, Multiplicative, Real,
+    AbstractSemigroup, Id, Identity, TwoSidedInverse, Multiplicative, Real,
 };
 use alga::linear::{
     self, AffineTransformation, DirectIsometry, Isometry, OrthogonalTransformation,
@@ -27,16 +27,16 @@ where DefaultAllocator: Allocator<N, D, D>
     }
 }
 
-impl<N: Real, D: DimName> Inverse<Multiplicative> for Rotation<N, D>
+impl<N: Real, D: DimName> TwoSidedInverse<Multiplicative> for Rotation<N, D>
 where DefaultAllocator: Allocator<N, D, D>
 {
     #[inline]
-    fn inverse(&self) -> Self {
+    fn two_sided_inverse(&self) -> Self {
         self.transpose()
     }
 
     #[inline]
-    fn inverse_mut(&mut self) {
+    fn two_sided_inverse_mut(&mut self) {
         self.transpose_mut()
     }
 }

src/geometry/similarity_alga.rs

@@ -1,6 +1,6 @@
 use alga::general::{
     AbstractGroup, AbstractLoop, AbstractMagma, AbstractMonoid, AbstractQuasigroup,
-    AbstractSemigroup, Identity, Inverse, Multiplicative, Real,
+    AbstractSemigroup, Identity, TwoSidedInverse, Multiplicative, Real,
 };
 use alga::linear::Similarity as AlgaSimilarity;
 use alga::linear::{AffineTransformation, ProjectiveTransformation, Rotation, Transformation};
@@ -27,18 +27,18 @@ where
     }
 }
 
-impl<N: Real, D: DimName, R> Inverse<Multiplicative> for Similarity<N, D, R>
+impl<N: Real, D: DimName, R> TwoSidedInverse<Multiplicative> for Similarity<N, D, R>
 where
     R: Rotation<Point<N, D>>,
     DefaultAllocator: Allocator<N, D>,
 {
     #[inline]
-    fn inverse(&self) -> Self {
+    fn two_sided_inverse(&self) -> Self {
         self.inverse()
     }
 
     #[inline]
-    fn inverse_mut(&mut self) {
+    fn two_sided_inverse_mut(&mut self) {
         self.inverse_mut()
     }
 }

src/geometry/similarity_ops.rs

@@ -222,8 +222,8 @@ similarity_binop_assign_impl_all!(
     DivAssign, div_assign;
     self: Similarity<N, D, R>, rhs: R;
     // FIXME: don't invert explicitly?
-    [val] => *self *= rhs.inverse();
+    [val] => *self *= rhs.two_sided_inverse();
-    [ref] => *self *= rhs.inverse();
+    [ref] => *self *= rhs.two_sided_inverse();
 );
 
 // Similarity × R

src/geometry/transform_alga.rs

@@ -1,6 +1,6 @@
 use alga::general::{
     AbstractGroup, AbstractLoop, AbstractMagma, AbstractMonoid, AbstractQuasigroup,
-    AbstractSemigroup, Identity, Inverse, Multiplicative, Real,
+    AbstractSemigroup, Identity, TwoSidedInverse, Multiplicative, Real,
 };
 use alga::linear::{ProjectiveTransformation, Transformation};
 
@@ -26,18 +26,18 @@ where
     }
 }
 
-impl<N: Real, D: DimNameAdd<U1>, C> Inverse<Multiplicative> for Transform<N, D, C>
+impl<N: Real, D: DimNameAdd<U1>, C> TwoSidedInverse<Multiplicative> for Transform<N, D, C>
 where
     C: SubTCategoryOf<TProjective>,
     DefaultAllocator: Allocator<N, DimNameSum<D, U1>, DimNameSum<D, U1>>,
 {
     #[inline]
-    fn inverse(&self) -> Self {
+    fn two_sided_inverse(&self) -> Self {
         self.clone().inverse()
     }
 
     #[inline]
-    fn inverse_mut(&mut self) {
+    fn two_sided_inverse_mut(&mut self) {
         self.inverse_mut()
     }
 }
@@ -116,12 +116,12 @@ where
 {
     #[inline]
     fn inverse_transform_point(&self, pt: &Point<N, D>) -> Point<N, D> {
-        self.inverse() * pt
+        self.two_sided_inverse() * pt
     }
 
     #[inline]
     fn inverse_transform_vector(&self, v: &VectorN<N, D>) -> VectorN<N, D> {
-        self.inverse() * v
+        self.two_sided_inverse() * v
     }
 }
 

src/geometry/translation_alga.rs

@@ -1,6 +1,6 @@
 use alga::general::{
     AbstractGroup, AbstractLoop, AbstractMagma, AbstractMonoid, AbstractQuasigroup,
-    AbstractSemigroup, Id, Identity, Inverse, Multiplicative, Real,
+    AbstractSemigroup, Id, Identity, TwoSidedInverse, Multiplicative, Real,
 };
 use alga::linear::Translation as AlgaTranslation;
 use alga::linear::{
@@ -28,16 +28,16 @@ where DefaultAllocator: Allocator<N, D>
     }
 }
 
-impl<N: Real, D: DimName> Inverse<Multiplicative> for Translation<N, D>
+impl<N: Real, D: DimName> TwoSidedInverse<Multiplicative> for Translation<N, D>
 where DefaultAllocator: Allocator<N, D>
 {
     #[inline]
-    fn inverse(&self) -> Self {
+    fn two_sided_inverse(&self) -> Self {
         self.inverse()
     }
 
     #[inline]
-    fn inverse_mut(&mut self) {
+    fn two_sided_inverse_mut(&mut self) {
         self.inverse_mut()
     }
 }

src/geometry/translation_coordinates.rs

@@ -0,0 +1,44 @@
+use std::mem;
+use std::ops::{Deref, DerefMut};
+
+use base::allocator::Allocator;
+use base::coordinates::{X, XY, XYZ, XYZW, XYZWA, XYZWAB};
+use base::dimension::{U1, U2, U3, U4, U5, U6};
+use base::{DefaultAllocator, Scalar};
+
+use geometry::Translation;
+
+/*
+ *
+ * Give coordinates to Translation{1 .. 6}
+ *
+ */
+
+macro_rules! deref_impl(
+    ($D: ty, $Target: ident $(, $comps: ident)*) => {
+        impl<N: Scalar> Deref for Translation<N, $D>
+            where DefaultAllocator: Allocator<N, $D> {
+            type Target = $Target<N>;
+
+            #[inline]
+            fn deref(&self) -> &Self::Target {
+                unsafe { mem::transmute(self) }
+            }
+        }
+
+        impl<N: Scalar> DerefMut for Translation<N, $D>
+            where DefaultAllocator: Allocator<N, $D> {
+            #[inline]
+            fn deref_mut(&mut self) -> &mut Self::Target {
+                unsafe { mem::transmute(self) }
+            }
+        }
+    }
+);
+
+deref_impl!(U1, X, x);
+deref_impl!(U2, XY, x, y);
+deref_impl!(U3, XYZ, x, y, z);
+deref_impl!(U4, XYZW, x, y, z, w);
+deref_impl!(U5, XYZWA, x, y, z, w, a);
+deref_impl!(U6, XYZWAB, x, y, z, w, a, b);

src/geometry/unit_complex_alga.rs

@@ -1,6 +1,6 @@
 use alga::general::{
     AbstractGroup, AbstractLoop, AbstractMagma, AbstractMonoid, AbstractQuasigroup,
-    AbstractSemigroup, Id, Identity, Inverse, Multiplicative, Real,
+    AbstractSemigroup, Id, Identity, TwoSidedInverse, Multiplicative, Real,
 };
 use alga::linear::{
     AffineTransformation, DirectIsometry, Isometry, OrthogonalTransformation,
@@ -31,14 +31,14 @@ impl<N: Real> AbstractMagma<Multiplicative> for UnitComplex<N> {
     }
 }
 
-impl<N: Real> Inverse<Multiplicative> for UnitComplex<N> {
+impl<N: Real> TwoSidedInverse<Multiplicative> for UnitComplex<N> {
     #[inline]
-    fn inverse(&self) -> Self {
+    fn two_sided_inverse(&self) -> Self {
         self.inverse()
     }
 
     #[inline]
-    fn inverse_mut(&mut self) {
+    fn two_sided_inverse_mut(&mut self) {
         self.inverse_mut()
     }
 }

src/io/matrix_market.pest

@@ -0,0 +1,16 @@
+WHITESPACE = _{ " " }
+
+Comments = _{ "%" ~ (!NEWLINE ~ ANY)* }
+Header = { "%%" ~ (!NEWLINE ~ ANY)* }
+Shape = { Dimension ~ Dimension ~ Dimension }
+Document = {
+    SOI ~
+    NEWLINE* ~
+    Header ~
+    (NEWLINE ~ Comments)* ~
+    (NEWLINE ~ Shape) ~
+    (NEWLINE ~ Entry?)*
+}
+Dimension = @{ ASCII_DIGIT+ }
+Value = @{ ("+" | "-")? ~ NUMBER+ ~ ("." ~ NUMBER+)? ~ ("e" ~ ("+" | "-")? ~ NUMBER+)? }
+Entry = { Dimension ~ Dimension ~ Value }

src/io/matrix_market.rs

@@ -0,0 +1,53 @@
+use std::fs;
+use std::path::Path;
+
+use pest::Parser;
+use sparse::CsMatrix;
+use Real;
+
+#[derive(Parser)]
+#[grammar = "io/matrix_market.pest"]
+struct MatrixMarketParser;
+
+// FIXME: return an Error instead of an Option.
+/// Parses a Matrix Market file at the given path, and returns the corresponding sparse matrix.
+pub fn cs_matrix_from_matrix_market<N: Real, P: AsRef<Path>>(path: P) -> Option<CsMatrix<N>> {
+    let file = fs::read_to_string(path).ok()?;
+    cs_matrix_from_matrix_market_str(&file)
+}
+
+// FIXME: return an Error instead of an Option.
+/// Parses a Matrix Market file described by the given string, and returns the corresponding sparse matrix.
+pub fn cs_matrix_from_matrix_market_str<N: Real>(data: &str) -> Option<CsMatrix<N>> {
+    let file = MatrixMarketParser::parse(Rule::Document, data)
+        .unwrap()
+        .next()?;
+    let mut shape = (0, 0, 0);
+    let mut rows: Vec<usize> = Vec::new();
+    let mut cols: Vec<usize> = Vec::new();
+    let mut data: Vec<N> = Vec::new();
+
+    for line in file.into_inner() {
+        match line.as_rule() {
+            Rule::Header => {}
+            Rule::Shape => {
+                let mut inner = line.into_inner();
+                shape.0 = inner.next()?.as_str().parse::<usize>().ok()?;
+                shape.1 = inner.next()?.as_str().parse::<usize>().ok()?;
+                shape.2 = inner.next()?.as_str().parse::<usize>().ok()?;
+            }
+            Rule::Entry => {
+                let mut inner = line.into_inner();
+                // NOTE: indices are 1-based.
+                rows.push(inner.next()?.as_str().parse::<usize>().ok()? - 1);
+                cols.push(inner.next()?.as_str().parse::<usize>().ok()? - 1);
+                data.push(::convert(inner.next()?.as_str().parse::<f64>().ok()?));
+            }
+            _ => return None, // FIXME: return an Err instead.
+        }
+    }
+
+    Some(CsMatrix::from_triplet(
+        shape.0, shape.1, &rows, &cols, &data,
+    ))
+}

src/io/mod.rs

@@ -0,0 +1,5 @@
+//! Parsers for various matrix formats.
+
+pub use self::matrix_market::{cs_matrix_from_matrix_market, cs_matrix_from_matrix_market_str};
+
+mod matrix_market;

src/lib.rs

@@ -81,10 +81,12 @@ an optimized set of tools for computer graphics and physics. Those features incl
 #![deny(non_upper_case_globals)]
 #![deny(unused_qualifications)]
 #![deny(unused_results)]
-#![deny(missing_docs)]
+#![warn(missing_docs)] // FIXME: deny this
 #![warn(incoherent_fundamental_impls)]
-#![doc(html_favicon_url = "http://nalgebra.org/img/favicon.ico",
+#![doc(
-       html_root_url = "http://nalgebra.org/rustdoc")]
+    html_favicon_url = "http://nalgebra.org/img/favicon.ico",
+    html_root_url = "http://nalgebra.org/rustdoc"
+)]
 #![cfg_attr(not(feature = "std"), no_std)]
 #![cfg_attr(all(feature = "alloc", not(feature = "std")), feature(alloc))]
 
@@ -121,11 +123,21 @@ extern crate alloc;
 #[cfg(not(feature = "std"))]
 extern crate core as std;
 
+#[cfg(feature = "io")]
+extern crate pest;
+#[macro_use]
+#[cfg(feature = "io")]
+extern crate pest_derive;
+
 pub mod base;
 #[cfg(feature = "debug")]
 pub mod debug;
 pub mod geometry;
+#[cfg(feature = "io")]
+pub mod io;
 pub mod linalg;
+#[cfg(feature = "sparse")]
+pub mod sparse;
 
 #[cfg(feature = "std")]
 #[deprecated(
@@ -135,11 +147,13 @@ pub use base as core;
 pub use base::*;
 pub use geometry::*;
 pub use linalg::*;
+#[cfg(feature = "sparse")]
+pub use sparse::*;
 
 use std::cmp::{self, Ordering, PartialOrd};
 
 use alga::general::{
-    Additive, AdditiveGroup, Identity, Inverse, JoinSemilattice, Lattice, MeetSemilattice,
+    Additive, AdditiveGroup, Identity, TwoSidedInverse, JoinSemilattice, Lattice, MeetSemilattice,
     Multiplicative, SupersetOf,
 };
 use alga::linear::SquareMatrix as AlgaSquareMatrix;
@@ -413,8 +427,8 @@ pub fn try_inverse<M: AlgaSquareMatrix>(m: &M) -> Option<M> {
 ///
 /// * [`try_inverse`](fn.try_inverse.html)
 #[inline]
-pub fn inverse<M: Inverse<Multiplicative>>(m: &M) -> M {
+pub fn inverse<M: TwoSidedInverse<Multiplicative>>(m: &M) -> M {
-    m.inverse()
+    m.two_sided_inverse()
 }
 
