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Docs for most items in nalgebra-sparse

This commit is contained in:
Andreas Longva 2021-01-22 14:32:13 +01:00
parent 7a083d50f7
commit 15c4382fa9
10 changed files with 251 additions and 64 deletions
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4
nalgebra-sparse/Cargo.toml
View File

@ -21,3 +21,7 @@ matrixcompare-core = { version = "0.1.0", optional = true }
itertools = "0.9"
matrixcompare = { version = "0.2.0", features = [ "proptest-support" ] }
nalgebra = { version="0.23", path = "../", features = ["compare"] }
[package.metadata.docs.rs]
# Enable certain features when building docs for docs.rs
features = [ "proptest-support", "compare" ]

37
nalgebra-sparse/src/convert/mod.rs
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@ -1,4 +1,39 @@
//! TODO
//! Routines for converting between sparse matrix formats.
//!
//! Most users should instead use the provided `From` implementations to convert between matrix
//! formats. Note that `From` implementations may not be available between all combinations of
//! sparse matrices.
//!
//! The following example illustrates how to convert between matrix formats with the `From`
//! implementations.
//!
//! ```rust
//! use nalgebra_sparse::{csr::CsrMatrix, csc::CscMatrix, coo::CooMatrix};
//! use nalgebra::DMatrix;
//!
//! // Conversion from dense
//! let dense = DMatrix::<f64>::identity(9, 8);
//! let csr = CsrMatrix::from(&dense);
//! let csc = CscMatrix::from(&dense);
//! let coo = CooMatrix::from(&dense);
//!
//! // CSR <-> CSC
//! let _ = CsrMatrix::from(&csc);
//! let _ = CscMatrix::from(&csr);
//!
//! // CSR <-> COO
//! let _ = CooMatrix::from(&csr);
//! let _ = CsrMatrix::from(&coo);
//!
//! // CSC <-> COO
//! let _ = CooMatrix::from(&csc);
//! let _ = CscMatrix::from(&coo);
//! ```
//!
//! The routines available here are able to provide more specialized APIs, giving
//! more control over the conversion process. The routines are organized by backends.
//! Currently, only the [`serial`] backend is available.
//! In the future, backends that offer parallel routines may become available.
pub mod serial;

