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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 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
// 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/linear_least_squares_problems.h"
#include <cstdio>
#include <memory>
#include <string>
#include <vector>
#include "absl/log/check.h"
#include "absl/log/log.h"
#include "ceres/block_sparse_matrix.h"
#include "ceres/block_structure.h"
#include "ceres/casts.h"
#include "ceres/file.h"
#include "ceres/stringprintf.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
namespace ceres::internal {
std::unique_ptr<LinearLeastSquaresProblem>
CreateLinearLeastSquaresProblemFromId(int id) {
switch (id) {
case 0:
return LinearLeastSquaresProblem0();
case 1:
return LinearLeastSquaresProblem1();
case 2:
return LinearLeastSquaresProblem2();
case 3:
return LinearLeastSquaresProblem3();
case 4:
return LinearLeastSquaresProblem4();
case 5:
return LinearLeastSquaresProblem5();
case 6:
return LinearLeastSquaresProblem6();
default:
LOG(FATAL) << "Unknown problem id requested " << id;
}
return nullptr;
}
/*
A = [1 2]
[3 4]
[6 -10]
b = [ 8
18
-18]
x = [2
3]
D = [1
2]
x_D = [1.78448275;
2.82327586;]
*/
std::unique_ptr<LinearLeastSquaresProblem> LinearLeastSquaresProblem0() {
auto problem = std::make_unique<LinearLeastSquaresProblem>();
auto A = std::make_unique<TripletSparseMatrix>(3, 2, 6);
problem->b = std::make_unique<double[]>(3);
problem->D = std::make_unique<double[]>(2);
problem->x = std::make_unique<double[]>(2);
problem->x_D = std::make_unique<double[]>(2);
int* Ai = A->mutable_rows();
int* Aj = A->mutable_cols();
double* Ax = A->mutable_values();
int counter = 0;
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 2; ++j) {
Ai[counter] = i;
Aj[counter] = j;
++counter;
}
}
Ax[0] = 1.;
Ax[1] = 2.;
Ax[2] = 3.;
Ax[3] = 4.;
Ax[4] = 6;
Ax[5] = -10;
A->set_num_nonzeros(6);
problem->A = std::move(A);
problem->b[0] = 8;
problem->b[1] = 18;
problem->b[2] = -18;
problem->x[0] = 2.0;
problem->x[1] = 3.0;
problem->D[0] = 1;
problem->D[1] = 2;
problem->x_D[0] = 1.78448275;
problem->x_D[1] = 2.82327586;
return problem;
}
/*
A = [1 0 | 2 0 0
3 0 | 0 4 0
0 5 | 0 0 6
0 7 | 8 0 0
0 9 | 1 0 0
0 0 | 1 1 1]
b = [0
1
2
3
4
5]
c = A'* b = [ 3
67
33
9
17]
A'A = [10 0 2 12 0
0 155 65 0 30
2 65 70 1 1
12 0 1 17 1
0 30 1 1 37]
cond(A'A) = 200.36
S = [ 42.3419 -1.4000 -11.5806
-1.4000 2.6000 1.0000
-11.5806 1.0000 31.1935]
r = [ 4.3032
5.4000
4.0323]
S\r = [ 0.2102
2.1367
0.1388]
A\b = [-2.3061
0.3172
0.2102
2.1367
0.1388]
*/
// The following two functions create a TripletSparseMatrix and a
// BlockSparseMatrix version of this problem.
// TripletSparseMatrix version.