 /*

src/sparse/cs_matrix.rs

@@ -0,0 +1,517 @@
+use alga::general::ClosedAdd;
+use num::Zero;
+use std::iter;
+use std::marker::PhantomData;
+use std::ops::Range;
+use std::slice;
+
+use allocator::Allocator;
+use sparse::cs_utils;
+use {
+    DefaultAllocator, Dim, Dynamic, Scalar, Vector, VectorN, U1
+};
+
+pub struct ColumnEntries<'a, N> {
+    curr: usize,
+    i: &'a [usize],
+    v: &'a [N],
+}
+
+impl<'a, N> ColumnEntries<'a, N> {
+    #[inline]
+    pub fn new(i: &'a [usize], v: &'a [N]) -> Self {
+        assert_eq!(i.len(), v.len());
+        ColumnEntries { curr: 0, i, v }
+    }
+}
+
+impl<'a, N: Copy> Iterator for ColumnEntries<'a, N> {
+    type Item = (usize, N);
+
+    #[inline]
+    fn next(&mut self) -> Option<(usize, N)> {
+        if self.curr >= self.i.len() {
+            None
+        } else {
+            let res = Some((unsafe { *self.i.get_unchecked(self.curr) }, unsafe {
+                *self.v.get_unchecked(self.curr)
+            }));
+            self.curr += 1;
+            res
+        }
+    }
+}
+
+// FIXME: this structure exists for now only because impl trait
+// cannot be used for trait method return types.
+/// Trait for iterable compressed-column matrix storage.
+pub trait CsStorageIter<'a, N, R, C = U1> {
+    /// Iterator through all the rows of a specific columns.
+    ///
+    /// The elements are given as a tuple (row_index, value).
+    type ColumnEntries: Iterator<Item = (usize, N)>;
+    /// Iterator through the row indices of a specific column.
+    type ColumnRowIndices: Iterator<Item = usize>;
+
+    /// Iterates through all the row indices of the j-th column.
+    fn column_row_indices(&'a self, j: usize) -> Self::ColumnRowIndices;
+    #[inline(always)]
+    /// Iterates through all the entries of the j-th column.
+    fn column_entries(&'a self, j: usize) -> Self::ColumnEntries;
+}
+
+/// Trait for mutably iterable compressed-column sparse matrix storage.
+pub trait CsStorageIterMut<'a, N: 'a, R, C = U1> {
+    /// Mutable iterator through all the values of the sparse matrix.
+    type ValuesMut: Iterator<Item = &'a mut N>;
+    /// Mutable iterator through all the rows of a specific columns.
+    ///
+    /// The elements are given as a tuple (row_index, value).
+    type ColumnEntriesMut: Iterator<Item = (usize, &'a mut N)>;
+
+    /// A mutable iterator through the values buffer of the sparse matrix.
+    fn values_mut(&'a mut self) -> Self::ValuesMut;
+    /// Iterates mutably through all the entries of the j-th column.
+    fn column_entries_mut(&'a mut self, j: usize) -> Self::ColumnEntriesMut;
+}
+
+/// Trait for compressed column sparse matrix storage.
+pub trait CsStorage<N, R, C = U1>: for<'a> CsStorageIter<'a, N, R, C> {
+    /// The shape of the stored matrix.
+    fn shape(&self) -> (R, C);
+    /// Retrieve the i-th row index of the underlying row index buffer.
+    ///
+    /// No bound-checking is performed.
+    unsafe fn row_index_unchecked(&self, i: usize) -> usize;
+    /// The i-th value on the contiguous value buffer of this storage.
+    ///
+    /// No bound-checking is performed.
+    unsafe fn get_value_unchecked(&self, i: usize) -> &N;
+    /// The i-th value on the contiguous value buffer of this storage.
+    fn get_value(&self, i: usize) -> &N;
+    /// Retrieve the i-th row index of the underlying row index buffer.
+    fn row_index(&self, i: usize) -> usize;
+    /// The value indices for the `i`-th column.
+    fn column_range(&self, i: usize) -> Range<usize>;
+    /// The size of the value buffer (i.e. the entries known as possibly being non-zero).
+    fn len(&self) -> usize;
+}
+
+/// Trait for compressed column sparse matrix mutable storage.
+pub trait CsStorageMut<N, R, C = U1>:
+    CsStorage<N, R, C> + for<'a> CsStorageIterMut<'a, N, R, C>
+{
+}
+
+/// A storage of column-compressed sparse matrix based on a Vec.
+#[derive(Clone, Debug, PartialEq)]
+pub struct CsVecStorage<N: Scalar, R: Dim, C: Dim>
+where DefaultAllocator: Allocator<usize, C>
+{
+    pub(crate) shape: (R, C),
+    pub(crate) p: VectorN<usize, C>,
+    pub(crate) i: Vec<usize>,
+    pub(crate) vals: Vec<N>,
+}
+
+impl<N: Scalar, R: Dim, C: Dim> CsVecStorage<N, R, C>
+where DefaultAllocator: Allocator<usize, C>
+{
+    /// The value buffer of this storage.
+    pub fn values(&self) -> &[N] {
+        &self.vals
+    }
+
+    /// The column shifts buffer.
+    pub fn p(&self) -> &[usize] {
+        self.p.as_slice()
+    }
+
+    /// The row index buffers.
+    pub fn i(&self) -> &[usize] {
+        &self.i
+    }
+}
+
+impl<N: Scalar, R: Dim, C: Dim> CsVecStorage<N, R, C> where DefaultAllocator: Allocator<usize, C> {}
+
+impl<'a, N: Scalar, R: Dim, C: Dim> CsStorageIter<'a, N, R, C> for CsVecStorage<N, R, C>
+where DefaultAllocator: Allocator<usize, C>
+{
+    type ColumnEntries = ColumnEntries<'a, N>;
+    type ColumnRowIndices = iter::Cloned<slice::Iter<'a, usize>>;
+
+    #[inline]
+    fn column_entries(&'a self, j: usize) -> Self::ColumnEntries {
+        let rng = self.column_range(j);
+        ColumnEntries::new(&self.i[rng.clone()], &self.vals[rng])
+    }
+
+    #[inline]
+    fn column_row_indices(&'a self, j: usize) -> Self::ColumnRowIndices {
+        let rng = self.column_range(j);
+        self.i[rng.clone()].iter().cloned()
+    }
+}
+
+impl<N: Scalar, R: Dim, C: Dim> CsStorage<N, R, C> for CsVecStorage<N, R, C>
+where DefaultAllocator: Allocator<usize, C>
+{
+    #[inline]
+    fn shape(&self) -> (R, C) {
+        self.shape
+    }
+
+    #[inline]
+    fn len(&self) -> usize {
+        self.vals.len()
+    }
+
+    #[inline]
+    fn row_index(&self, i: usize) -> usize {
+        self.i[i]
+    }
+
+    #[inline]
+    unsafe fn row_index_unchecked(&self, i: usize) -> usize {
+        *self.i.get_unchecked(i)
+    }
+
+    #[inline]
+    unsafe fn get_value_unchecked(&self, i: usize) -> &N {
+        self.vals.get_unchecked(i)
+    }
+
+    #[inline]
+    fn get_value(&self, i: usize) -> &N {
+        &self.vals[i]
+    }
+
+    #[inline]
+    fn column_range(&self, j: usize) -> Range<usize> {
+        let end = if j + 1 == self.p.len() {
+            self.len()
+        } else {
+            self.p[j + 1]
+        };
+
+        self.p[j]..end
+    }
+}
+
+impl<'a, N: Scalar, R: Dim, C: Dim> CsStorageIterMut<'a, N, R, C> for CsVecStorage<N, R, C>
+where DefaultAllocator: Allocator<usize, C>
+{
+    type ValuesMut = slice::IterMut<'a, N>;
+    type ColumnEntriesMut = iter::Zip<iter::Cloned<slice::Iter<'a, usize>>, slice::IterMut<'a, N>>;
+
+    #[inline]
+    fn values_mut(&'a mut self) -> Self::ValuesMut {
+        self.vals.iter_mut()
+    }
+
+    #[inline]
+    fn column_entries_mut(&'a mut self, j: usize) -> Self::ColumnEntriesMut {
+        let rng = self.column_range(j);
+        self.i[rng.clone()]
+            .iter()
+            .cloned()
+            .zip(self.vals[rng].iter_mut())
+    }
+}
+
+impl<N: Scalar, R: Dim, C: Dim> CsStorageMut<N, R, C> for CsVecStorage<N, R, C> where DefaultAllocator: Allocator<usize, C>
+{}
+
+/*
+pub struct CsSliceStorage<'a, N: Scalar, R: Dim, C: DimAdd<U1>> {
+    shape: (R, C),
+    p: VectorSlice<usize, DimSum<C, U1>>,
+    i: VectorSlice<usize, Dynamic>,
+    vals: VectorSlice<N, Dynamic>,
+}*/
+
+/// A compressed sparse column matrix.
+#[derive(Clone, Debug, PartialEq)]
+pub struct CsMatrix<
+    N: Scalar,
+    R: Dim = Dynamic,
+    C: Dim = Dynamic,
+    S: CsStorage<N, R, C> = CsVecStorage<N, R, C>,
+> {
+    pub(crate) data: S,
+    _phantoms: PhantomData<(N, R, C)>,
+}
+
+/// A column compressed sparse vector.
+pub type CsVector<N, R = Dynamic, S = CsVecStorage<N, R, U1>> = CsMatrix<N, R, U1, S>;
+
+impl<N: Scalar, R: Dim, C: Dim> CsMatrix<N, R, C>
+where DefaultAllocator: Allocator<usize, C>
+{
+    /// Creates a new compressed sparse column matrix with the specified dimension and
+    /// `nvals` possible non-zero values.
+    pub fn new_uninitialized_generic(nrows: R, ncols: C, nvals: usize) -> Self {
+        let mut i = Vec::with_capacity(nvals);
+        unsafe {
+            i.set_len(nvals);
+        }
+        i.shrink_to_fit();
+
+        let mut vals = Vec::with_capacity(nvals);
+        unsafe {
+            vals.set_len(nvals);
+        }
+        vals.shrink_to_fit();
+
+        CsMatrix {
+            data: CsVecStorage {
+                shape: (nrows, ncols),
+                p: VectorN::zeros_generic(ncols, U1),
+                i,
+                vals,
+            },
+            _phantoms: PhantomData,
+        }
+    }
+
+    /*
+    pub(crate) fn from_parts_generic(
+        nrows: R,
+        ncols: C,
+        p: VectorN<usize, C>,
+        i: Vec<usize>,
+        vals: Vec<N>,
+    ) -> Self
+    where
+        N: Zero + ClosedAdd,
+        DefaultAllocator: Allocator<N, R>,
+    {
+        assert_eq!(ncols.value(), p.len(), "Invalid inptr size.");
+        assert_eq!(i.len(), vals.len(), "Invalid value size.");
+
+        // Check p.
+        for ptr in &p {
+            assert!(*ptr < i.len(), "Invalid inptr value.");
+        }
+
+        for ptr in p.as_slice().windows(2) {
+            assert!(ptr[0] <= ptr[1], "Invalid inptr ordering.");
+        }
+
+        // Check i.
+        for i in &i {
+            assert!(*i < nrows.value(), "Invalid row ptr value.")
+        }
+
+        let mut res = CsMatrix {
+            data: CsVecStorage {
+                shape: (nrows, ncols),
+                p,
+                i,
+                vals,
+            },
+            _phantoms: PhantomData,
+        };
+
+        // Sort and remove duplicates.
+        res.sort();
+        res.dedup();
+
+        res
+    }*/
+}
+
+/*
+impl<N: Scalar + Zero + ClosedAdd> CsMatrix<N> {
+    pub(crate) fn from_parts(
+        nrows: usize,
+        ncols: usize,
+        p: Vec<usize>,
+        i: Vec<usize>,
+        vals: Vec<N>,
+    ) -> Self
+    {
+        let nrows = Dynamic::new(nrows);
+        let ncols = Dynamic::new(ncols);
+        let p = DVector::from_data(VecStorage::new(ncols, U1, p));
+        Self::from_parts_generic(nrows, ncols, p, i, vals)
+    }
+}
+*/
+
+impl<N: Scalar, R: Dim, C: Dim, S: CsStorage<N, R, C>> CsMatrix<N, R, C, S> {
+    pub(crate) fn from_data(data: S) -> Self {
+        CsMatrix {
+            data,
+            _phantoms: PhantomData,
+        }
+    }
+
+    /// The size of the data buffer.
+    pub fn len(&self) -> usize {
+        self.data.len()
+    }
+
+    /// The number of rows of this matrix.
+    pub fn nrows(&self) -> usize {
+        self.data.shape().0.value()
+    }
+
+    /// The number of rows of this matrix.
+    pub fn ncols(&self) -> usize {
+        self.data.shape().1.value()
+    }
+
+    /// The shape of this matrix.
+    pub fn shape(&self) -> (usize, usize) {
+        let (nrows, ncols) = self.data.shape();
+        (nrows.value(), ncols.value())
+    }
+
+    /// Whether this matrix is square or not.
+    pub fn is_square(&self) -> bool {
+        let (nrows, ncols) = self.data.shape();
+        nrows.value() == ncols.value()
+    }
+
+    /// Should always return `true`.
+    ///
+    /// This method is generally used for debugging and should typically not be called in user code.
+    /// This checks that the row inner indices of this matrix are sorted. It takes `O(n)` time,
+    /// where n` is `self.len()`.
+    /// All operations of CSC matrices on nalgebra assume, and will return, sorted indices.
+    /// If at any time this `is_sorted` method returns `false`, then, something went wrong
+    /// and an issue should be open on the nalgebra repository with details on how to reproduce
+    /// this.
+    pub fn is_sorted(&self) -> bool {
+        for j in 0..self.ncols() {
+            let mut curr = None;
+            for idx in self.data.column_row_indices(j) {
+                if let Some(curr) = curr {
+                    if idx <= curr {
+                        return false;
+                    }
+                }
+
+                curr = Some(idx);
+            }
+        }
+
+        true
+    }
+
+    /// Computes the transpose of this sparse matrix.
+    pub fn transpose(&self) -> CsMatrix<N, C, R>
+    where DefaultAllocator: Allocator<usize, R> {
+        let (nrows, ncols) = self.data.shape();
+
+        let nvals = self.len();
+        let mut res = CsMatrix::new_uninitialized_generic(ncols, nrows, nvals);
+        let mut workspace = Vector::zeros_generic(nrows, U1);
+
+        // Compute p.
+        for i in 0..nvals {
+            let row_id = self.data.row_index(i);
+            workspace[row_id] += 1;
+        }
+
+        let _ = cs_utils::cumsum(&mut workspace, &mut res.data.p);
+
+        // Fill the result.
+        for j in 0..ncols.value() {
+            for (row_id, value) in self.data.column_entries(j) {
+                let shift = workspace[row_id];
+
+                res.data.vals[shift] = value;
+                res.data.i[shift] = j;
+                workspace[row_id] += 1;
+            }
+        }
+
+        res
+    }
+}
+
+impl<N: Scalar, R: Dim, C: Dim, S: CsStorageMut<N, R, C>> CsMatrix<N, R, C, S> {
+    /// Iterator through all the mutable values of this sparse matrix.
+    #[inline]
+    pub fn values_mut(&mut self) -> impl Iterator<Item = &mut N> {
+        self.data.values_mut()
+    }
+}
+
+impl<N: Scalar, R: Dim, C: Dim> CsMatrix<N, R, C>
+where DefaultAllocator: Allocator<usize, C>
+{
+    pub(crate) fn sort(&mut self)
+    where DefaultAllocator: Allocator<N, R> {
+        // Size = R
+        let nrows = self.data.shape().0;
+        let mut workspace = unsafe { VectorN::new_uninitialized_generic(nrows, U1) };
+        self.sort_with_workspace(workspace.as_mut_slice());
+    }
+
+    pub(crate) fn sort_with_workspace(&mut self, workspace: &mut [N]) {
+        assert!(
+            workspace.len() >= self.nrows(),
+            "Workspace must be able to hold at least self.nrows() elements."
+        );
+
+        for j in 0..self.ncols() {
+            // Scatter the row in the workspace.
+            for (irow, val) in self.data.column_entries(j) {
+                workspace[irow] = val;
+            }
+
+            // Sort the index vector.
+            let range = self.data.column_range(j);
+            self.data.i[range.clone()].sort();
+
+            // Permute the values too.
+            for (i, irow) in range.clone().zip(self.data.i[range].iter().cloned()) {
+                self.data.vals[i] = workspace[irow];
+            }
+        }
+    }
+
+    // Remove dupliate entries on a sorted CsMatrix.
+    pub(crate) fn dedup(&mut self)
+    where N: Zero + ClosedAdd {
+        let mut curr_i = 0;
+
+        for j in 0..self.ncols() {
+            let range = self.data.column_range(j);
+            self.data.p[j] = curr_i;
+
+            if range.start != range.end {
+                let mut value = N::zero();
+                let mut irow = self.data.i[range.start];
+
+                for idx in range {
+                    let curr_irow = self.data.i[idx];
+
+                    if curr_irow == irow {
+                        value += self.data.vals[idx];
+                    } else {
+                        self.data.i[curr_i] = irow;
+                        self.data.vals[curr_i] = value;
+                        value = self.data.vals[idx];
+                        irow = curr_irow;
+                        curr_i += 1;
+                    }
+                }
+
+                // Handle the last entry.
+                self.data.i[curr_i] = irow;
+                self.data.vals[curr_i] = value;
+                curr_i += 1;
+            }
+        }
+
+        self.data.i.truncate(curr_i);
+        self.data.i.shrink_to_fit();
+        self.data.vals.truncate(curr_i);
+        self.data.vals.shrink_to_fit();
+    }
+}