32
nalgebra-sparse/src/convert/serial.rs
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@ -1,4 +1,8 @@
//! TODO
//! Serial routines for converting between matrix formats.
//!
//! All routines in this module are single-threaded. At present these routines offer no
//! advantage over using the [`From`] trait, but future changes to the API might offer more
//! control to the user.
use std::ops::Add;
use num_traits::Zero;
@ -11,7 +15,7 @@ use crate::cs;
use crate::csc::CscMatrix;
use crate::csr::CsrMatrix;
/// TODO
/// Converts a dense matrix to [`CooMatrix`].
pub fn convert_dense_coo<T, R, C, S>(dense: &Matrix<T, R, C, S>) -> CooMatrix<T>
where
T: Scalar + Zero,
@ -33,9 +37,7 @@ where
coo
}
/// TODO
///
/// TODO: What should the actual trait bounds be?
/// Converts a [`CooMatrix`] to a dense matrix.
pub fn convert_coo_dense<T>(coo: &CooMatrix<T>) -> DMatrix<T>
where
T: Scalar + Zero + ClosedAdd,
@ -47,7 +49,7 @@ where
output
}
/// TODO
/// Converts a [`CooMatrix`] to a [`CsrMatrix`].
pub fn convert_coo_csr<T>(coo: &CooMatrix<T>) -> CsrMatrix<T>
where
T: Scalar + Zero
@ -63,7 +65,7 @@ where
.expect("Internal error: Invalid CSR data during COO->CSR conversion")
}
/// TODO
/// Converts a [`CsrMatrix`] to a [`CooMatrix`].
pub fn convert_csr_coo<T: Scalar>(csr: &CsrMatrix<T>) -> CooMatrix<T>
{
let mut result = CooMatrix::new(csr.nrows(), csr.ncols());
@ -73,7 +75,7 @@ pub fn convert_csr_coo<T: Scalar>(csr: &CsrMatrix<T>) -> CooMatrix<T>
result
}
/// TODO
/// Converts a [`CsrMatrix`] to a dense matrix.
pub fn convert_csr_dense<T>(csr:& CsrMatrix<T>) -> DMatrix<T>
where
T: Scalar + ClosedAdd + Zero
@ -87,7 +89,7 @@ where
output
}
/// TODO
/// Converts a dense matrix to a [`CsrMatrix`].
pub fn convert_dense_csr<T, R, C, S>(dense: &Matrix<T, R, C, S>) -> CsrMatrix<T>
where
T: Scalar + Zero,
@ -120,7 +122,7 @@ where
.expect("Internal error: Invalid CsrMatrix format during dense-> CSR conversion")
}
/// TODO
/// Converts a [`CooMatrix`] to a [`CscMatrix`].
pub fn convert_coo_csc<T>(coo: &CooMatrix<T>) -> CscMatrix<T>
where
T: Scalar + Zero
@ -136,7 +138,7 @@ where
.expect("Internal error: Invalid CSC data during COO->CSC conversion")
}
/// TODO
/// Converts a [`CscMatrix`] to a [`CooMatrix`].
pub fn convert_csc_coo<T>(csc: &CscMatrix<T>) -> CooMatrix<T>
where
T: Scalar
@ -148,7 +150,7 @@ where
coo
}
/// TODO
/// Converts a [`CscMatrix`] to a dense matrix.
pub fn convert_csc_dense<T>(csc: &CscMatrix<T>) -> DMatrix<T>
where
T: Scalar + ClosedAdd + Zero
@ -162,7 +164,7 @@ where
output
}
/// TODO
/// Converts a dense matrix to a [`CscMatrix`].
pub fn convert_dense_csc<T, R, C, S>(dense: &Matrix<T, R, C, S>) -> CscMatrix<T>
where
T: Scalar + Zero,
@ -192,7 +194,7 @@ pub fn convert_dense_csc<T, R, C, S>(dense: &Matrix<T, R, C, S>) -> CscMatrix<T>
.expect("Internal error: Invalid CscMatrix format during dense-> CSC conversion")
}
/// TODO
/// Converts a [`CsrMatrix`] to a [`CscMatrix`].
pub fn convert_csr_csc<T>(csr: &CsrMatrix<T>) -> CscMatrix<T>
where
T: Scalar
@ -208,7 +210,7 @@ where
.expect("Internal error: Invalid CSC data during CSR->CSC conversion")
}
/// TODO
/// Converts a [`CscMatrix`] to a [`CsrMatrix`].
pub fn convert_csc_csr<T>(csc: &CscMatrix<T>) -> CsrMatrix<T>
where
T: Scalar