std::unique_ptr<LinearLeastSquaresProblem> LinearLeastSquaresProblem1() {
int num_rows = 6;
int num_cols = 5;
auto problem = std::make_unique<LinearLeastSquaresProblem>();
auto A = std::make_unique<TripletSparseMatrix>(
num_rows, num_cols, num_rows * num_cols);
problem->b = std::make_unique<double[]>(num_rows);
problem->D = std::make_unique<double[]>(num_cols);
problem->num_eliminate_blocks = 2;
problem->x = std::make_unique<double[]>(num_cols);
problem->x[0] = -2.3061;
problem->x[1] = 0.3172;
problem->x[2] = 0.2102;
problem->x[3] = 2.1367;
problem->x[4] = 0.1388;
int* rows = A->mutable_rows();
int* cols = A->mutable_cols();
double* values = A->mutable_values();
int nnz = 0;
// Row 1
{
rows[nnz] = 0;
cols[nnz] = 0;
values[nnz++] = 1;
rows[nnz] = 0;
cols[nnz] = 2;
values[nnz++] = 2;
}
// Row 2
{
rows[nnz] = 1;
cols[nnz] = 0;
values[nnz++] = 3;
rows[nnz] = 1;
cols[nnz] = 3;
values[nnz++] = 4;
}
// Row 3
{
rows[nnz] = 2;
cols[nnz] = 1;
values[nnz++] = 5;
rows[nnz] = 2;
cols[nnz] = 4;
values[nnz++] = 6;
}
// Row 4
{
rows[nnz] = 3;
cols[nnz] = 1;
values[nnz++] = 7;
rows[nnz] = 3;
cols[nnz] = 2;
values[nnz++] = 8;
}
// Row 5
{
rows[nnz] = 4;
cols[nnz] = 1;
values[nnz++] = 9;
rows[nnz] = 4;
cols[nnz] = 2;
values[nnz++] = 1;
}
// Row 6
{
rows[nnz] = 5;
cols[nnz] = 2;
values[nnz++] = 1;
rows[nnz] = 5;
cols[nnz] = 3;
values[nnz++] = 1;
rows[nnz] = 5;
cols[nnz] = 4;
values[nnz++] = 1;
}
A->set_num_nonzeros(nnz);
CHECK(A->IsValid());
problem->A = std::move(A);
for (int i = 0; i < num_cols; ++i) {
problem->D.get()[i] = 1;
}
for (int i = 0; i < num_rows; ++i) {
problem->b.get()[i] = i;
}
return problem;
}
// BlockSparseMatrix version
std::unique_ptr<LinearLeastSquaresProblem> LinearLeastSquaresProblem2() {
int num_rows = 6;
int num_cols = 5;
auto problem = std::make_unique<LinearLeastSquaresProblem>();
problem->b = std::make_unique<double[]>(num_rows);
problem->D = std::make_unique<double[]>(num_cols);
problem->num_eliminate_blocks = 2;
problem->x = std::make_unique<double[]>(num_cols);
problem->x[0] = -2.3061;
problem->x[1] = 0.3172;
problem->x[2] = 0.2102;
problem->x[3] = 2.1367;
problem->x[4] = 0.1388;
auto* bs = new CompressedRowBlockStructure;
auto values = std::make_unique<double[]>(num_rows * num_cols);
for (int c = 0; c < num_cols; ++c) {
bs->cols.emplace_back();
bs->cols.back().size = 1;
bs->cols.back().position = c;
}
int nnz = 0;
// Row 1
{
values[nnz++] = 1;
values[nnz++] = 2;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 0;
row.cells.emplace_back(0, 0);
row.cells.emplace_back(2, 1);
}
// Row 2
{
values[nnz++] = 3;
values[nnz++] = 4;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 1;
row.cells.emplace_back(0, 2);
row.cells.emplace_back(3, 3);
}
// Row 3
{
values[nnz++] = 5;
values[nnz++] = 6;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 2;
row.cells.emplace_back(1, 4);
row.cells.emplace_back(4, 5);
}
// Row 4
{
values[nnz++] = 7;
values[nnz++] = 8;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 3;
row.cells.emplace_back(1, 6);
row.cells.emplace_back(2, 7);
}
// Row 5
{
values[nnz++] = 9;
values[nnz++] = 1;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 4;
row.cells.emplace_back(1, 8);
row.cells.emplace_back(2, 9);
}
// Row 6
{
values[nnz++] = 1;
values[nnz++] = 1;
values[nnz++] = 1;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 5;
row.cells.emplace_back(2, 10);
row.cells.emplace_back(3, 11);
row.cells.emplace_back(4, 12);
}
auto A = std::make_unique<BlockSparseMatrix>(bs);
memcpy(A->mutable_values(), values.get(), nnz * sizeof(*A->values()));
for (int i = 0; i < num_cols; ++i) {
problem->D.get()[i] = 1;
}
for (int i = 0; i < num_rows; ++i) {
problem->b.get()[i] = i;
}
problem->A = std::move(A);
