Template specializations for PartitionedMatrixView.
This speeds up the matrix vector products in the
IterativeSchurSolver by upto 40%.
Change-Id: Ib5e8d77c7269cf5ffdd2d161893734bb6d38215d
diff --git a/internal/ceres/partitioned_matrix_view_test.cc b/internal/ceres/partitioned_matrix_view_test.cc
index 48f7d24..ef5dae9 100644
--- a/internal/ceres/partitioned_matrix_view_test.cc
+++ b/internal/ceres/partitioned_matrix_view_test.cc
@@ -49,6 +49,7 @@
class PartitionedMatrixViewTest : public ::testing::Test {
protected :
virtual void SetUp() {
+ srand(5);
scoped_ptr<LinearLeastSquaresProblem> problem(
CreateLinearLeastSquaresProblemFromId(2));
CHECK_NOTNULL(problem.get());
@@ -57,108 +58,93 @@
num_cols_ = A_->num_cols();
num_rows_ = A_->num_rows();
num_eliminate_blocks_ = problem->num_eliminate_blocks;
+ LinearSolver::Options options;
+ options.elimination_groups.push_back(num_eliminate_blocks_);
+ pmv_.reset(PartitionedMatrixViewBase::Create(
+ options,
+ *down_cast<BlockSparseMatrix*>(A_.get())));
}
int num_rows_;
int num_cols_;
int num_eliminate_blocks_;
-
scoped_ptr<SparseMatrix> A_;
+ scoped_ptr<PartitionedMatrixViewBase> pmv_;
};
TEST_F(PartitionedMatrixViewTest, DimensionsTest) {
- PartitionedMatrixView m(*down_cast<BlockSparseMatrix*>(A_.get()),
- num_eliminate_blocks_);
- EXPECT_EQ(m.num_col_blocks_e(), num_eliminate_blocks_);
- EXPECT_EQ(m.num_col_blocks_f(), num_cols_ - num_eliminate_blocks_);
- EXPECT_EQ(m.num_cols_e(), num_eliminate_blocks_);
- EXPECT_EQ(m.num_cols_f(), num_cols_ - num_eliminate_blocks_);
- EXPECT_EQ(m.num_cols(), A_->num_cols());
- EXPECT_EQ(m.num_rows(), A_->num_rows());
+ EXPECT_EQ(pmv_->num_col_blocks_e(), num_eliminate_blocks_);
+ EXPECT_EQ(pmv_->num_col_blocks_f(), num_cols_ - num_eliminate_blocks_);
+ EXPECT_EQ(pmv_->num_cols_e(), num_eliminate_blocks_);
+ EXPECT_EQ(pmv_->num_cols_f(), num_cols_ - num_eliminate_blocks_);
+ EXPECT_EQ(pmv_->num_cols(), A_->num_cols());
+ EXPECT_EQ(pmv_->num_rows(), A_->num_rows());
}
TEST_F(PartitionedMatrixViewTest, RightMultiplyE) {
- PartitionedMatrixView m(*down_cast<BlockSparseMatrix*>(A_.get()),
- num_eliminate_blocks_);
-
- srand(5);
-
- Vector x1(m.num_cols_e());
- Vector x2(m.num_cols());
+ Vector x1(pmv_->num_cols_e());
+ Vector x2(pmv_->num_cols());
x2.setZero();
- for (int i = 0; i < m.num_cols_e(); ++i) {
+ for (int i = 0; i < pmv_->num_cols_e(); ++i) {
x1(i) = x2(i) = RandDouble();
}
- Vector y1 = Vector::Zero(m.num_rows());
- m.RightMultiplyE(x1.data(), y1.data());
+ Vector y1 = Vector::Zero(pmv_->num_rows());
+ pmv_->RightMultiplyE(x1.data(), y1.data());
- Vector y2 = Vector::Zero(m.num_rows());
+ Vector y2 = Vector::Zero(pmv_->num_rows());
A_->RightMultiply(x2.data(), y2.data());
- for (int i = 0; i < m.num_rows(); ++i) {
+ for (int i = 0; i < pmv_->num_rows(); ++i) {
EXPECT_NEAR(y1(i), y2(i), kEpsilon);
}
}