97
nalgebra-sparse/src/factorization/cholesky.rs
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@ -1,6 +1,3 @@
// TODO: Remove this allowance
#![allow(missing_docs)]
use crate::pattern::SparsityPattern;
use crate::csc::CscMatrix;
use core::{mem, iter};
@ -9,6 +6,10 @@ use std::fmt::{Display, Formatter};
use crate::ops::serial::spsolve_csc_lower_triangular;
use crate::ops::Op;
/// A symbolic sparse Cholesky factorization of a CSC matrix.
///
/// The symbolic factorization computes the sparsity pattern of `L`, the Cholesky factor.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct CscSymbolicCholesky {
// Pattern of the original matrix that was decomposed
m_pattern: SparsityPattern,
@ -18,6 +19,14 @@ pub struct CscSymbolicCholesky {
}
impl CscSymbolicCholesky {
/// Compute the symbolic factorization for a sparsity pattern belonging to a CSC matrix.
///
/// The sparsity pattern must be symmetric. However, this is not enforced, and it is the
/// responsibility of the user to ensure that this property holds.
///
/// # Panics
///
/// Panics if the sparsity pattern is not square.
pub fn factor(pattern: SparsityPattern) -> Self {
assert_eq!(pattern.major_dim(), pattern.minor_dim(),
"Major and minor dimensions must be the same (square matrix).");
@ -29,11 +38,27 @@ impl CscSymbolicCholesky {
}
}
/// The pattern of the Cholesky factor `L`.
pub fn l_pattern(&self) -> &SparsityPattern {
&self.l_pattern
}
}
/// A sparse Cholesky factorization `A = L L^T` of a [`CscMatrix`].
///
/// The factor `L` is a sparse, lower-triangular matrix. See the article on [Wikipedia] for
/// more information.
///
/// The implementation is a port of the `CsCholesky` implementation in `nalgebra`. It is similar
/// to Tim Davis' [`CSparse`]. The current implementation performs no fill-in reduction, and can
/// therefore be expected to produce much too dense Cholesky factors for many matrices.
/// It is therefore not currently recommended to use this implementation for serious projects.
///
/// [`CSparse`]: https://epubs.siam.org/doi/book/10.1137/1.9780898718881
/// [Wikipedia]: https://en.wikipedia.org/wiki/Cholesky_decomposition
// TODO: We should probably implement PartialEq/Eq, but in that case we'd probably need a
// custom implementation, due to the need to exclude the workspace arrays
#[derive(Debug, Clone)]
pub struct CscCholesky<T> {
// Pattern of the original matrix
m_pattern: SparsityPattern,
@ -45,6 +70,7 @@ pub struct CscCholesky<T> {
#[derive(Debug, PartialEq, Eq, Clone)]
#[non_exhaustive]
/// Possible errors produced by the Cholesky factorization.
pub enum CholeskyError {
/// The matrix is not positive definite.
NotPositiveDefinite,
@ -59,7 +85,24 @@ impl Display for CholeskyError {
impl std::error::Error for CholeskyError {}
impl<T: RealField> CscCholesky<T> {
pub fn factor_numerical(symbolic: CscSymbolicCholesky, values: &[T]) -> Result<Self, CholeskyError> {
/// Computes the numerical Cholesky factorization associated with the given
/// symbolic factorization and the provided values.
///
/// The values correspond to the non-zero values of the CSC matrix for which the
/// symbolic factorization was computed.
///
/// # Errors
///
/// Returns an error if the numerical factorization fails. This can occur if the matrix is not
/// symmetric positive definite.
///
/// # Panics
///
/// Panics if the number of values differ from the number of non-zeros of the sparsity pattern
/// of the matrix that was symbolically factored.
pub fn factor_numerical(symbolic: CscSymbolicCholesky, values: &[T])
-> Result<Self, CholeskyError>
{
assert_eq!(symbolic.l_pattern.nnz(), symbolic.u_pattern.nnz(),
"u is just the transpose of l, so should have the same nnz");
@ -84,19 +127,50 @@ impl<T: RealField> CscCholesky<T> {
Ok(factorization)
}
/// Computes the Cholesky factorization of the provided matrix.
///
/// The matrix must be symmetric positive definite. Symmetry is not checked, and it is up
/// to the user to enforce this property.
///
/// # Errors
///
/// Returns an error if the numerical factorization fails. This can occur if the matrix is not
/// symmetric positive definite.
///
/// # Panics
///
/// Panics if the matrix is not square.
///
/// TODO: Take matrix by value or not?
pub fn factor(matrix: &CscMatrix<T>) -> Result<Self, CholeskyError> {
let symbolic = CscSymbolicCholesky::factor(matrix.pattern().clone());
Self::factor_numerical(symbolic, matrix.values())
}
/// Re-computes the factorization for a new set of non-zero values.
///
/// This is useful when the values of a matrix changes, but the sparsity pattern remains
/// constant.
///
/// # Errors
///
/// Returns an error if the numerical factorization fails. This can occur if the matrix is not
/// symmetric positive definite.
///
/// # Panics
///
/// Panics if the number of values does not match the number of non-zeros in the sparsity
/// pattern.
pub fn refactor(&mut self, values: &[T]) -> Result<(), CholeskyError> {
self.decompose_left_looking(values)
}
/// Returns a reference to the Cholesky factor `L`.
pub fn l(&self) -> &CscMatrix<T> {
&self.l_factor
}
/// Returns the Cholesky factor `L`.
pub fn take_l(self) -> CscMatrix<T> {
self.l_factor
}
@ -178,6 +252,11 @@ impl<T: RealField> CscCholesky<T> {
Ok(())
}
/// Solves the system `A X = B`, where `X` and `B` are dense matrices.
///
/// # Panics
///
/// Panics if `B` is not square.
pub fn solve<'a>(&'a self, b: impl Into<DMatrixSlice<'a, T>>) -> DMatrix<T> {
let b = b.into();
let mut output = b.clone_owned();
@ -185,6 +264,13 @@ impl<T: RealField> CscCholesky<T> {
output
}
/// Solves the system `AX = B`, where `X` and `B` are dense matrices.
///
/// The result is stored in-place in `b`.
///
/// # Panics
///
/// Panics if `b` is not square.
pub fn solve_mut<'a>(&'a self, b: impl Into<DMatrixSliceMut<'a, T>>)
{
let expect_msg = "If the Cholesky factorization succeeded,\
@ -201,9 +287,6 @@ impl<T: RealField> CscCholesky<T> {
}
}
fn reach(
pattern: &SparsityPattern,
j: usize,