src/sparse/cs_matrix_cholesky.rs

@@ -0,0 +1,408 @@
+use std::iter;
+use std::mem;
+
+use allocator::Allocator;
+use sparse::{CsMatrix, CsStorage, CsStorageIter, CsStorageIterMut, CsVecStorage};
+use {DefaultAllocator, Dim, Real, VectorN, U1};
+
+/// The cholesky decomposition of a column compressed sparse matrix.
+pub struct CsCholesky<N: Real, D: Dim>
+where DefaultAllocator: Allocator<usize, D> + Allocator<N, D>
+{
+    // Non-zero pattern of the original matrix upper-triangular part.
+    // Unlike the original matrix, the `original_p` array does contain the last sentinel value
+    // equal to `original_i.len()` at the end.
+    original_p: Vec<usize>,
+    original_i: Vec<usize>,
+    // Decomposition result.
+    l: CsMatrix<N, D, D>,
+    // Used only for the pattern.
+    // FIXME: store only the nonzero pattern instead.
+    u: CsMatrix<N, D, D>,
+    ok: bool,
+    // Workspaces.
+    work_x: VectorN<N, D>,
+    work_c: VectorN<usize, D>,
+}
+
+impl<N: Real, D: Dim> CsCholesky<N, D>
+where DefaultAllocator: Allocator<usize, D> + Allocator<N, D>
+{
+    /// Computes the cholesky decomposition of the sparse matrix `m`.
+    pub fn new(m: &CsMatrix<N, D, D>) -> Self {
+        let mut me = Self::new_symbolic(m);
+        let _ = me.decompose_left_looking(&m.data.vals);
+        me
+    }
+    /// Perform symbolic analysis for the given matrix.
+    ///
+    /// This does not access the numerical values of `m`.
+    pub fn new_symbolic(m: &CsMatrix<N, D, D>) -> Self {
+        assert!(
+            m.is_square(),
+            "The matrix `m` must be square to compute its elimination tree."
+        );
+
+        let (l, u) = Self::nonzero_pattern(m);
+
+        // Workspaces.
+        let work_x = unsafe { VectorN::new_uninitialized_generic(m.data.shape().0, U1) };
+        let work_c = unsafe { VectorN::new_uninitialized_generic(m.data.shape().1, U1) };
+        let mut original_p = m.data.p.as_slice().to_vec();
+        original_p.push(m.data.i.len());
+
+        CsCholesky {
+            original_p,
+            original_i: m.data.i.clone(),
+            l,
+            u,
+            ok: false,
+            work_x,
+            work_c,
+        }
+    }
+
+    /// The lower-triangular matrix of the cholesky decomposition.
+    pub fn l(&self) -> Option<&CsMatrix<N, D, D>> {
+        if self.ok {
+            Some(&self.l)
+        } else {
+            None
+        }
+    }
+
+    /// Extracts the lower-triangular matrix of the cholesky decomposition.
+    pub fn unwrap_l(self) -> Option<CsMatrix<N, D, D>> {
+        if self.ok {
+            Some(self.l)
+        } else {
+            None
+        }
+    }
+
+    /// Perform a numerical left-looking cholesky decomposition of a matrix with the same structure as the
+    /// one used to initialize `self`, but with different non-zero values provided by `values`.
+    pub fn decompose_left_looking(&mut self, values: &[N]) -> bool {
+        assert!(
+            values.len() >= self.original_i.len(),
+            "The set of values is too small."
+        );
+
+        let n = self.l.nrows();
+
+        // Reset `work_c` to the column pointers of `l`.
+        self.work_c.copy_from(&self.l.data.p);
+
+        unsafe {
+            for k in 0..n {
+                // Scatter the k-th column of the original matrix with the values provided.
+                let range_k =
+                    *self.original_p.get_unchecked(k)..*self.original_p.get_unchecked(k + 1);
+
+                *self.work_x.vget_unchecked_mut(k) = N::zero();
+                for p in range_k.clone() {
+                    let irow = *self.original_i.get_unchecked(p);
+
+                    if irow >= k {
+                        *self.work_x.vget_unchecked_mut(irow) = *values.get_unchecked(p);
+                    }
+                }
+
+                for j in self.u.data.column_row_indices(k) {
+                    let factor = -*self
+                        .l
+                        .data
+                        .vals
+                        .get_unchecked(*self.work_c.vget_unchecked(j));
+                    *self.work_c.vget_unchecked_mut(j) += 1;
+
+                    if j < k {
+                        for (z, val) in self.l.data.column_entries(j) {
+                            if z >= k {
+                                *self.work_x.vget_unchecked_mut(z) += val * factor;
+                            }
+                        }
+                    }
+                }
+
+                let diag = *self.work_x.vget_unchecked(k);
+
+                if diag > N::zero() {
+                    let denom = diag.sqrt();
+                    *self
+                        .l
+                        .data
+                        .vals
+                        .get_unchecked_mut(*self.l.data.p.vget_unchecked(k)) = denom;
+
+                    for (p, val) in self.l.data.column_entries_mut(k) {
+                        *val = *self.work_x.vget_unchecked(p) / denom;
+                        *self.work_x.vget_unchecked_mut(p) = N::zero();
+                    }
+                } else {
+                    self.ok = false;
+                    return false;
+                }
+            }
+        }
+
+        self.ok = true;
+        true
+    }
+
+    /// Perform a numerical up-looking cholesky decomposition of a matrix with the same structure as the
+    /// one used to initialize `self`, but with different non-zero values provided by `values`.
+    pub fn decompose_up_looking(&mut self, values: &[N]) -> bool {
+        assert!(
+            values.len() >= self.original_i.len(),
+            "The set of values is too small."
+        );
+
+        // Reset `work_c` to the column pointers of `l`.
+        self.work_c.copy_from(&self.l.data.p);
+
+        // Perform the decomposition.
+        for k in 0..self.l.nrows() {
+            unsafe {
+                // Scatter the k-th column of the original matrix with the values provided.
+                let column_range =
+                    *self.original_p.get_unchecked(k)..*self.original_p.get_unchecked(k + 1);
+
+                *self.work_x.vget_unchecked_mut(k) = N::zero();
+                for p in column_range.clone() {
+                    let irow = *self.original_i.get_unchecked(p);
+
+                    if irow <= k {
+                        *self.work_x.vget_unchecked_mut(irow) = *values.get_unchecked(p);
+                    }
+                }
+
+                let mut diag = *self.work_x.vget_unchecked(k);
+                *self.work_x.vget_unchecked_mut(k) = N::zero();
+
+                // Triangular solve.
+                for irow in self.u.data.column_row_indices(k) {
+                    if irow >= k {
+                        continue;
+                    }
+
+                    let lki = *self.work_x.vget_unchecked(irow)
+                        / *self
+                            .l
+                            .data
+                            .vals
+                            .get_unchecked(*self.l.data.p.vget_unchecked(irow));
+                    *self.work_x.vget_unchecked_mut(irow) = N::zero();
+
+                    for p in
+                        *self.l.data.p.vget_unchecked(irow) + 1..*self.work_c.vget_unchecked(irow)
+                    {
+                        *self
+                            .work_x
+                            .vget_unchecked_mut(*self.l.data.i.get_unchecked(p)) -=
+                            *self.l.data.vals.get_unchecked(p) * lki;
+                    }
+
+                    diag -= lki * lki;
+                    let p = *self.work_c.vget_unchecked(irow);
+                    *self.work_c.vget_unchecked_mut(irow) += 1;
+                    *self.l.data.i.get_unchecked_mut(p) = k;
+                    *self.l.data.vals.get_unchecked_mut(p) = lki;
+                }
+
+                if diag <= N::zero() {
+                    self.ok = false;
+                    return false;
+                }
+
+                // Deal with the diagonal element.
+                let p = *self.work_c.vget_unchecked(k);
+                *self.work_c.vget_unchecked_mut(k) += 1;
+                *self.l.data.i.get_unchecked_mut(p) = k;
+                *self.l.data.vals.get_unchecked_mut(p) = diag.sqrt();
+            }
+        }
+
+        self.ok = true;
+        true
+    }
+
+    fn elimination_tree<S: CsStorage<N, D, D>>(m: &CsMatrix<N, D, D, S>) -> Vec<usize> {
+        let nrows = m.nrows();
+        let mut forest: Vec<_> = iter::repeat(usize::max_value()).take(nrows).collect();
+        let mut ancestor: Vec<_> = iter::repeat(usize::max_value()).take(nrows).collect();
+
+        for k in 0..nrows {
+            for irow in m.data.column_row_indices(k) {
+                let mut i = irow;
+
+                while i < k {
+                    let i_ancestor = ancestor[i];
+                    ancestor[i] = k;
+
+                    if i_ancestor == usize::max_value() {
+                        forest[i] = k;
+                        break;
+                    }
+
+                    i = i_ancestor;
+                }
+            }
+        }
+
+        forest
+    }
+
+    fn reach<S: CsStorage<N, D, D>>(
+        m: &CsMatrix<N, D, D, S>,
+        j: usize,
+        max_j: usize,
+        tree: &[usize],
+        marks: &mut Vec<bool>,
+        out: &mut Vec<usize>,
+    )
+    {
+        marks.clear();
+        marks.resize(tree.len(), false);
+
+        // FIXME: avoid all those allocations.
+        let mut tmp = Vec::new();
+        let mut res = Vec::new();
+
+        for irow in m.data.column_row_indices(j) {
+            let mut curr = irow;
+            while curr != usize::max_value() && curr <= max_j && !marks[curr] {
+                marks[curr] = true;
+                tmp.push(curr);
+                curr = tree[curr];
+            }
+
+            tmp.append(&mut res);
+            mem::swap(&mut tmp, &mut res);
+        }
+
+        out.append(&mut res);
+    }
+
+    fn nonzero_pattern<S: CsStorage<N, D, D>>(
+        m: &CsMatrix<N, D, D, S>,
+    ) -> (CsMatrix<N, D, D>, CsMatrix<N, D, D>) {
+        let etree = Self::elimination_tree(m);
+        let (nrows, ncols) = m.data.shape();
+        let mut rows = Vec::with_capacity(m.len());
+        let mut cols = unsafe { VectorN::new_uninitialized_generic(m.data.shape().0, U1) };
+        let mut marks = Vec::new();
+
+        // NOTE: the following will actually compute the non-zero pattern of
+        // the transpose of l.
+        for i in 0..nrows.value() {
+            cols[i] = rows.len();
+            Self::reach(m, i, i, &etree, &mut marks, &mut rows);
+        }
+
+        let mut vals = Vec::with_capacity(rows.len());
+        unsafe {
+            vals.set_len(rows.len());
+        }
+        vals.shrink_to_fit();
+
+        let data = CsVecStorage {
+            shape: (nrows, ncols),
+            p: cols,
+            i: rows,
+            vals,
+        };
+
+        let u = CsMatrix::from_data(data);
+        // XXX: avoid this transpose.
+        let l = u.transpose();
+
+        (l, u)
+    }
+
+    /*
+     *
+     * NOTE: All the following methods are untested and currently unused.
+     *
+     *
+    fn column_counts<S: CsStorage<N, D, D>>(
+        m: &CsMatrix<N, D, D, S>,
+        tree: &[usize],
+    ) -> Vec<usize> {
+        let len = m.data.shape().0.value();
+        let mut counts: Vec<_> = iter::repeat(0).take(len).collect();
+        let mut reach = Vec::new();
+        let mut marks = Vec::new();
+    
+        for i in 0..len {
+            Self::reach(m, i, i, tree, &mut marks, &mut reach);
+    
+            for j in reach.drain(..) {
+                counts[j] += 1;
+            }
+        }
+    
+        counts
+    }
+    
+    fn tree_postorder(tree: &[usize]) -> Vec<usize> {
+        // FIXME: avoid all those allocations?
+        let mut first_child: Vec<_> = iter::repeat(usize::max_value()).take(tree.len()).collect();
+        let mut other_children: Vec<_> =
+            iter::repeat(usize::max_value()).take(tree.len()).collect();
+    
+        // Build the children list from the parent list.
+        // The set of children of the node `i` is given by the linked list
+        // starting at `first_child[i]`. The nodes of this list are then:
+        // { first_child[i], other_children[first_child[i]], other_children[other_children[first_child[i]], ... }
+        for (i, parent) in tree.iter().enumerate() {
+            if *parent != usize::max_value() {
+                let brother = first_child[*parent];
+                first_child[*parent] = i;
+                other_children[i] = brother;
+            }
+        }
+    
+        let mut stack = Vec::with_capacity(tree.len());
+        let mut postorder = Vec::with_capacity(tree.len());
+    
+        for (i, node) in tree.iter().enumerate() {
+            if *node == usize::max_value() {
+                Self::dfs(
+                    i,
+                    &mut first_child,
+                    &other_children,
+                    &mut stack,
+                    &mut postorder,
+                )
+            }
+        }
+    
+        postorder
+    }
+    
+    fn dfs(
+        i: usize,
+        first_child: &mut [usize],
+        other_children: &[usize],
+        stack: &mut Vec<usize>,
+        result: &mut Vec<usize>,
+    ) {
+        stack.clear();
+        stack.push(i);
+    
+        while let Some(n) = stack.pop() {
+            let child = first_child[n];
+    
+            if child == usize::max_value() {
+                // No children left.
+                result.push(n);
+            } else {
+                stack.push(n);
+                stack.push(child);
+                first_child[n] = other_children[child];
+            }
+        }
+    }
+    */
+}