TEST_F(PartitionedMatrixViewTest, RightMultiplyF) {
- PartitionedMatrixView m(*down_cast<BlockSparseMatrix*>(A_.get()),
- num_eliminate_blocks_);
+ Vector x1(pmv_->num_cols_f());
+ Vector x2 = Vector::Zero(pmv_->num_cols());
- srand(5);
-
- Vector x1(m.num_cols_f());
- Vector x2 = Vector::Zero(m.num_cols());
-
- for (int i = 0; i < m.num_cols_f(); ++i) {
+ for (int i = 0; i < pmv_->num_cols_f(); ++i) {
x1(i) = RandDouble();
- x2(i + m.num_cols_e()) = x1(i);
+ x2(i + pmv_->num_cols_e()) = x1(i);
}
- Vector y1 = Vector::Zero(m.num_rows());
- m.RightMultiplyF(x1.data(), y1.data());
+ Vector y1 = Vector::Zero(pmv_->num_rows());
+ pmv_->RightMultiplyF(x1.data(), y1.data());
- Vector y2 = Vector::Zero(m.num_rows());
+ Vector y2 = Vector::Zero(pmv_->num_rows());
A_->RightMultiply(x2.data(), y2.data());
- for (int i = 0; i < m.num_rows(); ++i) {
+ for (int i = 0; i < pmv_->num_rows(); ++i) {
EXPECT_NEAR(y1(i), y2(i), kEpsilon);
}
}
TEST_F(PartitionedMatrixViewTest, LeftMultiply) {
- PartitionedMatrixView m(*down_cast<BlockSparseMatrix*>(A_.get()),
- num_eliminate_blocks_);
-
- srand(5);
-
- Vector x = Vector::Zero(m.num_rows());
- for (int i = 0; i < m.num_rows(); ++i) {
+ Vector x = Vector::Zero(pmv_->num_rows());
+ for (int i = 0; i < pmv_->num_rows(); ++i) {
x(i) = RandDouble();
}
- Vector y = Vector::Zero(m.num_cols());
- Vector y1 = Vector::Zero(m.num_cols_e());
- Vector y2 = Vector::Zero(m.num_cols_f());
+ Vector y = Vector::Zero(pmv_->num_cols());
+ Vector y1 = Vector::Zero(pmv_->num_cols_e());
+ Vector y2 = Vector::Zero(pmv_->num_cols_f());
A_->LeftMultiply(x.data(), y.data());
- m.LeftMultiplyE(x.data(), y1.data());
- m.LeftMultiplyF(x.data(), y2.data());
+ pmv_->LeftMultiplyE(x.data(), y1.data());
+ pmv_->LeftMultiplyF(x.data(), y2.data());
- for (int i = 0; i < m.num_cols(); ++i) {
+ for (int i = 0; i < pmv_->num_cols(); ++i) {
EXPECT_NEAR(y(i),
- (i < m.num_cols_e()) ? y1(i) : y2(i - m.num_cols_e()),
+ (i < pmv_->num_cols_e()) ? y1(i) : y2(i - pmv_->num_cols_e()),
kEpsilon);
}
}
TEST_F(PartitionedMatrixViewTest, BlockDiagonalEtE) {
- PartitionedMatrixView m(*down_cast<BlockSparseMatrix*>(A_.get()),
- num_eliminate_blocks_);
-
scoped_ptr<BlockSparseMatrix>
- block_diagonal_ee(m.CreateBlockDiagonalEtE());
+ block_diagonal_ee(pmv_->CreateBlockDiagonalEtE());
const CompressedRowBlockStructure* bs = block_diagonal_ee->block_structure();
EXPECT_EQ(block_diagonal_ee->num_rows(), 2);
@@ -171,11 +157,8 @@
}
TEST_F(PartitionedMatrixViewTest, BlockDiagonalFtF) {
- PartitionedMatrixView m(*down_cast<BlockSparseMatrix*>(A_.get()),
- num_eliminate_blocks_);
-
scoped_ptr<BlockSparseMatrix>
- block_diagonal_ff(m.CreateBlockDiagonalFtF());
+ block_diagonal_ff(pmv_->CreateBlockDiagonalFtF());
const CompressedRowBlockStructure* bs = block_diagonal_ff->block_structure();
EXPECT_EQ(block_diagonal_ff->num_rows(), 3);