2
nalgebra-sparse/src/factorization/mod.rs
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@ -1,4 +1,6 @@
//! Matrix factorization for sparse matrices.
//!
//! Currently, the only factorization provided here is the [`CscCholesky`] factorization.
mod cholesky;
pub use cholesky::*;

33
nalgebra-sparse/src/lib.rs
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@ -158,17 +158,26 @@ impl fmt::Display for SparseFormatError {
impl Error for SparseFormatError {}
/// TODO
/// An entry in a sparse matrix.
///
/// Sparse matrices do not store all their entries explicitly. Therefore, entry (i, j) in the matrix
/// can either be a reference to an explicitly stored element, or it is implicitly zero.
#[derive(Debug, PartialEq, Eq)]
pub enum SparseEntry<'a, T> {
/// TODO
/// The entry is a reference to an explicitly stored element.
///
/// Note that the naming here is a misnomer: The element can still be zero, even though it
/// is explicitly stored (a so-called "explicit zero").
NonZero(&'a T),
/// TODO
/// The entry is implicitly zero, i.e. it is not explicitly stored.
Zero
}
impl<'a, T: Clone + Zero> SparseEntry<'a, T> {
/// TODO
/// Returns the value represented by this entry.
///
/// Either clones the underlying reference or returns zero if the entry is not explicitly
/// stored.
pub fn to_value(self) -> T {
match self {
SparseEntry::NonZero(value) => value.clone(),
@ -177,17 +186,25 @@ impl<'a, T: Clone + Zero> SparseEntry<'a, T> {
}
}
/// TODO
/// A mutable entry in a sparse matrix.
///
/// See also `SparseEntry`.
#[derive(Debug, PartialEq, Eq)]
pub enum SparseEntryMut<'a, T> {
/// TODO
/// The entry is a mutable reference to an explicitly stored element.
///
/// Note that the naming here is a misnomer: The element can still be zero, even though it
/// is explicitly stored (a so-called "explicit zero").
NonZero(&'a mut T),
/// TODO
/// The entry is implicitly zero i.e. it is not explicitly stored.
Zero
}
impl<'a, T: Clone + Zero> SparseEntryMut<'a, T> {
/// TODO
/// Returns the value represented by this entry.
///
/// Either clones the underlying reference or returns zero if the entry is not explicitly
/// stored.
pub fn to_value(self) -> T {
match self {
SparseEntryMut::NonZero(value) => value.clone(),