return problem;
}
/*
A = [1 0
3 0
0 5
0 7
0 9
0 0]
b = [0
1
2
3
4
5]
*/
// BlockSparseMatrix version
std::unique_ptr<LinearLeastSquaresProblem> LinearLeastSquaresProblem3() {
int num_rows = 5;
int num_cols = 2;
auto problem = std::make_unique<LinearLeastSquaresProblem>();
problem->b = std::make_unique<double[]>(num_rows);
problem->D = std::make_unique<double[]>(num_cols);
problem->num_eliminate_blocks = 2;
auto* bs = new CompressedRowBlockStructure;
auto values = std::make_unique<double[]>(num_rows * num_cols);
for (int c = 0; c < num_cols; ++c) {
bs->cols.emplace_back();
bs->cols.back().size = 1;
bs->cols.back().position = c;
}
int nnz = 0;
// Row 1
{
values[nnz++] = 1;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 0;
row.cells.emplace_back(0, 0);
}
// Row 2
{
values[nnz++] = 3;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 1;
row.cells.emplace_back(0, 1);
}
// Row 3
{
values[nnz++] = 5;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 2;
row.cells.emplace_back(1, 2);
}
// Row 4
{
values[nnz++] = 7;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 3;
row.cells.emplace_back(1, 3);
}
// Row 5
{
values[nnz++] = 9;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 4;
row.cells.emplace_back(1, 4);
}
auto A = std::make_unique<BlockSparseMatrix>(bs);
memcpy(A->mutable_values(), values.get(), nnz * sizeof(*A->values()));
for (int i = 0; i < num_cols; ++i) {
problem->D.get()[i] = 1;
}
for (int i = 0; i < num_rows; ++i) {
problem->b.get()[i] = i;
}
problem->A = std::move(A);
return problem;
}
/*
A = [1 2 0 0 0 1 1
1 4 0 0 0 5 6
0 0 9 0 0 3 1]
b = [0
1
2]
*/
// BlockSparseMatrix version
//
// This problem has the unique property that it has two different
// sized f-blocks, but only one of them occurs in the rows involving
// the one e-block. So performing Schur elimination on this problem
// tests the Schur Eliminator's ability to handle non-e-block rows
// correctly when their structure does not conform to the static
// structure determined by DetectStructure.
//
// NOTE: This problem is too small and rank deficient to be solved without
// the diagonal regularization.
std::unique_ptr<LinearLeastSquaresProblem> LinearLeastSquaresProblem4() {
int num_rows = 3;
int num_cols = 7;
auto problem = std::make_unique<LinearLeastSquaresProblem>();
problem->b = std::make_unique<double[]>(num_rows);
problem->D = std::make_unique<double[]>(num_cols);
problem->num_eliminate_blocks = 1;
auto* bs = new CompressedRowBlockStructure;
auto values = std::make_unique<double[]>(num_rows * num_cols);
// Column block structure
bs->cols.emplace_back();
bs->cols.back().size = 2;
bs->cols.back().position = 0;
bs->cols.emplace_back();
bs->cols.back().size = 3;
bs->cols.back().position = 2;
bs->cols.emplace_back();
bs->cols.back().size = 2;
bs->cols.back().position = 5;
int nnz = 0;
// Row 1 & 2
{
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 2;
row.block.position = 0;
row.cells.emplace_back(0, nnz);
values[nnz++] = 1;
values[nnz++] = 2;
values[nnz++] = 1;
values[nnz++] = 4;
row.cells.emplace_back(2, nnz);
values[nnz++] = 1;
values[nnz++] = 1;
values[nnz++] = 5;
values[nnz++] = 6;
}
// Row 3
{
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 2;
row.cells.emplace_back(1, nnz);
values[nnz++] = 9;
values[nnz++] = 0;
values[nnz++] = 0;
row.cells.emplace_back(2, nnz);
values[nnz++] = 3;
values[nnz++] = 1;
}
auto A = std::make_unique<BlockSparseMatrix>(bs);
memcpy(A->mutable_values(), values.get(), nnz * sizeof(*A->values()));
for (int i = 0; i < num_cols; ++i) {
problem->D.get()[i] = (i + 1) * 100;
}
for (int i = 0; i < num_rows; ++i) {
problem->b.get()[i] = i;
}
problem->A = std::move(A);
return problem;
}
/*
A problem with block-diagonal F'F.