src/sparse/cs_matrix_conversion.rs

@@ -0,0 +1,114 @@
+use alga::general::ClosedAdd;
+use num::Zero;
+
+use allocator::Allocator;
+use sparse::cs_utils;
+use sparse::{CsMatrix, CsStorage};
+use storage::Storage;
+use {DefaultAllocator, Dim, Dynamic, Matrix, MatrixMN, Scalar};
+
+impl<'a, N: Scalar + Zero + ClosedAdd> CsMatrix<N> {
+    /// Creates a column-compressed sparse matrix from a sparse matrix in triplet form.
+    pub fn from_triplet(
+        nrows: usize,
+        ncols: usize,
+        irows: &[usize],
+        icols: &[usize],
+        vals: &[N],
+    ) -> Self
+    {
+        Self::from_triplet_generic(Dynamic::new(nrows), Dynamic::new(ncols), irows, icols, vals)
+    }
+}
+
+impl<'a, N: Scalar + Zero + ClosedAdd, R: Dim, C: Dim> CsMatrix<N, R, C>
+where DefaultAllocator: Allocator<usize, C> + Allocator<N, R>
+{
+    /// Creates a column-compressed sparse matrix from a sparse matrix in triplet form.
+    pub fn from_triplet_generic(
+        nrows: R,
+        ncols: C,
+        irows: &[usize],
+        icols: &[usize],
+        vals: &[N],
+    ) -> Self
+    {
+        assert!(vals.len() == irows.len());
+        assert!(vals.len() == icols.len());
+
+        let mut res = CsMatrix::new_uninitialized_generic(nrows, ncols, vals.len());
+        let mut workspace = res.data.p.clone();
+
+        // Column count.
+        for j in icols.iter().cloned() {
+            workspace[j] += 1;
+        }
+
+        let _ = cs_utils::cumsum(&mut workspace, &mut res.data.p);
+
+        // Fill i and vals.
+        for ((i, j), val) in irows
+            .iter()
+            .cloned()
+            .zip(icols.iter().cloned())
+            .zip(vals.iter().cloned())
+        {
+            let offset = workspace[j];
+            res.data.i[offset] = i;
+            res.data.vals[offset] = val;
+            workspace[j] = offset + 1;
+        }
+
+        // Sort the result.
+        res.sort();
+        res.dedup();
+        res
+    }
+}
+
+impl<'a, N: Scalar + Zero, R: Dim, C: Dim, S> From<CsMatrix<N, R, C, S>> for MatrixMN<N, R, C>
+where
+    S: CsStorage<N, R, C>,
+    DefaultAllocator: Allocator<N, R, C>,
+{
+    fn from(m: CsMatrix<N, R, C, S>) -> Self {
+        let (nrows, ncols) = m.data.shape();
+        let mut res = MatrixMN::zeros_generic(nrows, ncols);
+
+        for j in 0..ncols.value() {
+            for (i, val) in m.data.column_entries(j) {
+                res[(i, j)] = val;
+            }
+        }
+
+        res
+    }
+}
+
+impl<'a, N: Scalar + Zero, R: Dim, C: Dim, S> From<Matrix<N, R, C, S>> for CsMatrix<N, R, C>
+where
+    S: Storage<N, R, C>,
+    DefaultAllocator: Allocator<N, R, C> + Allocator<usize, C>,
+{
+    fn from(m: Matrix<N, R, C, S>) -> Self {
+        let (nrows, ncols) = m.data.shape();
+        let len = m.iter().filter(|e| !e.is_zero()).count();
+        let mut res = CsMatrix::new_uninitialized_generic(nrows, ncols, len);
+        let mut nz = 0;
+
+        for j in 0..ncols.value() {
+            let column = m.column(j);
+            res.data.p[j] = nz;
+
+            for i in 0..nrows.value() {
+                if !column[i].is_zero() {
+                    res.data.i[nz] = i;
+                    res.data.vals[nz] = column[i];
+                    nz += 1;
+                }
+            }
+        }
+
+        res
+    }
+}

src/sparse/cs_matrix_ops.rs

@@ -0,0 +1,304 @@
+use alga::general::{ClosedAdd, ClosedMul};
+use num::{One, Zero};
+use std::ops::{Add, Mul};
+
+use allocator::Allocator;
+use constraint::{AreMultipliable, DimEq, ShapeConstraint};
+use sparse::{CsMatrix, CsStorage, CsStorageMut, CsVector};
+use storage::StorageMut;
+use {DefaultAllocator, Dim, Scalar, Vector, VectorN, U1};
+
+impl<N: Scalar, R: Dim, C: Dim, S: CsStorage<N, R, C>> CsMatrix<N, R, C, S> {
+    fn scatter<R2: Dim, C2: Dim>(
+        &self,
+        j: usize,
+        beta: N,
+        timestamps: &mut [usize],
+        timestamp: usize,
+        workspace: &mut [N],
+        mut nz: usize,
+        res: &mut CsMatrix<N, R2, C2>,
+    ) -> usize
+    where
+        N: ClosedAdd + ClosedMul,
+        DefaultAllocator: Allocator<usize, C2>,
+    {
+        for (i, val) in self.data.column_entries(j) {
+            if timestamps[i] < timestamp {
+                timestamps[i] = timestamp;
+                res.data.i[nz] = i;
+                nz += 1;
+                workspace[i] = val * beta;
+            } else {
+                workspace[i] += val * beta;
+            }
+        }
+
+        nz
+    }
+}
+
+/*
+impl<N: Scalar, R, S> CsVector<N, R, S> {
+    pub fn axpy(&mut self, alpha: N, x: CsVector<N, R, S>, beta: N) {
+        // First, compute the number of non-zero entries.
+        let mut nnzero = 0;
+
+        // Allocate a size large enough.
+        self.data.set_column_len(0, nnzero);
+
+        // Fill with the axpy.
+        let mut i = self.len();
+        let mut j = x.len();
+        let mut k = nnzero - 1;
+        let mut rid1 = self.data.row_index(0, i - 1);
+        let mut rid2 = x.data.row_index(0, j - 1);
+
+        while k > 0 {
+            if rid1 == rid2 {
+                self.data.set_row_index(0, k, rid1);
+                self[k] = alpha * x[j] + beta * self[k];
+                i -= 1;
+                j -= 1;
+            } else if rid1 < rid2 {
+                self.data.set_row_index(0, k, rid1);
+                self[k] = beta * self[i];
+                i -= 1;
+            } else {
+                self.data.set_row_index(0, k, rid2);
+                self[k] = alpha * x[j];
+                j -= 1;
+            }
+
+            k -= 1;
+        }
+    }
+}
+*/
+
+impl<N: Scalar + Zero + ClosedAdd + ClosedMul, D: Dim, S: StorageMut<N, D>> Vector<N, D, S> {
+    /// Perform a sparse axpy operation: `self = alpha * x + beta * self` operation.
+    pub fn axpy_cs<D2: Dim, S2>(&mut self, alpha: N, x: &CsVector<N, D2, S2>, beta: N)
+    where
+        S2: CsStorage<N, D2>,
+        ShapeConstraint: DimEq<D, D2>,
+    {
+        if beta.is_zero() {
+            for i in 0..x.len() {
+                unsafe {
+                    let k = x.data.row_index_unchecked(i);
+                    let y = self.vget_unchecked_mut(k);
+                    *y = alpha * *x.data.get_value_unchecked(i);
+                }
+            }
+        } else {
+            // Needed to be sure even components not present on `x` are multiplied.
+            *self *= beta;
+
+            for i in 0..x.len() {
+                unsafe {
+                    let k = x.data.row_index_unchecked(i);
+                    let y = self.vget_unchecked_mut(k);
+                    *y += alpha * *x.data.get_value_unchecked(i);
+                }
+            }
+        }
+    }
+
+    /*
+    pub fn gemv_sparse<R2: Dim, C2: Dim, S2>(&mut self, alpha: N, a: &CsMatrix<N, R2, C2, S2>, x: &DVector<N>, beta: N)
+        where
+            S2: CsStorage<N, R2, C2> {
+        let col2 = a.column(0);
+        let val = unsafe { *x.vget_unchecked(0) };
+        self.axpy_sparse(alpha * val, &col2, beta);
+    
+        for j in 1..ncols2 {
+            let col2 = a.column(j);
+            let val = unsafe { *x.vget_unchecked(j) };
+    
+            self.axpy_sparse(alpha * val, &col2, N::one());
+        }
+    }
+    */
+}
+
+impl<'a, 'b, N, R1, R2, C1, C2, S1, S2> Mul<&'b CsMatrix<N, R2, C2, S2>>
+    for &'a CsMatrix<N, R1, C1, S1>
+where
+    N: Scalar + ClosedAdd + ClosedMul + Zero,
+    R1: Dim,
+    C1: Dim,
+    R2: Dim,
+    C2: Dim,
+    S1: CsStorage<N, R1, C1>,
+    S2: CsStorage<N, R2, C2>,
+    ShapeConstraint: AreMultipliable<R1, C1, R2, C2>,
+    DefaultAllocator: Allocator<usize, C2> + Allocator<usize, R1> + Allocator<N, R1>,
+{
+    type Output = CsMatrix<N, R1, C2>;
+
+    fn mul(self, rhs: &'b CsMatrix<N, R2, C2, S2>) -> CsMatrix<N, R1, C2> {
+        let (nrows1, ncols1) = self.data.shape();
+        let (nrows2, ncols2) = rhs.data.shape();
+        assert_eq!(
+            ncols1.value(),
+            nrows2.value(),
+            "Mismatched dimensions for matrix multiplication."
+        );
+
+        let mut res = CsMatrix::new_uninitialized_generic(nrows1, ncols2, self.len() + rhs.len());
+        let mut workspace = VectorN::<N, R1>::zeros_generic(nrows1, U1);
+        let mut nz = 0;
+
+        for j in 0..ncols2.value() {
+            res.data.p[j] = nz;
+            let new_size_bound = nz + nrows1.value();
+            res.data.i.resize(new_size_bound, 0);
+            res.data.vals.resize(new_size_bound, N::zero());
+
+            for (i, beta) in rhs.data.column_entries(j) {
+                for (k, val) in self.data.column_entries(i) {
+                    workspace[k] += val * beta;
+                }
+            }
+
+            for (i, val) in workspace.as_mut_slice().iter_mut().enumerate() {
+                if !val.is_zero() {
+                    res.data.i[nz] = i;
+                    res.data.vals[nz] = *val;
+                    *val = N::zero();
+                    nz += 1;
+                }
+            }
+        }
+
+        // NOTE: the following has a lower complexity, but is slower in many cases, likely because
+        // of branching inside of the inner loop.
+        //
+        // let mut res = CsMatrix::new_uninitialized_generic(nrows1, ncols2, self.len() + rhs.len());
+        // let mut timestamps = VectorN::zeros_generic(nrows1, U1);
+        // let mut workspace = unsafe { VectorN::new_uninitialized_generic(nrows1, U1) };
+        // let mut nz = 0;
+        //
+        // for j in 0..ncols2.value() {
+        //     res.data.p[j] = nz;
+        //     let new_size_bound = nz + nrows1.value();
+        //     res.data.i.resize(new_size_bound, 0);
+        //     res.data.vals.resize(new_size_bound, N::zero());
+        //
+        //     for (i, val) in rhs.data.column_entries(j) {
+        //         nz = self.scatter(
+        //             i,
+        //             val,
+        //             timestamps.as_mut_slice(),
+        //             j + 1,
+        //             workspace.as_mut_slice(),
+        //             nz,
+        //             &mut res,
+        //         );
+        //     }
+        //
+        //     // Keep the output sorted.
+        //     let range = res.data.p[j]..nz;
+        //     res.data.i[range.clone()].sort();
+        //
+        //     for p in range {
+        //         res.data.vals[p] = workspace[res.data.i[p]]
+        //     }
+        // }
+
+        res.data.i.truncate(nz);
+        res.data.i.shrink_to_fit();
+        res.data.vals.truncate(nz);
+        res.data.vals.shrink_to_fit();
+        res
+    }
+}
+
+impl<'a, 'b, N, R1, R2, C1, C2, S1, S2> Add<&'b CsMatrix<N, R2, C2, S2>>
+    for &'a CsMatrix<N, R1, C1, S1>
+where
+    N: Scalar + ClosedAdd + ClosedMul + One,
+    R1: Dim,
+    C1: Dim,
+    R2: Dim,
+    C2: Dim,
+    S1: CsStorage<N, R1, C1>,
+    S2: CsStorage<N, R2, C2>,
+    ShapeConstraint: DimEq<R1, R2> + DimEq<C1, C2>,
+    DefaultAllocator: Allocator<usize, C2> + Allocator<usize, R1> + Allocator<N, R1>,
+{
+    type Output = CsMatrix<N, R1, C2>;
+
+    fn add(self, rhs: &'b CsMatrix<N, R2, C2, S2>) -> CsMatrix<N, R1, C2> {
+        let (nrows1, ncols1) = self.data.shape();
+        let (nrows2, ncols2) = rhs.data.shape();
+        assert_eq!(
+            (nrows1.value(), ncols1.value()),
+            (nrows2.value(), ncols2.value()),
+            "Mismatched dimensions for matrix sum."
+        );
+
+        let mut res = CsMatrix::new_uninitialized_generic(nrows1, ncols2, self.len() + rhs.len());
+        let mut timestamps = VectorN::zeros_generic(nrows1, U1);
+        let mut workspace = unsafe { VectorN::new_uninitialized_generic(nrows1, U1) };
+        let mut nz = 0;
+
+        for j in 0..ncols2.value() {
+            res.data.p[j] = nz;
+
+            nz = self.scatter(
+                j,
+                N::one(),
+                timestamps.as_mut_slice(),
+                j + 1,
+                workspace.as_mut_slice(),
+                nz,
+                &mut res,
+            );
+
+            nz = rhs.scatter(
+                j,
+                N::one(),
+                timestamps.as_mut_slice(),
+                j + 1,
+                workspace.as_mut_slice(),
+                nz,
+                &mut res,
+            );
+
+            // Keep the output sorted.
+            let range = res.data.p[j]..nz;
+            res.data.i[range.clone()].sort();
+
+            for p in range {
+                res.data.vals[p] = workspace[res.data.i[p]]
+            }
+        }
+
+        res.data.i.truncate(nz);
+        res.data.i.shrink_to_fit();
+        res.data.vals.truncate(nz);
+        res.data.vals.shrink_to_fit();
+        res
+    }
+}
+
+impl<'a, 'b, N, R, C, S> Mul<N> for CsMatrix<N, R, C, S>
+where
+    N: Scalar + ClosedAdd + ClosedMul + Zero,
+    R: Dim,
+    C: Dim,
+    S: CsStorageMut<N, R, C>,
+{
+    type Output = Self;
+
+    fn mul(mut self, rhs: N) -> Self {
+        for e in self.values_mut() {
+            *e *= rhs
+        }
+
+        self
+    }
+}