32
nalgebra-sparse/src/ops/mod.rs
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@ -1,24 +1,37 @@
//! TODO
//! Sparse matrix arithmetic operations.
//!
//! TODO: Explain that users should prefer to use std ops unless they need to get more performance
//!
//! The available operations are organized by backend. Currently, only the [`serial`] backend
//! is available. In the future, backends that expose parallel operations may become available.
//!
//! Many routines are able to implicitly transpose matrices involved in the operation.
//! For example, the routine [`spadd_csr_prealloc`](serial::spadd_csr_prealloc) performs the
//! operation `C <- beta * C + alpha * op(A)`. Here `op(A)` indicates that the matrix `A` can
//! either be used as-is or transposed. The notation `op(A)` is represented in code by the
//! [`Op`] enum.
mod impl_std_ops;
pub mod serial;
/// TODO
/// Determines whether a matrix should be transposed in a given operation.
///
/// See the [module-level documentation](crate::ops) for the purpose of this enum.
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
pub enum Op<T> {
/// TODO
/// Indicates that the matrix should be used as-is.
NoOp(T),
/// TODO
/// Indicates that the matrix should be transposed.
Transpose(T),
}
impl<T> Op<T> {
/// TODO
/// Returns a reference to the inner value that the operation applies to.
pub fn inner_ref(&self) -> &T {
self.as_ref().into_inner()
}
/// TODO
/// Returns an `Op` applied to a reference of the inner value.
pub fn as_ref(&self) -> Op<&T> {
match self {
Op::NoOp(obj) => Op::NoOp(&obj),
@ -26,15 +39,14 @@ impl<T> Op<T> {
}
}
/// TODO
/// Converts the underlying data type.
pub fn convert<U>(self) -> Op<U>
where T: Into<U>
{
self.map_same_op(T::into)
}
/// TODO
/// TODO: Rewrite the other functions by leveraging this one
/// Transforms the inner value with the provided function, but preserves the operation.
pub fn map_same_op<U, F: FnOnce(T) -> U>(self, f: F) -> Op<U> {
match self {
Op::NoOp(obj) => Op::NoOp(f(obj)),
@ -42,7 +54,7 @@ impl<T> Op<T> {
}
}
/// TODO
/// Consumes the `Op` and returns the inner value.
pub fn into_inner(self) -> T {
match self {
Op::NoOp(obj) | Op::Transpose(obj) => obj,

10
nalgebra-sparse/src/ops/serial/cs.rs
View File

@ -1,13 +1,13 @@
use crate::cs::CsMatrix;
use crate::ops::Op;
use crate::ops::serial::{OperationErrorType, OperationError};
use crate::ops::serial::{OperationErrorKind, OperationError};
use nalgebra::{Scalar, ClosedAdd, ClosedMul, DMatrixSliceMut, DMatrixSlice};
use num_traits::{Zero, One};
use crate::SparseEntryMut;
fn spmm_cs_unexpected_entry() -> OperationError {
OperationError::from_type_and_message(
OperationErrorType::InvalidPattern,
OperationError::from_kind_and_message(
OperationErrorKind::InvalidPattern,
String::from("Found unexpected entry that is not present in `c`."))
}
@ -58,8 +58,8 @@ pub fn spmm_cs_prealloc<T>(
}
fn spadd_cs_unexpected_entry() -> OperationError {
OperationError::from_type_and_message(
OperationErrorType::InvalidPattern,
OperationError::from_kind_and_message(
OperationErrorKind::InvalidPattern,
String::from("Found entry in `op(a)` that is not present in `c`."))
}

