// Copyright (c) 2022, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // 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 ETH Zurich and UNC Chapel Hill 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 HOLDERS 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: Johannes L. Schoenberger (jsch-at-demuc-dot-de) #define TEST_NAME "optim/least_absolute_deviations" #include "util/testing.h" #include #include "optim/least_absolute_deviations.h" #include "util/random.h" using namespace colmap; BOOST_AUTO_TEST_CASE(TestOverDetermined) { Eigen::SparseMatrix A(4, 3); for (int i = 0; i < A.rows(); ++i) { for (int j = 0; j < A.cols(); ++j) { A.insert(i, j) = i * A.cols() + j + 1; } } A.coeffRef(0, 0) = 10; Eigen::VectorXd b(A.rows()); for (int i = 0; i < b.size(); ++i) { b(i) = i + 1; } Eigen::VectorXd x = Eigen::VectorXd::Zero(A.cols()); LeastAbsoluteDeviationsOptions options; BOOST_CHECK(SolveLeastAbsoluteDeviations(options, A, b, &x)); // Reference solution obtained with Boyd's Matlab implementation. const Eigen::Vector3d x_ref(0, 0, 1 / 3.0); BOOST_CHECK(x.isApprox(x_ref)); const Eigen::VectorXd residual = A * x - b; BOOST_CHECK_LE(residual.norm(), 1e-6); } BOOST_AUTO_TEST_CASE(TestWellDetermined) { Eigen::SparseMatrix A(3, 3); for (int i = 0; i < A.rows(); ++i) { for (int j = 0; j < A.cols(); ++j) { A.insert(i, j) = i * A.cols() + j + 1; } } A.coeffRef(0, 0) = 10; Eigen::VectorXd b(A.rows()); for (int i = 0; i < b.size(); ++i) { b(i) = i + 1; } Eigen::VectorXd x = Eigen::VectorXd::Zero(A.cols()); LeastAbsoluteDeviationsOptions options; BOOST_CHECK(SolveLeastAbsoluteDeviations(options, A, b, &x)); // Reference solution obtained with Boyd's Matlab implementation. const Eigen::Vector3d x_ref(0, 0, 1 / 3.0); BOOST_CHECK(x.isApprox(x_ref)); const Eigen::VectorXd residual = A * x - b; BOOST_CHECK_LE(residual.norm(), 1e-6); } BOOST_AUTO_TEST_CASE(TestUnderDetermined) { // In this case, the system is rank-deficient and not positive semi-definite. Eigen::SparseMatrix A(2, 3); Eigen::VectorXd b(A.rows()); Eigen::VectorXd x = Eigen::VectorXd::Zero(A.cols()); LeastAbsoluteDeviationsOptions options; BOOST_CHECK(!SolveLeastAbsoluteDeviations(options, A, b, &x)); }