src/sparse/cs_matrix_solve.rs

@@ -0,0 +1,283 @@
+use allocator::Allocator;
+use constraint::{SameNumberOfRows, ShapeConstraint};
+use sparse::{CsMatrix, CsStorage, CsVector};
+use storage::{Storage, StorageMut};
+use {DefaultAllocator, Dim, Matrix, MatrixMN, Real, VectorN, U1};
+
+impl<N: Real, D: Dim, S: CsStorage<N, D, D>> CsMatrix<N, D, D, S> {
+    /// Solve a lower-triangular system with a dense right-hand-side.
+    pub fn solve_lower_triangular<R2: Dim, C2: Dim, S2>(
+        &self,
+        b: &Matrix<N, R2, C2, S2>,
+    ) -> Option<MatrixMN<N, R2, C2>>
+    where
+        S2: Storage<N, R2, C2>,
+        DefaultAllocator: Allocator<N, R2, C2>,
+        ShapeConstraint: SameNumberOfRows<D, R2>,
+    {
+        let mut b = b.clone_owned();
+        if self.solve_lower_triangular_mut(&mut b) {
+            Some(b)
+        } else {
+            None
+        }
+    }
+
+    /// Solve a lower-triangular system with `self` transposed and a dense right-hand-side.
+    pub fn tr_solve_lower_triangular<R2: Dim, C2: Dim, S2>(
+        &self,
+        b: &Matrix<N, R2, C2, S2>,
+    ) -> Option<MatrixMN<N, R2, C2>>
+    where
+        S2: Storage<N, R2, C2>,
+        DefaultAllocator: Allocator<N, R2, C2>,
+        ShapeConstraint: SameNumberOfRows<D, R2>,
+    {
+        let mut b = b.clone_owned();
+        if self.tr_solve_lower_triangular_mut(&mut b) {
+            Some(b)
+        } else {
+            None
+        }
+    }
+
+    /// Solve in-place a lower-triangular system with a dense right-hand-side.
+    pub fn solve_lower_triangular_mut<R2: Dim, C2: Dim, S2>(
+        &self,
+        b: &mut Matrix<N, R2, C2, S2>,
+    ) -> bool
+    where
+        S2: StorageMut<N, R2, C2>,
+        ShapeConstraint: SameNumberOfRows<D, R2>,
+    {
+        let (nrows, ncols) = self.data.shape();
+        assert_eq!(nrows.value(), ncols.value(), "The matrix must be square.");
+        assert_eq!(nrows.value(), b.len(), "Mismatched matrix dimensions.");
+
+        for j2 in 0..b.ncols() {
+            let mut b = b.column_mut(j2);
+
+            for j in 0..ncols.value() {
+                let mut column = self.data.column_entries(j);
+                let mut diag_found = false;
+
+                while let Some((i, val)) = column.next() {
+                    if i == j {
+                        if val.is_zero() {
+                            return false;
+                        }
+
+                        b[j] /= val;
+                        diag_found = true;
+                        break;
+                    }
+                }
+
+                if !diag_found {
+                    return false;
+                }
+
+                for (i, val) in column {
+                    b[i] -= b[j] * val;
+                }
+            }
+        }
+
+        true
+    }
+
+    /// Solve a lower-triangular system with `self` transposed and a dense right-hand-side.
+    pub fn tr_solve_lower_triangular_mut<R2: Dim, C2: Dim, S2>(
+        &self,
+        b: &mut Matrix<N, R2, C2, S2>,
+    ) -> bool
+    where
+        S2: StorageMut<N, R2, C2>,
+        ShapeConstraint: SameNumberOfRows<D, R2>,
+    {
+        let (nrows, ncols) = self.data.shape();
+        assert_eq!(nrows.value(), ncols.value(), "The matrix must be square.");
+        assert_eq!(nrows.value(), b.len(), "Mismatched matrix dimensions.");
+
+        for j2 in 0..b.ncols() {
+            let mut b = b.column_mut(j2);
+
+            for j in (0..ncols.value()).rev() {
+                let mut column = self.data.column_entries(j);
+                let mut diag = None;
+
+                while let Some((i, val)) = column.next() {
+                    if i == j {
+                        if val.is_zero() {
+                            return false;
+                        }
+
+                        diag = Some(val);
+                        break;
+                    }
+                }
+
+                if let Some(diag) = diag {
+                    for (i, val) in column {
+                        b[j] -= val * b[i];
+                    }
+
+                    b[j] /= diag;
+                } else {
+                    return false;
+                }
+            }
+        }
+
+        true
+    }
+
+    /// Solve a lower-triangular system with a sparse right-hand-side.
+    pub fn solve_lower_triangular_cs<D2: Dim, S2>(
+        &self,
+        b: &CsVector<N, D2, S2>,
+    ) -> Option<CsVector<N, D2>>
+    where
+        S2: CsStorage<N, D2>,
+        DefaultAllocator: Allocator<bool, D> + Allocator<N, D2> + Allocator<usize, D2>,
+        ShapeConstraint: SameNumberOfRows<D, D2>,
+    {
+        let mut reach = Vec::new();
+        // We don't compute a postordered reach here because it will be sorted after anyway.
+        self.lower_triangular_reach(b, &mut reach);
+        // We sort the reach so the result matrix has sorted indices.
+        reach.sort();
+        let mut workspace = unsafe { VectorN::new_uninitialized_generic(b.data.shape().0, U1) };
+
+        for i in reach.iter().cloned() {
+            workspace[i] = N::zero();
+        }
+
+        for (i, val) in b.data.column_entries(0) {
+            workspace[i] = val;
+        }
+
+        for j in reach.iter().cloned() {
+            let mut column = self.data.column_entries(j);
+            let mut diag_found = false;
+
+            while let Some((i, val)) = column.next() {
+                if i == j {
+                    if val.is_zero() {
+                        break;
+                    }
+
+                    workspace[j] /= val;
+                    diag_found = true;
+                    break;
+                }
+            }
+
+            if !diag_found {
+                return None;
+            }
+
+            for (i, val) in column {
+                workspace[i] -= workspace[j] * val;
+            }
+        }
+
+        // Copy the result into a sparse vector.
+        let mut result = CsVector::new_uninitialized_generic(b.data.shape().0, U1, reach.len());
+
+        for (i, val) in reach.iter().zip(result.data.vals.iter_mut()) {
+            *val = workspace[*i];
+        }
+
+        result.data.i = reach;
+        Some(result)
+    }
+
+    /*
+    // Computes the reachable, post-ordered, nodes from `b`.
+    fn lower_triangular_reach_postordered<D2: Dim, S2>(
+        &self,
+        b: &CsVector<N, D2, S2>,
+        xi: &mut Vec<usize>,
+    ) where
+        S2: CsStorage<N, D2>,
+        DefaultAllocator: Allocator<bool, D>,
+    {
+        let mut visited = VectorN::repeat_generic(self.data.shape().1, U1, false);
+        let mut stack = Vec::new();
+
+        for i in b.data.column_range(0) {
+            let row_index = b.data.row_index(i);
+
+            if !visited[row_index] {
+                let rng = self.data.column_range(row_index);
+                stack.push((row_index, rng));
+                self.lower_triangular_dfs(visited.as_mut_slice(), &mut stack, xi);
+            }
+        }
+    }
+
+    fn lower_triangular_dfs(
+        &self,
+        visited: &mut [bool],
+        stack: &mut Vec<(usize, Range<usize>)>,
+        xi: &mut Vec<usize>,
+    )
+    {
+        'recursion: while let Some((j, rng)) = stack.pop() {
+            visited[j] = true;
+
+            for i in rng.clone() {
+                let row_id = self.data.row_index(i);
+                if row_id > j && !visited[row_id] {
+                    stack.push((j, (i + 1)..rng.end));
+                    stack.push((row_id, self.data.column_range(row_id)));
+                    continue 'recursion;
+                }
+            }
+
+            xi.push(j)
+        }
+    }
+    */
+
+    // Computes the nodes reachable from `b` in an arbitrary order.
+    fn lower_triangular_reach<D2: Dim, S2>(&self, b: &CsVector<N, D2, S2>, xi: &mut Vec<usize>)
+    where
+        S2: CsStorage<N, D2>,
+        DefaultAllocator: Allocator<bool, D>,
+    {
+        let mut visited = VectorN::repeat_generic(self.data.shape().1, U1, false);
+        let mut stack = Vec::new();
+
+        for irow in b.data.column_row_indices(0) {
+            self.lower_triangular_bfs(irow, visited.as_mut_slice(), &mut stack, xi);
+        }
+    }
+
+    fn lower_triangular_bfs(
+        &self,
+        start: usize,
+        visited: &mut [bool],
+        stack: &mut Vec<usize>,
+        xi: &mut Vec<usize>,
+    )
+    {
+        if !visited[start] {
+            stack.clear();
+            stack.push(start);
+            xi.push(start);
+            visited[start] = true;
+
+            while let Some(j) = stack.pop() {
+                for irow in self.data.column_row_indices(j) {
+                    if irow > j && !visited[irow] {
+                        stack.push(irow);
+                        xi.push(irow);
+                        visited[irow] = true;
+                    }
+                }
+            }
+        }
+    }
+}

src/sparse/cs_utils.rs

@@ -0,0 +1,16 @@
+use allocator::Allocator;
+use {DefaultAllocator, Dim, VectorN};
+
+pub fn cumsum<D: Dim>(a: &mut VectorN<usize, D>, b: &mut VectorN<usize, D>) -> usize
+where DefaultAllocator: Allocator<usize, D> {
+    assert!(a.len() == b.len());
+    let mut sum = 0;
+
+    for i in 0..a.len() {
+        b[i] = sum;
+        sum += a[i];
+        a[i] = b[i];
+    }
+
+    sum
+}

src/sparse/mod.rs

@@ -0,0 +1,13 @@
+//! Sparse matrices.
+
+pub use self::cs_matrix::{
+    CsMatrix, CsStorage, CsStorageIter, CsStorageIterMut, CsStorageMut, CsVecStorage, CsVector,
+};
+pub use self::cs_matrix_cholesky::CsCholesky;
+
+mod cs_matrix;
+mod cs_matrix_cholesky;
+mod cs_matrix_conversion;
+mod cs_matrix_ops;
+mod cs_matrix_solve;
+pub(crate) mod cs_utils;

tests/geometry/isometry.rs

@@ -25,27 +25,35 @@ quickcheck!(
         let viewmatrix = Isometry3::look_at_rh(&eye, &target, &up);
 
         let origin = Point3::origin();
-        relative_eq!(viewmatrix * eye, origin, epsilon = 1.0e-7) &&
+        relative_eq!(viewmatrix * eye, origin, epsilon = 1.0e-7)
-        relative_eq!((viewmatrix * (target - eye)).normalize(), -Vector3::z(), epsilon = 1.0e-7)
+            && relative_eq!(
+                (viewmatrix * (target - eye)).normalize(),
+                -Vector3::z(),
+                epsilon = 1.0e-7
+            )
     }
 
     fn observer_frame_3(eye: Point3<f64>, target: Point3<f64>, up: Vector3<f64>) -> bool {
         let observer = Isometry3::face_towards(&eye, &target, &up);
 
         let origin = Point3::origin();
-        relative_eq!(observer * origin, eye, epsilon = 1.0e-7) &&
+        relative_eq!(observer * origin, eye, epsilon = 1.0e-7)
-        relative_eq!(observer * Vector3::z(), (target - eye).normalize(), epsilon = 1.0e-7)
+            && relative_eq!(
+                observer * Vector3::z(),
+                (target - eye).normalize(),
+                epsilon = 1.0e-7
+            )
     }
 
     fn inverse_is_identity(i: Isometry3<f64>, p: Point3<f64>, v: Vector3<f64>) -> bool {
         let ii = i.inverse();
 
-        relative_eq!(i  * ii, Isometry3::identity(), epsilon = 1.0e-7) &&
+        relative_eq!(i * ii, Isometry3::identity(), epsilon = 1.0e-7)
-        relative_eq!(ii *  i, Isometry3::identity(), epsilon = 1.0e-7) &&
+            && relative_eq!(ii * i, Isometry3::identity(), epsilon = 1.0e-7)
-        relative_eq!((i  * ii) * p, p, epsilon = 1.0e-7) &&
+            && relative_eq!((i * ii) * p, p, epsilon = 1.0e-7)
-        relative_eq!((ii * i)  * p, p, epsilon = 1.0e-7) &&
+            && relative_eq!((ii * i) * p, p, epsilon = 1.0e-7)
-        relative_eq!((i  * ii) * v, v, epsilon = 1.0e-7) &&
+            && relative_eq!((i * ii) * v, v, epsilon = 1.0e-7)
-        relative_eq!((ii * i)  * v, v, epsilon = 1.0e-7)
+            && relative_eq!((ii * i) * v, v, epsilon = 1.0e-7)
     }
 
     fn inverse_is_parts_inversion(t: Translation3<f64>, r: UnitQuaternion<f64>) -> bool {
@@ -54,14 +62,29 @@ quickcheck!(
     }
 
     fn multiply_equals_alga_transform(i: Isometry3<f64>, v: Vector3<f64>, p: Point3<f64>) -> bool {
-        i * v == i.transform_vector(&v) &&
+        i * v == i.transform_vector(&v)
-        i * p == i.transform_point(&p)  &&
+            && i * p == i.transform_point(&p)
-        relative_eq!(i.inverse() * v, i.inverse_transform_vector(&v), epsilon = 1.0e-7) &&
+            && relative_eq!(
-        relative_eq!(i.inverse() * p, i.inverse_transform_point(&p), epsilon = 1.0e-7)
+                i.inverse() * v,
+                i.inverse_transform_vector(&v),
+                epsilon = 1.0e-7
+            )
+            && relative_eq!(
+                i.inverse() * p,
+                i.inverse_transform_point(&p),
+                epsilon = 1.0e-7
+            )
     }
 
-    fn composition2(i: Isometry2<f64>,    uc: UnitComplex<f64>, r: Rotation2<f64>,
+    fn composition2(
-                    t: Translation2<f64>, v:  Vector2<f64>,     p: Point2<f64>) -> bool {
+        i: Isometry2<f64>,
+        uc: UnitComplex<f64>,
+        r: Rotation2<f64>,
+        t: Translation2<f64>,
+        v: Vector2<f64>,
+        p: Point2<f64>,
+    ) -> bool
+    {
         // (rotation × translation) * point = rotation × (translation * point)
         relative_eq!((uc * t) * v, uc * v, epsilon = 1.0e-7)       &&
         relative_eq!((r  * t) * v, r  * v, epsilon = 1.0e-7)       &&
@@ -91,8 +114,15 @@ quickcheck!(
         relative_eq!((i * t) * p, i * (t * p), epsilon = 1.0e-7)
     }
 
-    fn composition3(i: Isometry3<f64>,    uq: UnitQuaternion<f64>, r: Rotation3<f64>,
+    fn composition3(
-                    t: Translation3<f64>, v:  Vector3<f64>,        p: Point3<f64>) -> bool {
+        i: Isometry3<f64>,
+        uq: UnitQuaternion<f64>,
+        r: Rotation3<f64>,
+        t: Translation3<f64>,
+        v: Vector3<f64>,
+        p: Point3<f64>,
+    ) -> bool
+    {
         // (rotation × translation) * point = rotation × (translation * point)
         relative_eq!((uq * t) * v, uq * v, epsilon = 1.0e-7)       &&
         relative_eq!((r  * t) * v, r  * v, epsilon = 1.0e-7)       &&
@@ -122,8 +152,15 @@ quickcheck!(
         relative_eq!((i * t) * p, i * (t * p), epsilon = 1.0e-7)
     }
 