A = [1 0 | 0 0 2
3 0 | 0 0 4
0 -1 | 0 1 0
0 -3 | 0 1 0
0 -1 | 3 0 0
0 -2 | 1 0 0]
b = [0
1
2
3
4
5]
c = A'* b = [ 22
-25
17
7
4]
A'A = [10 0 0 0 10
0 15 -5 -4 0
0 -5 10 0 0
0 -4 0 2 0
10 0 0 0 20]
cond(A'A) = 41.402
S = [ 8.3333 -1.3333 0
-1.3333 0.9333 0
0 0 10.0000]
r = [ 8.6667
-1.6667
1.0000]
S\r = [ 0.9778
-0.3889
0.1000]
A\b = [ 0.2
-1.4444
0.9777
-0.3888
0.1]
*/
std::unique_ptr<LinearLeastSquaresProblem> LinearLeastSquaresProblem5() {
int num_rows = 6;
int num_cols = 5;
auto problem = std::make_unique<LinearLeastSquaresProblem>();
problem->b = std::make_unique<double[]>(num_rows);
problem->D = std::make_unique<double[]>(num_cols);
problem->num_eliminate_blocks = 2;
// TODO: add x
problem->x = std::make_unique<double[]>(num_cols);
problem->x[0] = 0.2;
problem->x[1] = -1.4444;
problem->x[2] = 0.9777;
problem->x[3] = -0.3888;
problem->x[4] = 0.1;
auto* bs = new CompressedRowBlockStructure;
auto values = std::make_unique<double[]>(num_rows * num_cols);
for (int c = 0; c < num_cols; ++c) {
bs->cols.emplace_back();
bs->cols.back().size = 1;
bs->cols.back().position = c;
}
int nnz = 0;
// Row 1
{
values[nnz++] = -1;
values[nnz++] = 2;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 0;
row.cells.emplace_back(0, 0);
row.cells.emplace_back(4, 1);
}
// Row 2
{
values[nnz++] = 3;
values[nnz++] = 4;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 1;
row.cells.emplace_back(0, 2);
row.cells.emplace_back(4, 3);
}
// Row 3
{
values[nnz++] = -1;
values[nnz++] = 1;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 2;
row.cells.emplace_back(1, 4);
row.cells.emplace_back(3, 5);
}
// Row 4
{
values[nnz++] = -3;
values[nnz++] = 1;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 3;
row.cells.emplace_back(1, 6);
row.cells.emplace_back(3, 7);
}
// Row 5
{
values[nnz++] = -1;
values[nnz++] = 3;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 4;
row.cells.emplace_back(1, 8);
row.cells.emplace_back(2, 9);
}
// Row 6
{
// values[nnz++] = 2;
values[nnz++] = -2;
values[nnz++] = 1;
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 5;
// row.cells.emplace_back(0, 10);
row.cells.emplace_back(1, 10);
row.cells.emplace_back(2, 11);
}
auto A = std::make_unique<BlockSparseMatrix>(bs);
memcpy(A->mutable_values(), values.get(), nnz * sizeof(*A->values()));
for (int i = 0; i < num_cols; ++i) {
problem->D.get()[i] = 1;
}
for (int i = 0; i < num_rows; ++i) {
problem->b.get()[i] = i;
}
problem->A = std::move(A);
return problem;
}
/*
A = [1 2 0 0 0 1 1
1 4 0 0 0 5 6
3 4 0 0 0 7 8
5 6 0 0 0 9 0
0 0 9 0 0 3 1]
b = [0
1
2
3
4]
*/
// BlockSparseMatrix version
//
// This problem has the unique property that it has two different
// sized f-blocks, but only one of them occurs in the rows involving
// the one e-block. So performing Schur elimination on this problem
// tests the Schur Eliminator's ability to handle non-e-block rows
// correctly when their structure does not conform to the static
// structure determined by DetectStructure.
//
// Additionally, this problem has the first row of the last row block of E being
// larger than number of row blocks in E
//
// NOTE: This problem is too small and rank deficient to be solved without
// the diagonal regularization.
std::unique_ptr<LinearLeastSquaresProblem> LinearLeastSquaresProblem6() {
int num_rows = 5;
int num_cols = 7;
auto problem = std::make_unique<LinearLeastSquaresProblem>();
problem->b = std::make_unique<double[]>(num_rows);
problem->D = std::make_unique<double[]>(num_cols);
problem->num_eliminate_blocks = 1;
auto* bs = new CompressedRowBlockStructure;
auto values = std::make_unique<double[]>(num_rows * num_cols);
// Column block structure
bs->cols.emplace_back();
bs->cols.back().size = 2;
bs->cols.back().position = 0;
bs->cols.emplace_back();
bs->cols.back().size = 3;
bs->cols.back().position = 2;
bs->cols.emplace_back();
bs->cols.back().size = 2;
bs->cols.back().position = 5;
int nnz = 0;
// Row 1 & 2
{
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 2;
row.block.position = 0;
row.cells.emplace_back(0, nnz);
values[nnz++] = 1;
values[nnz++] = 2;
