Files
ceres-solver/internal/ceres/implicit_schur_complement_test.cc
T
Sameer Agarwal 81219fff78 Allow using Eigen's LDLT factorization instead of LLT factorization
It seems that Eigen's LLT factorization is broken on ARM.
This patch enables the use of LDLT factorization instead of LLT
factorization. The switch is controlled at compile time using a
preprocessor define - CERES_USE_EIGEN_LDLT.

By default we continue to use LLT factorization though.

To make the switching easier without introducing the Cholesky factorization
based inversion and linear system solve routines have been abstracted into
two new functions.

Android.mk has been updated to enable the LDLT factorization, but
the cmake file has not been updated as I will leave it to Alex's
capable hands to do proper detection of ARM as a target platform.

Change-Id: Iffe3abd2ce894de2a388b454df3da909b482d5e5
2015-04-05 22:50:41 -07:00

208 lines
7.6 KiB
C++

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// 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
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/implicit_schur_complement.h"
#include <cstddef>
#include "ceres/block_random_access_dense_matrix.h"
#include "ceres/block_sparse_matrix.h"
#include "ceres/casts.h"
#include "ceres/eigen_dense_cholesky.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/linear_solver.h"
#include "ceres/schur_eliminator.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
using testing::AssertionResult;
const double kEpsilon = 1e-14;
class ImplicitSchurComplementTest : public ::testing::Test {
protected :
virtual void SetUp() {
scoped_ptr<LinearLeastSquaresProblem> problem(
CreateLinearLeastSquaresProblemFromId(2));
CHECK_NOTNULL(problem.get());
A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
b_.reset(problem->b.release());
D_.reset(problem->D.release());
num_cols_ = A_->num_cols();
num_rows_ = A_->num_rows();
num_eliminate_blocks_ = problem->num_eliminate_blocks;
}
void ReducedLinearSystemAndSolution(double* D,
Matrix* lhs,
Vector* rhs,
Vector* solution) {
const CompressedRowBlockStructure* bs = A_->block_structure();
const int num_col_blocks = bs->cols.size();
std::vector<int> blocks(num_col_blocks - num_eliminate_blocks_, 0);
for (int i = num_eliminate_blocks_; i < num_col_blocks; ++i) {
blocks[i - num_eliminate_blocks_] = bs->cols[i].size;
}
BlockRandomAccessDenseMatrix blhs(blocks);
const int num_schur_rows = blhs.num_rows();
LinearSolver::Options options;
options.elimination_groups.push_back(num_eliminate_blocks_);
options.type = DENSE_SCHUR;
scoped_ptr<SchurEliminatorBase> eliminator(
SchurEliminatorBase::Create(options));
CHECK_NOTNULL(eliminator.get());
eliminator->Init(num_eliminate_blocks_, bs);
lhs->resize(num_schur_rows, num_schur_rows);
rhs->resize(num_schur_rows);
eliminator->Eliminate(A_.get(), b_.get(), D, &blhs, rhs->data());
MatrixRef lhs_ref(blhs.mutable_values(), num_schur_rows, num_schur_rows);
// lhs_ref is an upper triangular matrix. Construct a full version
// of lhs_ref in lhs by transposing lhs_ref, choosing the strictly
// lower triangular part of the matrix and adding it to lhs_ref.
*lhs = lhs_ref;
lhs->triangularView<Eigen::StrictlyLower>() =
lhs_ref.triangularView<Eigen::StrictlyUpper>().transpose();
solution->resize(num_cols_);
solution->setZero();
double* schur_solution = solution->data() + num_cols_ - num_schur_rows;
SolveUpperTriangularUsingCholesky(num_schur_rows,
lhs->data(),
rhs->data(),
schur_solution);
eliminator->BackSubstitute(A_.get(),
b_.get(),
D,
schur_solution,
solution->data());
}
AssertionResult TestImplicitSchurComplement(double* D) {
Matrix lhs;
Vector rhs;
Vector reference_solution;
ReducedLinearSystemAndSolution(D, &lhs, &rhs, &reference_solution);
LinearSolver::Options options;
options.elimination_groups.push_back(num_eliminate_blocks_);
options.preconditioner_type = JACOBI;
ImplicitSchurComplement isc(options);
isc.Init(*A_, D, b_.get());
int num_sc_cols = lhs.cols();
for (int i = 0; i < num_sc_cols; ++i) {
Vector x(num_sc_cols);
x.setZero();
x(i) = 1.0;
Vector y(num_sc_cols);
y = lhs * x;
Vector z(num_sc_cols);
isc.RightMultiply(x.data(), z.data());
// The i^th column of the implicit schur complement is the same as
// the explicit schur complement.
if ((y - z).norm() > kEpsilon) {
return testing::AssertionFailure()
<< "Explicit and Implicit SchurComplements differ in "
<< "column " << i << ". explicit: " << y.transpose()
<< " implicit: " << z.transpose();
}
}
// Compare the rhs of the reduced linear system
if ((isc.rhs() - rhs).norm() > kEpsilon) {
return testing::AssertionFailure()
<< "Explicit and Implicit SchurComplements differ in "
<< "rhs. explicit: " << rhs.transpose()
<< " implicit: " << isc.rhs().transpose();
}
// Reference solution to the f_block.
Vector reference_f_sol(rhs.rows());
SolveUpperTriangularUsingCholesky(lhs.rows(),
lhs.data(),
rhs.data(),
reference_f_sol.data());
// Backsubstituted solution from the implicit schur solver using the
// reference solution to the f_block.
Vector sol(num_cols_);
isc.BackSubstitute(reference_f_sol.data(), sol.data());
if ((sol - reference_solution).norm() > kEpsilon) {
return testing::AssertionFailure()
<< "Explicit and Implicit SchurComplements solutions differ. "
<< "explicit: " << reference_solution.transpose()
<< " implicit: " << sol.transpose();
}
return testing::AssertionSuccess();
}
int num_rows_;
int num_cols_;
int num_eliminate_blocks_;
scoped_ptr<BlockSparseMatrix> A_;
scoped_array<double> b_;
scoped_array<double> D_;
};
// Verify that the Schur Complement matrix implied by the
// ImplicitSchurComplement class matches the one explicitly computed
// by the SchurComplement solver.
//
// We do this with and without regularization to check that the
// support for the LM diagonal is correct.
TEST_F(ImplicitSchurComplementTest, SchurMatrixValuesTest) {
EXPECT_TRUE(TestImplicitSchurComplement(NULL));
EXPECT_TRUE(TestImplicitSchurComplement(D_.get()));
}
} // namespace internal
} // namespace ceres