-    fn all_op_exist(i: Isometry3<f64>, uq: UnitQuaternion<f64>, t: Translation3<f64>,
+    fn all_op_exist(
-                    v: Vector3<f64>, p: Point3<f64>, r: Rotation3<f64>) -> bool {
+        i: Isometry3<f64>,
+        uq: UnitQuaternion<f64>,
+        t: Translation3<f64>,
+        v: Vector3<f64>,
+        p: Point3<f64>,
+        r: Rotation3<f64>,
+    ) -> bool
+    {
         let iMi = i * i;
         let iMuq = i * uq;
         let iDi = i / i;
@@ -174,75 +211,57 @@ quickcheck!(
         iDuq1 /= uq;
         iDuq2 /= &uq;
 
-        iMt == iMt1 &&
+        iMt == iMt1
-        iMt == iMt2 &&
+            && iMt == iMt2
-
+            && iMi == iMi1
-        iMi == iMi1 &&
+            && iMi == iMi2
-        iMi == iMi2 &&
+            && iMuq == iMuq1
-
+            && iMuq == iMuq2
-        iMuq == iMuq1 &&
+            && iDi == iDi1
-        iMuq == iMuq2 &&
+            && iDi == iDi2
-
+            && iDuq == iDuq1
-        iDi == iDi1 &&
+            && iDuq == iDuq2
-        iDi == iDi2 &&
+            && iMi == &i * &i
-
+            && iMi == i * &i
-        iDuq == iDuq1 &&
+            && iMi == &i * i
-        iDuq == iDuq2 &&
+            && iMuq == &i * &uq
-
+            && iMuq == i * &uq
-        iMi == &i * &i &&
+            && iMuq == &i * uq
-        iMi ==  i * &i &&
+            && iDi == &i / &i
-        iMi == &i *  i &&
+            && iDi == i / &i
-
+            && iDi == &i / i
-        iMuq == &i * &uq &&
+            && iDuq == &i / &uq
-        iMuq ==  i * &uq &&
+            && iDuq == i / &uq
-        iMuq == &i *  uq &&
+            && iDuq == &i / uq
-
+            && iMp == &i * &p
-        iDi == &i / &i &&
+            && iMp == i * &p
-        iDi ==  i / &i &&
+            && iMp == &i * p
-        iDi == &i /  i &&
+            && iMv == &i * &v
-
+            && iMv == i * &v
-        iDuq == &i / &uq &&
+            && iMv == &i * v
-        iDuq ==  i / &uq &&
+            && iMt == &i * &t
-        iDuq == &i /  uq &&
+            && iMt == i * &t
-
+            && iMt == &i * t
-        iMp == &i * &p &&
+            && tMi == &t * &i
-        iMp ==  i * &p &&
+            && tMi == t * &i
-        iMp == &i *  p &&
+            && tMi == &t * i
-
+            && tMr == &t * &r
-        iMv == &i * &v &&
+            && tMr == t * &r
-        iMv ==  i * &v &&
+            && tMr == &t * r
-        iMv == &i *  v &&
+            && tMuq == &t * &uq
-
+            && tMuq == t * &uq
-        iMt == &i * &t &&
+            && tMuq == &t * uq
-        iMt ==  i * &t &&
+            && uqMi == &uq * &i
-        iMt == &i *  t &&
+            && uqMi == uq * &i
-
+            && uqMi == &uq * i
-        tMi == &t * &i &&
+            && uqDi == &uq / &i
-        tMi ==  t * &i &&
+            && uqDi == uq / &i
-        tMi == &t *  i &&
+            && uqDi == &uq / i
-
+            && rMt == &r * &t
-        tMr == &t * &r  &&
+            && rMt == r * &t
-        tMr ==  t * &r  &&
+            && rMt == &r * t
-        tMr == &t *  r  &&
+            && uqMt == &uq * &t
-
+            && uqMt == uq * &t
-        tMuq == &t * &uq &&
+            && uqMt == &uq * t
-        tMuq ==  t * &uq &&
-        tMuq == &t *  uq &&
-
-        uqMi == &uq * &i &&
-        uqMi ==  uq * &i &&
-        uqMi == &uq *  i &&
-
-        uqDi == &uq / &i &&
-        uqDi ==  uq / &i &&
-        uqDi == &uq /  i &&
-
-        rMt == &r * &t &&
-        rMt ==  r * &t &&
-        rMt == &r *  t &&
-
-        uqMt == &uq * &t &&
-        uqMt ==  uq * &t &&
-        uqMt == &uq *  t
     }
 );

tests/geometry/similarity.rs

@@ -8,33 +8,57 @@ quickcheck!(
     fn inverse_is_identity(i: Similarity3<f64>, p: Point3<f64>, v: Vector3<f64>) -> bool {
         let ii = i.inverse();
 
-        relative_eq!(i  * ii, Similarity3::identity(), epsilon = 1.0e-7) &&
+        relative_eq!(i * ii, Similarity3::identity(), epsilon = 1.0e-7)
-        relative_eq!(ii *  i, Similarity3::identity(), epsilon = 1.0e-7) &&
+            && relative_eq!(ii * i, Similarity3::identity(), epsilon = 1.0e-7)
-        relative_eq!((i  * ii) * p, p, epsilon = 1.0e-7) &&
+            && relative_eq!((i * ii) * p, p, epsilon = 1.0e-7)
-        relative_eq!((ii * i)  * p, p, epsilon = 1.0e-7) &&
+            && relative_eq!((ii * i) * p, p, epsilon = 1.0e-7)
-        relative_eq!((i  * ii) * v, v, epsilon = 1.0e-7) &&
+            && relative_eq!((i * ii) * v, v, epsilon = 1.0e-7)
-        relative_eq!((ii * i)  * v, v, epsilon = 1.0e-7)
+            && relative_eq!((ii * i) * v, v, epsilon = 1.0e-7)
     }
 
-    fn inverse_is_parts_inversion(t: Translation3<f64>, r: UnitQuaternion<f64>, scaling: f64) -> bool {
+    fn inverse_is_parts_inversion(
+        t: Translation3<f64>,
+        r: UnitQuaternion<f64>,
+        scaling: f64,
+    ) -> bool
+    {
         if relative_eq!(scaling, 0.0) {
             true
-        }
+        } else {
-        else {
             let s = Similarity3::from_isometry(t * r, scaling);
             s.inverse() == Similarity3::from_scaling(1.0 / scaling) * r.inverse() * t.inverse()
         }
     }
 
-    fn multiply_equals_alga_transform(s: Similarity3<f64>, v: Vector3<f64>, p: Point3<f64>) -> bool {
+    fn multiply_equals_alga_transform(
-        s * v == s.transform_vector(&v) &&
+        s: Similarity3<f64>,
-        s * p == s.transform_point(&p)  &&
+        v: Vector3<f64>,
-        relative_eq!(s.inverse() * v, s.inverse_transform_vector(&v), epsilon = 1.0e-7) &&
+        p: Point3<f64>,
-        relative_eq!(s.inverse() * p, s.inverse_transform_point(&p), epsilon = 1.0e-7)
+    ) -> bool
+    {
+        s * v == s.transform_vector(&v)
+            && s * p == s.transform_point(&p)
+            && relative_eq!(
+                s.inverse() * v,
+                s.inverse_transform_vector(&v),
+                epsilon = 1.0e-7
+            )
+            && relative_eq!(
+                s.inverse() * p,
+                s.inverse_transform_point(&p),
+                epsilon = 1.0e-7
+            )
     }
 
-    fn composition(i: Isometry3<f64>, uq: UnitQuaternion<f64>,
+    fn composition(
-                   t: Translation3<f64>, v: Vector3<f64>, p: Point3<f64>, scaling: f64) -> bool {
+        i: Isometry3<f64>,
+        uq: UnitQuaternion<f64>,
+        t: Translation3<f64>,
+        v: Vector3<f64>,
+        p: Point3<f64>,
+        scaling: f64,
+    ) -> bool
+    {
         if relative_eq!(scaling, 0.0) {
             return true;
         }
@@ -122,8 +146,15 @@ quickcheck!(
         relative_eq!((s * i * t) * p, scaling * (i * (t * p)), epsilon = 1.0e-7)
     }
 
-    fn all_op_exist(s: Similarity3<f64>, i: Isometry3<f64>, uq: UnitQuaternion<f64>,
+    fn all_op_exist(
-                    t: Translation3<f64>, v: Vector3<f64>, p: Point3<f64>) -> bool {
+        s: Similarity3<f64>,
+        i: Isometry3<f64>,
+        uq: UnitQuaternion<f64>,
+        t: Translation3<f64>,
+        v: Vector3<f64>,
+        p: Point3<f64>,
+    ) -> bool
+    {
         let sMs = s * s;
         let sMuq = s * uq;
         let sDs = s / s;
@@ -186,81 +217,61 @@ quickcheck!(
         sDi1 /= i;
         sDi2 /= &i;
 
-        sMt == sMt1 &&
+        sMt == sMt1
-        sMt == sMt2 &&
+            && sMt == sMt2
-
+            && sMs == sMs1
-        sMs == sMs1 &&
+            && sMs == sMs2
-        sMs == sMs2 &&
+            && sMuq == sMuq1
-
+            && sMuq == sMuq2
-        sMuq == sMuq1 &&
+            && sMi == sMi1
-        sMuq == sMuq2 &&
+            && sMi == sMi2
-
+            && sDs == sDs1
-        sMi == sMi1 &&
+            && sDs == sDs2
-        sMi == sMi2 &&
+            && sDuq == sDuq1
-
+            && sDuq == sDuq2
-        sDs == sDs1 &&
+            && sDi == sDi1
-        sDs == sDs2 &&
+            && sDi == sDi2
-
+            && sMs == &s * &s
-        sDuq == sDuq1 &&
+            && sMs == s * &s
-        sDuq == sDuq2 &&
+            && sMs == &s * s
-
+            && sMuq == &s * &uq
-        sDi == sDi1 &&
+            && sMuq == s * &uq
-        sDi == sDi2 &&
+            && sMuq == &s * uq
-
+            && sDs == &s / &s
-        sMs == &s * &s &&
+            && sDs == s / &s
-        sMs ==  s * &s &&
+            && sDs == &s / s
-        sMs == &s *  s &&
+            && sDuq == &s / &uq
-
+            && sDuq == s / &uq
-        sMuq == &s * &uq &&
+            && sDuq == &s / uq
-        sMuq ==  s * &uq &&
+            && sMp == &s * &p
-        sMuq == &s *  uq &&
+            && sMp == s * &p
-
+            && sMp == &s * p
-        sDs == &s / &s &&
+            && sMv == &s * &v
-        sDs ==  s / &s &&
+            && sMv == s * &v
-        sDs == &s /  s &&
+            && sMv == &s * v
-
+            && sMt == &s * &t
-        sDuq == &s / &uq &&
+            && sMt == s * &t
-        sDuq ==  s / &uq &&
+            && sMt == &s * t
-        sDuq == &s /  uq &&
+            && tMs == &t * &s
-
+            && tMs == t * &s
-        sMp == &s * &p &&
+            && tMs == &t * s
-        sMp ==  s * &p &&
+            && uqMs == &uq * &s
-        sMp == &s *  p &&
+            && uqMs == uq * &s
-
+            && uqMs == &uq * s
-        sMv == &s * &v &&
+            && uqDs == &uq / &s
-        sMv ==  s * &v &&
+            && uqDs == uq / &s
-        sMv == &s *  v &&
+            && uqDs == &uq / s
-
+            && sMi == &s * &i
-        sMt == &s * &t &&
+            && sMi == s * &i
-        sMt ==  s * &t &&
+            && sMi == &s * i
-        sMt == &s *  t &&
+            && sDi == &s / &i
-
+            && sDi == s / &i
-        tMs == &t * &s &&
+            && sDi == &s / i
-        tMs ==  t * &s &&
+            && iMs == &i * &s
-        tMs == &t *  s &&
+            && iMs == i * &s
-
+            && iMs == &i * s
-        uqMs == &uq * &s &&
+            && iDs == &i / &s
-        uqMs ==  uq * &s &&
+            && iDs == i / &s
-        uqMs == &uq *  s &&
+            && iDs == &i / s
-
-        uqDs == &uq / &s &&
-        uqDs ==  uq / &s &&
-        uqDs == &uq /  s &&
-
-        sMi == &s * &i &&
-        sMi ==  s * &i &&
-        sMi == &s *  i &&
-
-        sDi == &s / &i &&
-        sDi ==  s / &i &&
-        sDi == &s /  i &&
-
-        iMs == &i * &s &&
-        iMs ==  i * &s &&
-        iMs == &i *  s &&
-
-        iDs == &i / &s &&
-        iDs ==  i / &s &&
-        iDs == &i /  s
     }
 );

tests/geometry/unit_complex.rs

@@ -4,7 +4,6 @@
 use na::{Point2, Rotation2, Unit, UnitComplex, Vector2};
 
 quickcheck!(
-
     /*
      *
      * From/to rotation matrix.
@@ -15,8 +14,7 @@ quickcheck!(
         let cc = UnitComplex::from_rotation_matrix(&r);
         let rr = cc.to_rotation_matrix();
 
-        relative_eq!(c, cc, epsilon = 1.0e-7) &&
+        relative_eq!(c, cc, epsilon = 1.0e-7) && relative_eq!(r, rr, epsilon = 1.0e-7)
-        relative_eq!(r, rr, epsilon = 1.0e-7)
     }
 
     /*
@@ -29,15 +27,14 @@ quickcheck!(
         let rv = r * v;
         let rp = r * p;
 
-        relative_eq!( c *  v, rv, epsilon = 1.0e-7) &&
+        relative_eq!(c * v, rv, epsilon = 1.0e-7)
-        relative_eq!( c * &v, rv, epsilon = 1.0e-7) &&
+            && relative_eq!(c * &v, rv, epsilon = 1.0e-7)
-        relative_eq!(&c *  v, rv, epsilon = 1.0e-7) &&
+            && relative_eq!(&c * v, rv, epsilon = 1.0e-7)
-        relative_eq!(&c * &v, rv, epsilon = 1.0e-7) &&
+            && relative_eq!(&c * &v, rv, epsilon = 1.0e-7)
-
+            && relative_eq!(c * p, rp, epsilon = 1.0e-7)
-        relative_eq!( c *  p, rp, epsilon = 1.0e-7) &&
+            && relative_eq!(c * &p, rp, epsilon = 1.0e-7)
-        relative_eq!( c * &p, rp, epsilon = 1.0e-7) &&
+            && relative_eq!(&c * p, rp, epsilon = 1.0e-7)
-        relative_eq!(&c *  p, rp, epsilon = 1.0e-7) &&
+            && relative_eq!(&c * &p, rp, epsilon = 1.0e-7)
-        relative_eq!(&c * &p, rp, epsilon = 1.0e-7)
     }
 