values[nnz++] = 1;
values[nnz++] = 4;
row.cells.emplace_back(2, nnz);
values[nnz++] = 1;
values[nnz++] = 1;
values[nnz++] = 5;
values[nnz++] = 6;
}
// Row 3 & 4
{
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 2;
row.block.position = 2;
row.cells.emplace_back(0, nnz);
values[nnz++] = 3;
values[nnz++] = 4;
values[nnz++] = 5;
values[nnz++] = 6;
row.cells.emplace_back(2, nnz);
values[nnz++] = 7;
values[nnz++] = 8;
values[nnz++] = 9;
values[nnz++] = 0;
}
// Row 5
{
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = 1;
row.block.position = 4;
row.cells.emplace_back(1, nnz);
values[nnz++] = 9;
values[nnz++] = 0;
values[nnz++] = 0;
row.cells.emplace_back(2, nnz);
values[nnz++] = 3;
values[nnz++] = 1;
}
auto A = std::make_unique<BlockSparseMatrix>(bs);
memcpy(A->mutable_values(), values.get(), nnz * sizeof(*A->values()));
for (int i = 0; i < num_cols; ++i) {
problem->D.get()[i] = (i + 1) * 100;
}
for (int i = 0; i < num_rows; ++i) {
problem->b.get()[i] = i;
}
problem->A = std::move(A);
return problem;
}
namespace {
bool DumpLinearLeastSquaresProblemToConsole(const SparseMatrix* A,
const double* D,
const double* b,
const double* x,
int /*num_eliminate_blocks*/) {
CHECK(A != nullptr);
Matrix AA;
A->ToDenseMatrix(&AA);
LOG(INFO) << "A^T: \n" << AA.transpose();
if (D != nullptr) {
LOG(INFO) << "A's appended diagonal:\n" << ConstVectorRef(D, A->num_cols());
}
if (b != nullptr) {
LOG(INFO) << "b: \n" << ConstVectorRef(b, A->num_rows());
}
if (x != nullptr) {
LOG(INFO) << "x: \n" << ConstVectorRef(x, A->num_cols());
}
return true;
}
void WriteArrayToFileOrDie(const std::string& filename,
const double* x,
const int size) {
CHECK(x != nullptr);
VLOG(2) << "Writing array to: " << filename;
FILE* fptr = fopen(filename.c_str(), "w");
CHECK(fptr != nullptr);
for (int i = 0; i < size; ++i) {
fprintf(fptr, "%17f\n", x[i]);
}
fclose(fptr);
}
bool DumpLinearLeastSquaresProblemToTextFile(const std::string& filename_base,
const SparseMatrix* A,
const double* D,
const double* b,
const double* x,
int /*num_eliminate_blocks*/) {
CHECK(A != nullptr);
LOG(INFO) << "writing to: " << filename_base << "*";
std::string matlab_script;
StringAppendF(&matlab_script,
"function lsqp = load_trust_region_problem()\n");
StringAppendF(&matlab_script, "lsqp.num_rows = %d;\n", A->num_rows());
StringAppendF(&matlab_script, "lsqp.num_cols = %d;\n", A->num_cols());
{
std::string filename = filename_base + "_A.txt";
FILE* fptr = fopen(filename.c_str(), "w");
CHECK(fptr != nullptr);
A->ToTextFile(fptr);
fclose(fptr);
StringAppendF(
&matlab_script, "tmp = load('%s', '-ascii');\n", filename.c_str());
StringAppendF(
&matlab_script,
"lsqp.A = sparse(tmp(:, 1) + 1, tmp(:, 2) + 1, tmp(:, 3), %d, %d);\n",
A->num_rows(),
A->num_cols());
}
if (D != nullptr) {
std::string filename = filename_base + "_D.txt";
WriteArrayToFileOrDie(filename, D, A->num_cols());
StringAppendF(
&matlab_script, "lsqp.D = load('%s', '-ascii');\n", filename.c_str());
}
if (b != nullptr) {
std::string filename = filename_base + "_b.txt";
WriteArrayToFileOrDie(filename, b, A->num_rows());
StringAppendF(
&matlab_script, "lsqp.b = load('%s', '-ascii');\n", filename.c_str());
}
if (x != nullptr) {
std::string filename = filename_base + "_x.txt";
WriteArrayToFileOrDie(filename, x, A->num_cols());
StringAppendF(
&matlab_script, "lsqp.x = load('%s', '-ascii');\n", filename.c_str());
}
std::string matlab_filename = filename_base + ".m";
WriteStringToFileOrDie(matlab_script, matlab_filename);
return true;
}
} // namespace
bool DumpLinearLeastSquaresProblem(const std::string& filename_base,
DumpFormatType dump_format_type,
const SparseMatrix* A,
const double* D,
const double* b,
const double* x,
int num_eliminate_blocks) {
switch (dump_format_type) {
case CONSOLE:
return DumpLinearLeastSquaresProblemToConsole(
A, D, b, x, num_eliminate_blocks);
case TEXTFILE:
return DumpLinearLeastSquaresProblemToTextFile(
filename_base, A, D, b, x, num_eliminate_blocks);
default:
LOG(FATAL) << "Unknown DumpFormatType " << dump_format_type;
}
return true;
}
} // namespace ceres::internal