34
nalgebra-sparse/src/ops/serial/mod.rs
View File

@ -1,4 +1,12 @@
//! TODO
//! Serial sparse matrix arithmetic routines.
//!
//! All routines are single-threaded.
//!
//! Some operations have the `prealloc` suffix. This means that they expect that the sparsity
//! pattern of the output matrix has already been pre-allocated: that is, the pattern of the result
//! of the operation fits entirely in the output pattern. In the future, there will also be
//! some operations which will be able to dynamically adapt the output pattern to fit the
//! result, but these have yet to be implemented.
#[macro_use]
macro_rules! assert_compatible_spmm_dims {
@ -58,24 +66,32 @@ pub use csc::*;
pub use csr::*;
pub use pattern::*;
/// TODO
/// A description of the error that occurred during an arithmetic operation.
#[derive(Clone, Debug)]
pub struct OperationError {
error_type: OperationErrorType,
error_kind: OperationErrorKind,
message: String
}
/// TODO
/// The different kinds of operation errors that may occur.
#[non_exhaustive]
#[derive(Clone, Debug)]
pub enum OperationErrorType {
/// TODO
pub enum OperationErrorKind {
/// Indicates that one or more sparsity patterns involved in the operation violate the
/// expectations of the routine.
///
/// For example, this could indicate that the sparsity pattern of the output is not able to
/// contain the result of the operation.
InvalidPattern,
}
impl OperationError {
/// TODO
pub fn from_type_and_message(error_type: OperationErrorType, message: String) -> Self {
Self { error_type, message }
fn from_kind_and_message(error_type: OperationErrorKind, message: String) -> Self {
Self { error_kind: error_type, message }
}
/// The operation error kind.
pub fn kind(&self) -> &OperationErrorKind {
&self.error_kind
}
}

34
nalgebra-sparse/src/proptest.rs
View File

@ -1,6 +1,10 @@
//! TODO
//! Functionality for integrating `nalgebra-sparse` with `proptest`.
//!
//! TODO: Clarify that this module needs proptest-support feature
//! **This module is only available if the `proptest-support` feature is enabled**.
//!
//! The strategies provided here are generally expected to be able to generate the entire range
//! of possible outputs given the constraints on dimensions and values. However, there are no
//! particular guarantees on the distribution of possible values.
// Contains some patched code from proptest that we can remove in the (hopefully near) future.
// See docs in file for more details.
@ -139,7 +143,12 @@ fn sparse_triplet_strategy<T>(value_strategy: T,
.prop_shuffle()
}
/// TODO
/// A strategy for producing COO matrices without duplicate entries.
///
/// The values of the matrix are picked from the provided `value_strategy`, while the size of the
/// generated matrices is determined by the ranges `rows` and `cols`. The number of explicitly
/// stored entries is bounded from above by `max_nonzeros`. Note that the matrix might still
/// contain explicitly stored zeroes if the value strategy is capable of generating zero values.
pub fn coo_no_duplicates<T>(
value_strategy: T,
rows: impl Into<DimRange>,
@ -177,10 +186,17 @@ where
})
}
/// TODO
/// A strategy for producing COO matrices with duplicate entries.
///
/// TODO: Write note on how this strategy only maintains the constraints on values
/// for each triplet, but does not consider the sum of triplets
/// The values of the matrix are picked from the provided `value_strategy`, while the size of the
/// generated matrices is determined by the ranges `rows` and `cols`. Note that the values
/// only apply to individual entries, and since this strategy can generate duplicate entries,
/// the matrix will generally have values outside the range determined by `value_strategy` when
/// converted to other formats, since the duplicate entries are summed together in this case.
///
/// The number of explicitly stored entries is bounded from above by `max_nonzeros`. The maximum
/// number of duplicate entries is determined by `max_duplicates`. Note that the matrix might still
/// contain explicitly stored zeroes if the value strategy is capable of generating zero values.
pub fn coo_with_duplicates<T>(
value_strategy: T,
rows: impl Into<DimRange>,
@ -255,7 +271,7 @@ where
.expect("Internal error: Generated sparsity pattern is invalid")
}
/// TODO
/// A strategy for generating sparsity patterns.
pub fn sparsity_pattern(
major_lanes: impl Into<DimRange>,
minor_lanes: impl Into<DimRange>,
@ -286,7 +302,7 @@ pub fn sparsity_pattern(
})
}
/// TODO
/// A strategy for generating CSR matrices.
pub fn csr<T>(value_strategy: T,
rows: impl Into<DimRange>,
cols: impl Into<DimRange>,
@ -310,7 +326,7 @@ where
})
}
/// TODO
/// A strategy for generating CSC matrices.
pub fn csc<T>(value_strategy: T,
rows: impl Into<DimRange>,
cols: impl Into<DimRange>,