     /*
@@ -47,15 +44,14 @@ quickcheck!(
      */
     fn unit_complex_inv(c: UnitComplex<f64>) -> bool {
         let iq = c.inverse();
-        relative_eq!(&iq * &c, UnitComplex::identity(), epsilon = 1.0e-7) &&
+        relative_eq!(&iq * &c, UnitComplex::identity(), epsilon = 1.0e-7)
-        relative_eq!( iq * &c, UnitComplex::identity(), epsilon = 1.0e-7) &&
+            && relative_eq!(iq * &c, UnitComplex::identity(), epsilon = 1.0e-7)
-        relative_eq!(&iq *  c, UnitComplex::identity(), epsilon = 1.0e-7) &&
+            && relative_eq!(&iq * c, UnitComplex::identity(), epsilon = 1.0e-7)
-        relative_eq!( iq *  c, UnitComplex::identity(), epsilon = 1.0e-7) &&
+            && relative_eq!(iq * c, UnitComplex::identity(), epsilon = 1.0e-7)
-
+            && relative_eq!(&c * &iq, UnitComplex::identity(), epsilon = 1.0e-7)
-        relative_eq!(&c * &iq, UnitComplex::identity(), epsilon = 1.0e-7) &&
+            && relative_eq!(c * &iq, UnitComplex::identity(), epsilon = 1.0e-7)
-        relative_eq!( c * &iq, UnitComplex::identity(), epsilon = 1.0e-7) &&
+            && relative_eq!(&c * iq, UnitComplex::identity(), epsilon = 1.0e-7)
-        relative_eq!(&c *  iq, UnitComplex::identity(), epsilon = 1.0e-7) &&
+            && relative_eq!(c * iq, UnitComplex::identity(), epsilon = 1.0e-7)
-        relative_eq!( c *  iq, UnitComplex::identity(), epsilon = 1.0e-7)
     }
 
     /*
@@ -66,14 +62,19 @@ quickcheck!(
     fn unit_complex_mul_vector(c: UnitComplex<f64>, v: Vector2<f64>, p: Point2<f64>) -> bool {
         let r = c.to_rotation_matrix();
 
-        relative_eq!(c * v, r * v, epsilon = 1.0e-7) &&
+        relative_eq!(c * v, r * v, epsilon = 1.0e-7) && relative_eq!(c * p, r * p, epsilon = 1.0e-7)
-        relative_eq!(c * p, r * p, epsilon = 1.0e-7)
     }
 
     // Test that all operators (incl. all combinations of references) work.
     // See the top comment on `geometry/quaternion_ops.rs` for details on which operations are
     // supported.
-    fn all_op_exist(uc: UnitComplex<f64>, v: Vector2<f64>, p: Point2<f64>, r: Rotation2<f64>) -> bool {
+    fn all_op_exist(
+        uc: UnitComplex<f64>,
+        v: Vector2<f64>,
+        p: Point2<f64>,
+        r: Rotation2<f64>,
+    ) -> bool
+    {
         let uv = Unit::new_normalize(v);
 
         let ucMuc = uc * uc;
@@ -111,52 +112,40 @@ quickcheck!(
         ucDr1 /= r;
         ucDr2 /= &r;
 
-        ucMuc1 == ucMuc  &&
+        ucMuc1 == ucMuc
-        ucMuc1 == ucMuc2 &&
+            && ucMuc1 == ucMuc2
-
+            && ucMr1 == ucMr
-        ucMr1  == ucMr   &&
+            && ucMr1 == ucMr2
-        ucMr1  == ucMr2  &&
+            && ucDuc1 == ucDuc
-
+            && ucDuc1 == ucDuc2
-        ucDuc1 == ucDuc  &&
+            && ucDr1 == ucDr
-        ucDuc1 == ucDuc2 &&
+            && ucDr1 == ucDr2
-
+            && ucMuc == &uc * &uc
-        ucDr1  == ucDr   &&
+            && ucMuc == uc * &uc
-        ucDr1  == ucDr2  &&
+            && ucMuc == &uc * uc
-
+            && ucMr == &uc * &r
-        ucMuc == &uc * &uc &&
+            && ucMr == uc * &r
-        ucMuc ==  uc * &uc &&
+            && ucMr == &uc * r
-        ucMuc == &uc *  uc &&
+            && rMuc == &r * &uc
-
+            && rMuc == r * &uc
-        ucMr == &uc * &r &&
+            && rMuc == &r * uc
-        ucMr ==  uc * &r &&
+            && ucDuc == &uc / &uc
-        ucMr == &uc *  r &&
+            && ucDuc == uc / &uc
-
+            && ucDuc == &uc / uc
-        rMuc == &r * &uc &&
+            && ucDr == &uc / &r
-        rMuc ==  r * &uc &&
+            && ucDr == uc / &r
-        rMuc == &r *  uc &&
+            && ucDr == &uc / r
-
+            && rDuc == &r / &uc
-        ucDuc == &uc / &uc &&
+            && rDuc == r / &uc
-        ucDuc ==  uc / &uc &&
+            && rDuc == &r / uc
-        ucDuc == &uc /  uc &&
+            && ucMp == &uc * &p
-
+            && ucMp == uc * &p
-        ucDr == &uc / &r &&
+            && ucMp == &uc * p
-        ucDr ==  uc / &r &&
+            && ucMv == &uc * &v
-        ucDr == &uc /  r &&
+            && ucMv == uc * &v
-
+            && ucMv == &uc * v
-        rDuc == &r / &uc &&
+            && ucMuv == &uc * &uv
-        rDuc ==  r / &uc &&
+            && ucMuv == uc * &uv
-        rDuc == &r /  uc &&
+            && ucMuv == &uc * uv
-
-        ucMp == &uc * &p &&
-        ucMp ==  uc * &p &&
-        ucMp == &uc *  p &&
-
-        ucMv == &uc * &v &&
-        ucMv ==  uc * &v &&
-        ucMv == &uc *  v &&
-
-        ucMuv == &uc * &uv &&
-        ucMuv ==  uc * &uv &&
-        ucMuv == &uc *  uv
     }
 );

tests/lib.rs

@@ -16,3 +16,5 @@ extern crate serde_json;
 mod core;
 mod geometry;
 mod linalg;
+#[cfg(feature = "sparse")]
+mod sparse;

tests/linalg/eigen.rs

@@ -104,3 +104,89 @@ fn symmetric_eigen_singular_24x24() {
         epsilon = 1.0e-5
     ));
 }
+
+//  #[cfg(feature = "arbitrary")]
+//  quickcheck! {
+// FIXME: full eigendecomposition is not implemented yet because of its complexity when some
+// eigenvalues have multiplicity > 1.
+//
+//    /*
+//     * NOTE: for the following tests, we use only upper-triangular matrices.
+//     * Thes ensures the schur decomposition will work, and allows use to test the eigenvector
+//     * computation.
+//     */
+//    fn eigen(n: usize) -> bool {
+//        let n = cmp::max(1, cmp::min(n, 10));
+//        let m = DMatrix::<f64>::new_random(n, n).upper_triangle();
+//
+//        let eig = RealEigen::new(m.clone()).unwrap();
+//        verify_eigenvectors(m, eig)
+//    }
+//
+//    fn eigen_with_adjascent_duplicate_diagonals(n: usize) -> bool {
+//        let n = cmp::max(1, cmp::min(n, 10));
+//        let mut m = DMatrix::<f64>::new_random(n, n).upper_triangle();
+//
+//        // Suplicate some adjascent diagonal elements.
+//        for i in 0 .. n / 2 {
+//            m[(i * 2 + 1, i * 2 + 1)] = m[(i * 2, i * 2)];
+//        }
+//
+//        let eig = RealEigen::new(m.clone()).unwrap();
+//        verify_eigenvectors(m, eig)
+//    }
+//
+//    fn eigen_with_nonadjascent_duplicate_diagonals(n: usize) -> bool {
+//        let n = cmp::max(3, cmp::min(n, 10));
+//        let mut m = DMatrix::<f64>::new_random(n, n).upper_triangle();
+//
+//        // Suplicate some diagonal elements.
+//        for i in n / 2 .. n {
+//            m[(i, i)] = m[(i - n / 2, i - n / 2)];
+//        }
+//
+//        let eig = RealEigen::new(m.clone()).unwrap();
+//        verify_eigenvectors(m, eig)
+//    }
+//
+//    fn eigen_static_square_4x4(m: Matrix4<f64>) -> bool {
+//        let m = m.upper_triangle();
+//        let eig = RealEigen::new(m.clone()).unwrap();
+//        verify_eigenvectors(m, eig)
+//    }
+//
+//    fn eigen_static_square_3x3(m: Matrix3<f64>) -> bool {
+//        let m = m.upper_triangle();
+//        let eig = RealEigen::new(m.clone()).unwrap();
+//        verify_eigenvectors(m, eig)
+//    }
+//
+//    fn eigen_static_square_2x2(m: Matrix2<f64>) -> bool {
+//        let m = m.upper_triangle();
+//        println!("{}", m);
+//        let eig = RealEigen::new(m.clone()).unwrap();
+//        verify_eigenvectors(m, eig)
+//    }
+//  }
+//
+// fn verify_eigenvectors<D: Dim>(m: MatrixN<f64, D>, mut eig: RealEigen<f64, D>) -> bool
+//     where DefaultAllocator: Allocator<f64, D, D>   +
+//                             Allocator<f64, D>      +
+//                             Allocator<usize, D, D> +
+//                             Allocator<usize, D>,
+//           MatrixN<f64, D>: Display,
+//           VectorN<f64, D>: Display {
+//     let mv = &m * &eig.eigenvectors;
+//
+//     println!("eigenvalues: {}eigenvectors: {}", eig.eigenvalues, eig.eigenvectors);
+//
+//     let dim = m.nrows();
+//     for i in 0 .. dim {
+//         let mut col = eig.eigenvectors.column_mut(i);
+//         col *= eig.eigenvalues[i];
+//     }
+//
+//     println!("{}{:.5}{:.5}", m, mv, eig.eigenvectors);
+//
+//     relative_eq!(eig.eigenvectors, mv, epsilon = 1.0e-5)
+// }

tests/linalg/svd.rs

@@ -3,8 +3,10 @@ use na::{DMatrix, Matrix6};
 
 #[cfg(feature = "arbitrary")]
 mod quickcheck_tests {
+    use na::{
+        DMatrix, DVector, Matrix2, Matrix2x5, Matrix3, Matrix3x5, Matrix4, Matrix5x2, Matrix5x3,
+    };
     use std::cmp;
-    use na::{DMatrix, Matrix2, Matrix3, Matrix4, Matrix5x2, Matrix5x3, Matrix2x5, Matrix3x5, DVector};
 
     quickcheck! {
         fn svd(m: DMatrix<f64>) -> bool {
@@ -258,7 +260,6 @@ fn svd_singular_horizontal() {
     assert!(relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5));
 }
 
-
 #[test]
 fn svd_zeros() {
     let m = DMatrix::from_element(10, 10, 0.0);

tests/sparse/cs_cholesky.rs

@@ -0,0 +1,75 @@
+#![cfg_attr(rustfmt, rustfmt_skip)]
+
+use na::{CsMatrix, CsVector, CsCholesky, Cholesky, Matrix5, Vector5};
+
+#[test]
+fn cs_cholesky() {
+    let mut a = Matrix5::new(
+        40.0, 0.0, 0.0, 0.0, 0.0,
+        2.0, 60.0, 0.0, 0.0, 0.0,
+        1.0, 0.0, 11.0, 0.0, 0.0,
+        0.0, 0.0, 0.0, 50.0, 0.0,
+        1.0, 0.0, 0.0, 4.0, 10.0
+    );
+    a.fill_upper_triangle_with_lower_triangle();
+    test_cholesky(a);
+
+    let a = Matrix5::from_diagonal(&Vector5::new(40.0, 60.0, 11.0, 50.0, 10.0));
+    test_cholesky(a);
+
+    let mut a = Matrix5::new(
+        40.0, 0.0, 0.0, 0.0, 0.0,
+        2.0, 60.0, 0.0, 0.0, 0.0,
+        1.0, 0.0, 11.0, 0.0, 0.0,
+        1.0, 0.0, 0.0, 50.0, 0.0,
+        0.0, 0.0, 0.0, 4.0, 10.0
+    );
+    a.fill_upper_triangle_with_lower_triangle();
+    test_cholesky(a);
+
+    let mut a = Matrix5::new(
+        2.0, 0.0, 0.0, 0.0, 0.0,
+        0.0, 2.0, 0.0, 0.0, 0.0,
+        1.0, 1.0, 2.0, 0.0, 0.0,
+        0.0, 0.0, 0.0, 2.0, 0.0,
+        1.0, 1.0, 0.0, 0.0, 2.0
+    );
+    a.fill_upper_triangle_with_lower_triangle();
+    // Test ::new, left_looking, and up_looking implementations.
+    test_cholesky(a);
+}
+
+fn test_cholesky(a: Matrix5<f32>) {
+    // Test ::new
+    test_cholesky_variant(a, 0);
+    // Test up-looking
+    test_cholesky_variant(a, 1);
+    // Test left-looking
+    test_cholesky_variant(a, 2);
+}
+
+fn test_cholesky_variant(a: Matrix5<f32>, option: usize) {
+    let cs_a: CsMatrix<_, _, _> = a.into();
+
+    let chol_a = Cholesky::new(a).unwrap();
+    let mut chol_cs_a;
+
+    match option {
+        0 => chol_cs_a = CsCholesky::new(&cs_a),
+        1 => {
+            chol_cs_a = CsCholesky::new_symbolic(&cs_a);
+            chol_cs_a.decompose_up_looking(cs_a.data.values());
+        }
+        _ => {
+            chol_cs_a = CsCholesky::new_symbolic(&cs_a);
+            chol_cs_a.decompose_left_looking(cs_a.data.values());
+        }
+    };
+
+    let l = chol_a.l();
+    let cs_l = chol_cs_a.unwrap_l().unwrap();
+    assert!(cs_l.is_sorted());
+
+    let cs_l_mat: Matrix5<_> = cs_l.into();
+    assert_relative_eq!(l, cs_l_mat);
+}

