1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. Eigen itself is part of the KDE project.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla
7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed
8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "sparse.h"
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename SparseMatrixType> void sparse_product(const SparseMatrixType& ref)
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  const int rows = ref.rows();
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  const int cols = ref.cols();
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename SparseMatrixType::Scalar Scalar;
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  enum { Flags = SparseMatrixType::Flags };
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  double density = std::max(8./(rows*cols), 0.01);
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Dynamic,1> DenseVector;
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test matrix-matrix product
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refMat3 = DenseMatrix::Zero(rows, rows);
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refMat4 = DenseMatrix::Zero(rows, rows);
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix dm4 = DenseMatrix::Zero(rows, rows);
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrixType m2(rows, rows);
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrixType m3(rows, rows);
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrixType m4(rows, rows);
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat2, m2);
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat3, m3);
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat4, m4);
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3);
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m4=m2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m4=m2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m4=m2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // sparse * dense
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3);
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(dm4=m2*refMat3.transpose(), refMat4=refMat2*refMat3.transpose());
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3, refMat4=refMat2.transpose()*refMat3);
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // dense * sparse
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(dm4=refMat2*m3, refMat4=refMat2*refMat3);
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(dm4=refMat2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m3=m3*m3, refMat3=refMat3*refMat3);
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test matrix - diagonal product
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  if(false) // it compiles, but the precision is terrible. probably doesn't matter in this branch....
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DiagonalMatrix<DenseVector> d1(DenseVector::Random(rows));
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrixType m2(rows, rows);
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrixType m3(rows, rows);
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refM2, m2);
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refM3, m3);
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m3=m2*d1, refM3=refM2*d1);
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m3=m2.transpose()*d1, refM3=refM2.transpose()*d1);
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m3=d1*m2, refM3=d1*refM2);
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1 * refM2.transpose());
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test self adjoint products
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix b = DenseMatrix::Random(rows, rows);
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix x = DenseMatrix::Random(rows, rows);
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refX = DenseMatrix::Random(rows, rows);
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refS = DenseMatrix::Zero(rows, rows);
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrixType mUp(rows, rows);
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrixType mLo(rows, rows);
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrixType mS(rows, rows);
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    do {
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    } while (refUp.isZero());
85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    refLo = refUp.transpose().conjugate();
86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    mLo = mUp.transpose().conjugate();
87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    refS = refUp + refLo;
88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    refS.diagonal() *= 0.5;
89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    mS = mUp + mLo;
90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    for (int k=0; k<mS.outerSize(); ++k)
91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        if (it.index() == k)
93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          it.valueRef() *= 0.5;
94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(refS.adjoint(), refS);
96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(mS.transpose().conjugate(), mS);
97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(mS, refS);
98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(x=mUp.template marked<UpperTriangular|SelfAdjoint>()*b, refX=refS*b);
100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(x=mLo.template marked<LowerTriangular|SelfAdjoint>()*b, refX=refS*b);
101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(x=mS.template marked<SelfAdjoint>()*b, refX=refS*b);
102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_sparse_product()
107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for(int i = 0; i < g_repeat; i++) {
109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( sparse_product(SparseMatrix<double>(8, 8)) );
110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( sparse_product(SparseMatrix<std::complex<double> >(16, 16)) );
111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( sparse_product(SparseMatrix<double>(33, 33)) );
112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_3( sparse_product(DynamicSparseMatrix<double>(8, 8)) );
114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
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