Initial commit of Ceres Solver.
diff --git a/internal/ceres/partitioned_matrix_view_test.cc b/internal/ceres/partitioned_matrix_view_test.cc
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+// used to endorse or promote products derived from this software without
+// specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+
+#include "ceres/partitioned_matrix_view.h"
+
+#include <vector>
+#include <glog/logging.h>
+#include "gtest/gtest.h"
+#include "ceres/block_structure.h"
+#include "ceres/casts.h"
+#include "ceres/linear_least_squares_problems.h"
+#include "ceres/random.h"
+#include "ceres/sparse_matrix.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/internal/scoped_ptr.h"
+
+namespace ceres {
+namespace internal {
+
+const double kEpsilon = 1e-14;
+
+class PartitionedMatrixViewTest : public ::testing::Test {
+ protected :
+ virtual void SetUp() {
+ scoped_ptr<LinearLeastSquaresProblem> problem(
+ CreateLinearLeastSquaresProblemFromId(2));
+ CHECK_NOTNULL(problem.get());
+ A_.reset(problem->A.release());
+
+ num_cols_ = A_->num_cols();
+ num_rows_ = A_->num_rows();
+ num_eliminate_blocks_ = problem->num_eliminate_blocks;
+ }
+
+ int num_rows_;
+ int num_cols_;
+ int num_eliminate_blocks_;
+
+ scoped_ptr<SparseMatrix> A_;
+};
+
+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());
+}
+
+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());
+ x2.setZero();
+
+ for (int i = 0; i < m.num_cols_e(); ++i) {
+ x1(i) = x2(i) = RandDouble();
+ }
+
+ Vector y1 = Vector::Zero(m.num_rows());
+ m.RightMultiplyE(x1.data(), y1.data());
+
+ Vector y2 = Vector::Zero(m.num_rows());
+ A_->RightMultiply(x2.data(), y2.data());
+
+ for (int i = 0; i < m.num_rows(); ++i) {
+ EXPECT_NEAR(y1(i), y2(i), kEpsilon);
+ }
+}
+
+TEST_F(PartitionedMatrixViewTest, RightMultiplyF) {
+ PartitionedMatrixView m(*down_cast<BlockSparseMatrix*>(A_.get()),
+ num_eliminate_blocks_);
+
+ 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) {
+ x1(i) = RandDouble();
+ x2(i + m.num_cols_e()) = x1(i);
+ }
+
+ Vector y1 = Vector::Zero(m.num_rows());
+ m.RightMultiplyF(x1.data(), y1.data());
+
+ Vector y2 = Vector::Zero(m.num_rows());
+ A_->RightMultiply(x2.data(), y2.data());
+
+ for (int i = 0; i < m.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) {
+ 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());
+
+ A_->LeftMultiply(x.data(), y.data());
+ m.LeftMultiplyE(x.data(), y1.data());
+ m.LeftMultiplyF(x.data(), y2.data());
+
+ for (int i = 0; i < m.num_cols(); ++i) {
+ EXPECT_NEAR(y(i),
+ (i < m.num_cols_e()) ? y1(i) : y2(i - m.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());
+ const CompressedRowBlockStructure* bs = block_diagonal_ee->block_structure();
+
+ EXPECT_EQ(block_diagonal_ee->num_rows(), 2);
+ EXPECT_EQ(block_diagonal_ee->num_cols(), 2);
+ EXPECT_EQ(bs->cols.size(), 2);
+ EXPECT_EQ(bs->rows.size(), 2);
+
+ EXPECT_NEAR(block_diagonal_ee->values()[0], 10.0, kEpsilon);
+ EXPECT_NEAR(block_diagonal_ee->values()[1], 155.0, kEpsilon);
+}
+
+TEST_F(PartitionedMatrixViewTest, BlockDiagonalFtF) {
+ PartitionedMatrixView m(*down_cast<BlockSparseMatrix*>(A_.get()),
+ num_eliminate_blocks_);
+
+ scoped_ptr<BlockSparseMatrix>
+ block_diagonal_ff(m.CreateBlockDiagonalFtF());
+ const CompressedRowBlockStructure* bs = block_diagonal_ff->block_structure();
+
+ EXPECT_EQ(block_diagonal_ff->num_rows(), 3);
+ EXPECT_EQ(block_diagonal_ff->num_cols(), 3);
+ EXPECT_EQ(bs->cols.size(), 3);
+ EXPECT_EQ(bs->rows.size(), 3);
+ EXPECT_NEAR(block_diagonal_ff->values()[0], 70.0, kEpsilon);
+ EXPECT_NEAR(block_diagonal_ff->values()[1], 17.0, kEpsilon);
+ EXPECT_NEAR(block_diagonal_ff->values()[2], 37.0, kEpsilon);
+}
+
+} // namespace internal
+} // namespace ceres