tests/sparse/cs_construction.rs

@@ -0,0 +1 @@
+

tests/sparse/cs_conversion.rs

@@ -0,0 +1,89 @@
+use na::{CsMatrix, DMatrix, Matrix4x5};
+
+#[test]
+fn cs_from_to_matrix() {
+    #[cfg_attr(rustfmt, rustfmt_skip)]
+    let m = Matrix4x5::new(
+        5.0, 6.0, 0.0, 8.0, 15.0,
+        9.0, 10.0, 11.0, 12.0, 0.0,
+        0.0, 0.0, 13.0, 0.0, 0.0,
+        0.0, 1.0, 4.0, 0.0, 14.0,
+    );
+
+    let cs: CsMatrix<_, _, _> = m.into();
+    assert!(cs.is_sorted());
+
+    let m2: Matrix4x5<_> = cs.into();
+    assert_eq!(m2, m);
+}
+
+#[test]
+fn cs_matrix_from_triplet() {
+    let mut irows = vec![0, 0, 0, 0, 1, 1, 1, 1, 2, 3, 3, 3];
+    let mut icols = vec![0, 1, 3, 4, 0, 1, 2, 3, 2, 1, 2, 4];
+    let mut vals = vec![
+        5.0, 6.0, 8.0, 15.0, 9.0, 10.0, 11.0, 12.0, 13.0, 1.0, 4.0, 14.0,
+    ];
+
+    #[cfg_attr(rustfmt, rustfmt_skip)]
+    let expected = DMatrix::from_row_slice(4, 5, &[
+        5.0, 6.0, 0.0, 8.0, 15.0,
+        9.0, 10.0, 11.0, 12.0, 0.0,
+        0.0, 0.0, 13.0, 0.0, 0.0,
+        0.0, 1.0, 4.0, 0.0, 14.0,
+    ]);
+    let cs_expected = CsMatrix::from_parts(
+        4,
+        5,
+        vec![0, 2, 5, 8, 10],
+        vec![0, 1, 0, 1, 3, 1, 2, 3, 0, 1, 0, 3],
+        vec![
+            5.0, 9.0, 6.0, 10.0, 1.0, 11.0, 13.0, 4.0, 8.0, 12.0, 15.0, 14.0,
+        ],
+    );
+
+    let cs_mat = CsMatrix::from_triplet(4, 5, &irows, &icols, &vals);
+    println!("Mat from triplet: {:?}", cs_mat);
+    assert!(cs_mat.is_sorted());
+    assert_eq!(cs_mat, cs_expected);
+
+    let m: DMatrix<_> = cs_mat.into();
+    assert_eq!(m, expected);
+
+    /*
+     * Try again with some permutations.
+     */
+    let permutations = [(2, 5), (0, 4), (8, 10), (1, 11)];
+
+    for (i, j) in &permutations {
+        irows.swap(*i, *j);
+        icols.swap(*i, *j);
+        vals.swap(*i, *j);
+    }
+
+    let cs_mat = CsMatrix::from_triplet(4, 5, &irows, &icols, &vals);
+    println!("Mat from triplet: {:?}", cs_mat);
+    assert!(cs_mat.is_sorted());
+    assert_eq!(cs_mat, cs_expected);
+
+    let m: DMatrix<_> = cs_mat.into();
+    assert_eq!(m, expected);
+
+    /*
+     * Try again, duplicating all entries.
+     */
+    let mut ir = irows.clone();
+    let mut ic = icols.clone();
+    let mut va = vals.clone();
+    irows.append(&mut ir);
+    icols.append(&mut ic);
+    vals.append(&mut va);
+
+    let cs_mat = CsMatrix::from_triplet(4, 5, &irows, &icols, &vals);
+    println!("Mat from triplet: {:?}", cs_mat);
+    assert!(cs_mat.is_sorted());
+    assert_eq!(cs_mat, cs_expected * 2.0);
+
+    let m: DMatrix<_> = cs_mat.into();
+    assert_eq!(m, expected * 2.0);
+}

tests/sparse/cs_matrix.rs

@@ -0,0 +1,22 @@
+#![cfg_attr(rustfmt, rustfmt_skip)]
+
+use na::{Matrix4x5, Matrix5x4, CsMatrix};
+
+#[test]
+fn cs_transpose() {
+    let m = Matrix4x5::new(
+        4.0, 1.0, 4.0, 0.0, 9.0,
+        5.0, 6.0, 0.0, 8.0, 10.0,
+        9.0, 10.0, 11.0, 12.0, 0.0,
+        0.0, 0.0, 1.0, 0.0, 10.0
+    );
+
+    let cs: CsMatrix<_, _, _> = m.into();
+    assert!(cs.is_sorted());
+
+    let cs_transposed = cs.transpose();
+    assert!(cs_transposed.is_sorted());
+
+    let cs_transposed_mat: Matrix5x4<_> = cs_transposed.into();
+    assert_eq!(cs_transposed_mat, m.transpose())
+}

tests/sparse/cs_matrix_market.rs

@@ -0,0 +1,55 @@
+#![cfg_attr(rustfmt, rustfmt_skip)]
+
+
+use na::io;
+use na::DMatrix;
+
+#[test]
+fn cs_matrix_market() {
+    let file_str = r#"
+  %%MatrixMarket matrix coordinate real general
+%=================================================================================
+%
+% This ASCII file represents a sparse MxN matrix with L
+% nonzeros in the following Matrix Market format:
+%
+% +----------------------------------------------+
+% |%%MatrixMarket matrix coordinate real general | <--- header line
+% |%                                             | <--+
+% |% comments                                    |    |-- 0 or more comment lines
+% |%                                             | <--+
+% |    M  N  L                                   | <--- rows, columns, entries
+% |    I1  J1  A(I1, J1)                         | <--+
+% |    I2  J2  A(I2, J2)                         |    |
+% |    I3  J3  A(I3, J3)                         |    |-- L lines
+% |        . . .                                 |    |
+% |    IL JL  A(IL, JL)                          | <--+
+% +----------------------------------------------+
+%
+% Indices are 1-based, i.e. A(1,1) is the first element.
+%
+%=================================================================================
+  5  5  8
+    1     1   1.000e+00
+    2     2   1.050e+01
+    3     3   1.500e-02
+    1     4   6.000e+00
+    4     2   2.505e+02
+    4     4  -2.800e+02
+    4     5   3.332e+01
+    5     5   1.200e+01
+"#;
+
+    let cs_mat = io::cs_matrix_from_matrix_market_str(file_str).unwrap();
+    println!("CS mat: {:?}", cs_mat);
+    let mat: DMatrix<_> = cs_mat.into();
+    let expected = DMatrix::from_row_slice(5, 5, &[
+        1.0, 0.0,   0.0,   6.0,    0.0,
+        0.0, 10.5,  0.0,   0.0,    0.0,
+        0.0, 0.0,   0.015, 0.0,    0.0,
+        0.0, 250.5, 0.0,   -280.0, 33.32,
+        0.0, 0.0,   0.0,   0.0,    12.0,
+    ]);
+
+    assert_eq!(mat, expected);
+}

tests/sparse/cs_ops.rs

@@ -0,0 +1,72 @@
+#![cfg_attr(rustfmt, rustfmt_skip)]
+
+
+use na::{Matrix3x4, Matrix4x5, Matrix3x5, CsMatrix, Vector5, CsVector};
+
+#[test]
+fn axpy_cs() {
+    let mut v1 = Vector5::new(1.0, 2.0, 3.0, 4.0, 5.0);
+    let v2 = Vector5::new(10.0, 0.0, 30.0, 0.0, 50.0);
+    let expected = 5.0 * v2 + 10.0 * v1;
+
+    let cs: CsVector<_, _> = v2.into();
+    v1.axpy_cs(5.0, &cs, 10.0);
+
+    assert!(cs.is_sorted());
+    assert_eq!(v1, expected)
+}
+
+
+#[test]
+fn cs_mat_mul() {
+    let m1 = Matrix3x4::new(
+        0.0, 1.0, 4.0, 0.0,
+        5.0, 6.0, 0.0, 8.0,
+        9.0, 10.0, 11.0, 12.0,
+    );
+
+    let m2 = Matrix4x5::new(
+        5.0, 6.0, 0.0, 8.0, 15.0,
+        9.0, 10.0, 11.0, 12.0, 0.0,
+        0.0, 0.0, 13.0, 0.0, 0.0,
+        0.0, 1.0, 4.0, 0.0, 14.0,
+    );
+
+    let sm1: CsMatrix<_, _, _> = m1.into();
+    let sm2: CsMatrix<_, _, _> = m2.into();
+
+    let mul = &sm1 * &sm2;
+
+    assert!(sm1.is_sorted());
+    assert!(sm2.is_sorted());
+    assert!(mul.is_sorted());
+    assert_eq!(Matrix3x5::from(mul), m1 * m2);
+}
+
+
+#[test]
+fn cs_mat_add() {
+    let m1 = Matrix4x5::new(
+        4.0, 1.0, 4.0, 0.0, 0.0,
+        5.0, 6.0, 0.0, 8.0, 0.0,
+        9.0, 10.0, 11.0, 12.0, 0.0,
+        0.0, 0.0, 1.0, 0.0, 10.0
+    );
+
+    let m2 = Matrix4x5::new(
+        0.0, 1.0, 4.0, 0.0, 14.0,
+        5.0, 6.0, 0.0, 8.0, 15.0,
+        9.0, 10.0, 11.0, 12.0, 0.0,
+        0.0, 0.0, 13.0, 0.0, 0.0,
+    );
+
+    let sm1: CsMatrix<_, _, _> = m1.into();
+    let sm2: CsMatrix<_, _, _> = m2.into();
+
+    let sum = &sm1 + &sm2;
+
+    assert!(sm1.is_sorted());
+    assert!(sm2.is_sorted());
+    assert!(sum.is_sorted());
+    assert_eq!(Matrix4x5::from(sum), m1 + m2);
+}

tests/sparse/cs_solve.rs

@@ -0,0 +1,106 @@
+#![cfg_attr(rustfmt, rustfmt_skip)]
+
+use na::{CsMatrix, CsVector, Matrix5, Vector5};
+
+
+#[test]
+fn cs_lower_triangular_solve() {
+    let a = Matrix5::new(
+        4.0, 1.0,  4.0,  0.0,  9.0,
+        5.0, 6.0,  0.0,  8.0,  10.0,
+        9.0, 10.0, 11.0, 12.0, 0.0,
+        0.0, -8.0, 3.0,  5.0,  9.0,
+        0.0, 0.0,  1.0,  0.0,  -10.0
+    );
+    let b = Vector5::new(1.0, 2.0, 3.0, 4.0, 5.0);
+
+    let cs_a: CsMatrix<_, _, _> = a.into();
+
+    assert_eq!(cs_a.solve_lower_triangular(&b), a.solve_lower_triangular(&b));
+}
+
+#[test]
+fn cs_tr_lower_triangular_solve() {
+    let a = Matrix5::new(
+        4.0, 1.0,  4.0,  0.0,  9.0,
+        5.0, 6.0,  0.0,  8.0,  10.0,
+        9.0, 10.0, 11.0, 12.0, 0.0,
+        0.0, -8.0, 3.0,  5.0,  9.0,
+        0.0, 0.0,  1.0,  0.0,  -10.0
+    );
+    let b = Vector5::new(1.0, 2.0, 3.0, 4.0, 5.0);
+
+    let cs_a: CsMatrix<_, _, _> = a.into();
+
+    assert!(cs_a.tr_solve_lower_triangular(&b).is_some());
+    assert_eq!(cs_a.tr_solve_lower_triangular(&b), a.tr_solve_lower_triangular(&b));
+
+    // Singular case.
+    let a = Matrix5::new(
+        4.0, 1.0,  4.0, 0.0,  9.0,
+        5.0, 6.0,  0.0, 8.0,  10.0,
+        9.0, 10.0, 0.0, 12.0, 0.0,
+        0.0, -8.0, 3.0, 5.0,  9.0,
+        0.0, 0.0,  1.0, 0.0,  -10.0
+    );
+    let cs_a: CsMatrix<_, _, _> = a.into();
+
+    assert!(cs_a.tr_solve_lower_triangular(&b).is_none());
+}
+
+
+#[test]
+fn cs_lower_triangular_solve_cs() {
+    let a = Matrix5::new(
+        4.0, 1.0,  4.0,  0.0,  9.0,
+        5.0, 6.0,  0.0,  8.0,  10.0,
+        9.0, 10.0, 11.0, 12.0, 0.0,
+        0.0, -8.0, 3.0,  5.0,  9.0,
+        0.0, 0.0,  1.0,  0.0,  -10.0
+    );
+    let b1 = Vector5::zeros();
+    let b2 = Vector5::new(1.0, 2.0, 3.0, 4.0, 5.0);
+    let b3 = Vector5::new(1.0, 0.0, 0.0, 4.0, 0.0);
+    let b4 = Vector5::new(0.0, 1.0, 0.0, 4.0, 5.0);
+    let b5 = Vector5::x();
+    let b6 = Vector5::y();
+    let b7 = Vector5::z();
+    let b8 = Vector5::w();
+    let b9 = Vector5::a();
+
+    let cs_a: CsMatrix<_, _, _> = a.into();
+    let cs_b1: CsVector<_, _> = Vector5::zeros().into();
+    let cs_b2: CsVector<_, _> = Vector5::new(1.0, 2.0, 3.0, 4.0, 5.0).into();
+    let cs_b3: CsVector<_, _> = Vector5::new(1.0, 0.0, 0.0, 4.0, 0.0).into();
+    let cs_b4: CsVector<_, _> = Vector5::new(0.0, 1.0, 0.0, 4.0, 5.0).into();
+    let cs_b5: CsVector<_, _> = Vector5::x().into();
+    let cs_b6: CsVector<_, _> = Vector5::y().into();
+    let cs_b7: CsVector<_, _> = Vector5::z().into();
+    let cs_b8: CsVector<_, _> = Vector5::w().into();
+    let cs_b9: CsVector<_, _> = Vector5::a().into();
+
+    assert_eq!(cs_a.solve_lower_triangular_cs(&cs_b1).map(|v| { assert!(v.is_sorted()); v.into() }), a.solve_lower_triangular(&b1));
+    assert_eq!(cs_a.solve_lower_triangular_cs(&cs_b5).map(|v| { assert!(v.is_sorted()); v.into() }), a.solve_lower_triangular(&b5));
+    assert_eq!(cs_a.solve_lower_triangular_cs(&cs_b6).map(|v| { assert!(v.is_sorted()); v.into() }), a.solve_lower_triangular(&b6));
+    assert_eq!(cs_a.solve_lower_triangular_cs(&cs_b7).map(|v| { assert!(v.is_sorted()); v.into() }), a.solve_lower_triangular(&b7));
+    assert_eq!(cs_a.solve_lower_triangular_cs(&cs_b8).map(|v| { assert!(v.is_sorted()); v.into() }), a.solve_lower_triangular(&b8));
+    assert_eq!(cs_a.solve_lower_triangular_cs(&cs_b9).map(|v| { assert!(v.is_sorted()); v.into() }), a.solve_lower_triangular(&b9));
+    assert_eq!(cs_a.solve_lower_triangular_cs(&cs_b2).map(|v| { assert!(v.is_sorted()); v.into() }), a.solve_lower_triangular(&b2));
+    assert_eq!(cs_a.solve_lower_triangular_cs(&cs_b3).map(|v| { assert!(v.is_sorted()); v.into() }), a.solve_lower_triangular(&b3));
+    assert_eq!(cs_a.solve_lower_triangular_cs(&cs_b4).map(|v| { assert!(v.is_sorted()); v.into() }), a.solve_lower_triangular(&b4));
+
+
+    // Singular case.
+    let a = Matrix5::new(
+        4.0, 1.0,  4.0, 0.0,  9.0,
+        5.0, 0.0,  0.0, 8.0,  10.0,
+        9.0, 10.0, 0.0, 12.0, 0.0,
+        0.0, -8.0, 3.0, 5.0,  9.0,
+        0.0, 0.0,  1.0, 0.0,  -10.0
+    );
+    let cs_a: CsMatrix<_, _, _> = a.into();
+
+    assert!(cs_a.solve_lower_triangular_cs(&cs_b2).is_none());
+    assert!(cs_a.solve_lower_triangular_cs(&cs_b3).is_none());
+    assert!(cs_a.solve_lower_triangular_cs(&cs_b4).is_none());
+}

tests/sparse/mod.rs

@@ -0,0 +1,8 @@
+mod cs_cholesky;
+mod cs_construction;
+mod cs_conversion;
+mod cs_matrix;
+#[cfg(feature = "io")]
+mod cs_matrix_market;
+mod cs_ops;
+mod cs_solve;