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c8658c8992
Apply clang-tidy Google and modernize fixes without trailing return type
using:
$ clang-tidy -p <build-dir> \
-checks='-*,google-*,modernize-*,-modernize-use-trailing-return-type' {} -fix
Change-Id: I7450cc58ea9abf928f73a467e87876083217fa26
217 lines
6.9 KiB
C++
217 lines
6.9 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: richie.stebbing@gmail.com (Richard Stebbing)
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#include "ceres/dynamic_compressed_row_sparse_matrix.h"
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#include <memory>
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#include "ceres/casts.h"
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#include "ceres/compressed_row_sparse_matrix.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/linear_least_squares_problems.h"
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#include "ceres/triplet_sparse_matrix.h"
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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using std::copy;
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using std::vector;
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class DynamicCompressedRowSparseMatrixTest : public ::testing::Test {
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protected:
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void SetUp() final {
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num_rows = 7;
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num_cols = 4;
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// The number of additional elements reserved when `Finalize` is called
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// should have no effect on the number of rows, columns or nonzeros.
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// Set this to some nonzero value to be sure.
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num_additional_elements = 13;
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expected_num_nonzeros = num_rows * num_cols - std::min(num_rows, num_cols);
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InitialiseDenseReference();
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InitialiseSparseMatrixReferences();
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dcrsm = std::make_unique<DynamicCompressedRowSparseMatrix>(
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num_rows, num_cols, 0);
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}
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void Finalize() { dcrsm->Finalize(num_additional_elements); }
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void InitialiseDenseReference() {
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dense.resize(num_rows, num_cols);
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dense.setZero();
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int num_nonzeros = 0;
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for (int i = 0; i < (num_rows * num_cols); ++i) {
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const int r = i / num_cols, c = i % num_cols;
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if (r != c) {
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dense(r, c) = i + 1;
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++num_nonzeros;
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}
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}
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ASSERT_EQ(num_nonzeros, expected_num_nonzeros);
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}
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void InitialiseSparseMatrixReferences() {
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vector<int> rows, cols;
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vector<double> values;
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for (int i = 0; i < (num_rows * num_cols); ++i) {
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const int r = i / num_cols, c = i % num_cols;
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if (r != c) {
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rows.push_back(r);
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cols.push_back(c);
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values.push_back(i + 1);
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}
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}
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ASSERT_EQ(values.size(), expected_num_nonzeros);
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tsm = std::make_unique<TripletSparseMatrix>(
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num_rows, num_cols, expected_num_nonzeros);
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copy(rows.begin(), rows.end(), tsm->mutable_rows());
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copy(cols.begin(), cols.end(), tsm->mutable_cols());
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copy(values.begin(), values.end(), tsm->mutable_values());
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tsm->set_num_nonzeros(values.size());
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Matrix dense_from_tsm;
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tsm->ToDenseMatrix(&dense_from_tsm);
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ASSERT_TRUE((dense.array() == dense_from_tsm.array()).all());
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crsm = CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm);
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Matrix dense_from_crsm;
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crsm->ToDenseMatrix(&dense_from_crsm);
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ASSERT_TRUE((dense.array() == dense_from_crsm.array()).all());
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}
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void InsertNonZeroEntriesFromDenseReference() {
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for (int r = 0; r < num_rows; ++r) {
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for (int c = 0; c < num_cols; ++c) {
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const double& v = dense(r, c);
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if (v != 0.0) {
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dcrsm->InsertEntry(r, c, v);
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}
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}
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}
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}
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void ExpectEmpty() {
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EXPECT_EQ(dcrsm->num_rows(), num_rows);
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EXPECT_EQ(dcrsm->num_cols(), num_cols);
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EXPECT_EQ(dcrsm->num_nonzeros(), 0);
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Matrix dense_from_dcrsm;
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dcrsm->ToDenseMatrix(&dense_from_dcrsm);
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EXPECT_EQ(dense_from_dcrsm.rows(), num_rows);
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EXPECT_EQ(dense_from_dcrsm.cols(), num_cols);
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EXPECT_TRUE((dense_from_dcrsm.array() == 0.0).all());
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}
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void ExpectEqualToDenseReference() {
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Matrix dense_from_dcrsm;
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dcrsm->ToDenseMatrix(&dense_from_dcrsm);
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EXPECT_TRUE((dense.array() == dense_from_dcrsm.array()).all());
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}
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void ExpectEqualToCompressedRowSparseMatrixReference() {
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using ConstIntVectorRef = Eigen::Map<const Eigen::VectorXi>;
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ConstIntVectorRef crsm_rows(crsm->rows(), crsm->num_rows() + 1);
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ConstIntVectorRef dcrsm_rows(dcrsm->rows(), dcrsm->num_rows() + 1);
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EXPECT_TRUE((crsm_rows.array() == dcrsm_rows.array()).all());
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ConstIntVectorRef crsm_cols(crsm->cols(), crsm->num_nonzeros());
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ConstIntVectorRef dcrsm_cols(dcrsm->cols(), dcrsm->num_nonzeros());
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EXPECT_TRUE((crsm_cols.array() == dcrsm_cols.array()).all());
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ConstVectorRef crsm_values(crsm->values(), crsm->num_nonzeros());
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ConstVectorRef dcrsm_values(dcrsm->values(), dcrsm->num_nonzeros());
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EXPECT_TRUE((crsm_values.array() == dcrsm_values.array()).all());
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}
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int num_rows;
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int num_cols;
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int num_additional_elements;
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int expected_num_nonzeros;
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Matrix dense;
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std::unique_ptr<TripletSparseMatrix> tsm;
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std::unique_ptr<CompressedRowSparseMatrix> crsm;
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std::unique_ptr<DynamicCompressedRowSparseMatrix> dcrsm;
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};
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TEST_F(DynamicCompressedRowSparseMatrixTest, Initialization) {
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ExpectEmpty();
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Finalize();
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ExpectEmpty();
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}
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TEST_F(DynamicCompressedRowSparseMatrixTest, InsertEntryAndFinalize) {
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InsertNonZeroEntriesFromDenseReference();
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ExpectEmpty();
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Finalize();
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ExpectEqualToDenseReference();
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ExpectEqualToCompressedRowSparseMatrixReference();
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}
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TEST_F(DynamicCompressedRowSparseMatrixTest, ClearRows) {
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InsertNonZeroEntriesFromDenseReference();
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Finalize();
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ExpectEqualToDenseReference();
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ExpectEqualToCompressedRowSparseMatrixReference();
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dcrsm->ClearRows(0, 0);
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Finalize();
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ExpectEqualToDenseReference();
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ExpectEqualToCompressedRowSparseMatrixReference();
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dcrsm->ClearRows(0, num_rows);
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ExpectEqualToCompressedRowSparseMatrixReference();
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Finalize();
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ExpectEmpty();
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InsertNonZeroEntriesFromDenseReference();
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dcrsm->ClearRows(1, 2);
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Finalize();
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dense.block(1, 0, 2, num_cols).setZero();
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ExpectEqualToDenseReference();
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InitialiseDenseReference();
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}
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} // namespace internal
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} // namespace ceres
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