Replace spmm_pattern with spmm_{csr/csc}_pattern

nalgebra-sparse/src/ops/impl_std_ops.rs

@@ -2,8 +2,7 @@ use crate::csr::CsrMatrix;
 use crate::csc::CscMatrix;
 
 use std::ops::{Add, Div, DivAssign, Mul, MulAssign, Sub, Neg};
-use crate::ops::serial::{spadd_csr_prealloc, spadd_csc_prealloc, spadd_pattern, spmm_pattern,
+use crate::ops::serial::{spadd_csr_prealloc, spadd_csc_prealloc, spadd_pattern, spmm_csr_pattern, spmm_csr_prealloc, spmm_csc_prealloc, spmm_csc_dense, spmm_csr_dense, spmm_csc_pattern};
-                         spmm_csr_prealloc, spmm_csc_prealloc, spmm_csc_dense, spmm_csr_dense};
 use nalgebra::{ClosedAdd, ClosedMul, ClosedSub, ClosedDiv, Scalar, Matrix, Dim,
                DMatrixSlice, DMatrix, Dynamic};
 use num_traits::{Zero, One};
@@ -106,9 +105,9 @@ macro_rules! impl_spmm {
     }
 }
 
-impl_spmm!(CsrMatrix, spmm_pattern, spmm_csr_prealloc);
+impl_spmm!(CsrMatrix, spmm_csr_pattern, spmm_csr_prealloc);
 // Need to switch order of operations for CSC pattern
-impl_spmm!(CscMatrix, |a, b| spmm_pattern(b, a), spmm_csc_prealloc);
+impl_spmm!(CscMatrix, spmm_csc_pattern, spmm_csc_prealloc);
 
 /// Implements Scalar * Matrix operations for *concrete* scalar types. The reason this is necessary
 /// is that we are not able to implement Mul<Matrix<T>> for all T generically due to orphan rules.

nalgebra-sparse/src/ops/serial/pattern.rs

@@ -36,7 +36,23 @@ pub fn spadd_pattern(a: &SparsityPattern,
 }
 
 /// Sparse matrix multiplication pattern construction, `C <- A * B`.
-pub fn spmm_pattern(a: &SparsityPattern, b: &SparsityPattern) -> SparsityPattern {
+///
+/// Assumes that the sparsity patterns both represent CSC matrices, and the result is also
+/// represented as the sparsity pattern of a CSC matrix.
+pub fn spmm_csc_pattern(a: &SparsityPattern, b: &SparsityPattern) -> SparsityPattern {
+    // Let C = A * B in CSC format. We note that
+    //  C^T = B^T * A^T.
+    // Since the interpretation of a CSC matrix in CSR format represents the transpose of the
+    // matrix in CSR, we can compute C^T in *CSR format* by switching the order of a and b,
+    // which lets us obtain C^T in CSR format. Re-interpreting this as CSC gives us C in CSC format
+    spmm_csr_pattern(b, a)
+}
+
+/// Sparse matrix multiplication pattern construction, `C <- A * B`.
+///
+/// Assumes that the sparsity patterns both represent CSR matrices, and the result is also
+/// represented as the sparsity pattern of a CSR matrix.
+pub fn spmm_csr_pattern(a: &SparsityPattern, b: &SparsityPattern) -> SparsityPattern {
     assert_eq!(a.minor_dim(), b.major_dim(), "a and b must have compatible dimensions");
 
     let mut offsets = Vec::new();

nalgebra-sparse/tests/unit_tests/ops.rs

@@ -1,8 +1,5 @@
 use crate::common::{csc_strategy, csr_strategy, PROPTEST_MATRIX_DIM, PROPTEST_MAX_NNZ, PROPTEST_I32_VALUE_STRATEGY, non_zero_i32_value_strategy, value_strategy};
-use nalgebra_sparse::ops::serial::{spmm_csr_dense, spmm_csc_dense, spadd_pattern, spmm_pattern,
+use nalgebra_sparse::ops::serial::{spmm_csr_dense, spmm_csc_dense, spadd_pattern, spadd_csr_prealloc, spadd_csc_prealloc, spmm_csr_prealloc, spmm_csc_prealloc, spsolve_csc_lower_triangular, spmm_csr_pattern};
-                                   spadd_csr_prealloc, spadd_csc_prealloc,
-                                   spmm_csr_prealloc, spmm_csc_prealloc,
-                                   spsolve_csc_lower_triangular};
 use nalgebra_sparse::ops::{Op};
 use nalgebra_sparse::csr::CsrMatrix;
 use nalgebra_sparse::csc::CscMatrix;
@@ -188,7 +185,7 @@ fn spadd_pattern_strategy() -> impl Strategy<Value=(SparsityPattern, SparsityPat
 }
 
 /// Constructs pairs (a, b) where a and b have compatible dimensions for a matrix product
-fn spmm_pattern_strategy() -> impl Strategy<Value=(SparsityPattern, SparsityPattern)> {
+fn spmm_csr_pattern_strategy() -> impl Strategy<Value=(SparsityPattern, SparsityPattern)> {
     pattern_strategy()
         .prop_flat_map(|a| {
             let b = sparsity_pattern(Just(a.minor_dim()), PROPTEST_MATRIX_DIM, PROPTEST_MAX_NNZ);
@@ -215,11 +212,11 @@ struct SpmmCscArgs<T> {
 }
 
 fn spmm_csr_prealloc_args_strategy() -> impl Strategy<Value=SpmmCsrArgs<i32>> {
-    spmm_pattern_strategy()
+    spmm_csr_pattern_strategy()
         .prop_flat_map(|(a_pattern, b_pattern)| {
             let a_values = vec![PROPTEST_I32_VALUE_STRATEGY; a_pattern.nnz()];
             let b_values = vec![PROPTEST_I32_VALUE_STRATEGY; b_pattern.nnz()];
-            let c_pattern = spmm_pattern(&a_pattern, &b_pattern);
+            let c_pattern = spmm_csr_pattern(&a_pattern, &b_pattern);
             let c_values = vec![PROPTEST_I32_VALUE_STRATEGY; c_pattern.nnz()];
             let a = a_values.prop_map(move |values|
                 CsrMatrix::try_from_pattern_and_values(a_pattern.clone(), values).unwrap());
@@ -479,10 +476,10 @@ proptest! {
     }
 
     #[test]
-    fn spmm_pattern_test((a, b) in spmm_pattern_strategy())
+    fn spmm_csr_pattern_test((a, b) in spmm_csr_pattern_strategy())
     {
         // (a, b) are multiplication-wise dimensionally compatible patterns
-        let c_pattern = spmm_pattern(&a, &b);
+        let c_pattern = spmm_csr_pattern(&a, &b);
 
         // To verify the pattern, we construct CSR matrices with positive integer entries
         // corresponding to a and b, and convert them to dense matrices.