Carlos Hernandez | 79397c2 | 2014-08-07 17:51:38 -0700 | [diff] [blame] | 1 | // Ceres Solver - A fast non-linear least squares minimizer |
| 2 | // Copyright 2014 Google Inc. All rights reserved. |
| 3 | // http://code.google.com/p/ceres-solver/ |
| 4 | // |
| 5 | // Redistribution and use in source and binary forms, with or without |
| 6 | // modification, are permitted provided that the following conditions are met: |
| 7 | // |
| 8 | // * Redistributions of source code must retain the above copyright notice, |
| 9 | // this list of conditions and the following disclaimer. |
| 10 | // * Redistributions in binary form must reproduce the above copyright notice, |
| 11 | // this list of conditions and the following disclaimer in the documentation |
| 12 | // and/or other materials provided with the distribution. |
| 13 | // * Neither the name of Google Inc. nor the names of its contributors may be |
| 14 | // used to endorse or promote products derived from this software without |
| 15 | // specific prior written permission. |
| 16 | // |
| 17 | // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| 18 | // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| 19 | // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE |
| 20 | // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE |
| 21 | // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR |
| 22 | // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF |
| 23 | // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS |
| 24 | // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN |
| 25 | // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
| 26 | // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| 27 | // POSSIBILITY OF SUCH DAMAGE. |
| 28 | // |
| 29 | // Author: sameeragarwal@google.com (Sameer Agarwal) |
| 30 | |
| 31 | #include "ceres/solver.h" |
| 32 | |
| 33 | #include <limits> |
| 34 | #include <cmath> |
| 35 | #include <vector> |
| 36 | #include "gtest/gtest.h" |
| 37 | #include "ceres/internal/scoped_ptr.h" |
| 38 | #include "ceres/autodiff_cost_function.h" |
| 39 | #include "ceres/sized_cost_function.h" |
| 40 | #include "ceres/problem.h" |
| 41 | #include "ceres/problem_impl.h" |
| 42 | |
| 43 | namespace ceres { |
| 44 | namespace internal { |
| 45 | |
| 46 | TEST(SolverOptions, DefaultTrustRegionOptionsAreValid) { |
| 47 | Solver::Options options; |
| 48 | options.minimizer_type = TRUST_REGION; |
| 49 | string error; |
| 50 | EXPECT_TRUE(options.IsValid(&error)) << error; |
| 51 | } |
| 52 | |
| 53 | TEST(SolverOptions, DefaultLineSearchOptionsAreValid) { |
| 54 | Solver::Options options; |
| 55 | options.minimizer_type = LINE_SEARCH; |
| 56 | string error; |
| 57 | EXPECT_TRUE(options.IsValid(&error)) << error; |
| 58 | } |
| 59 | |
| 60 | struct QuadraticCostFunctor { |
| 61 | template <typename T> bool operator()(const T* const x, |
| 62 | T* residual) const { |
| 63 | residual[0] = T(5.0) - *x; |
| 64 | return true; |
| 65 | } |
| 66 | |
| 67 | static CostFunction* Create() { |
| 68 | return new AutoDiffCostFunction<QuadraticCostFunctor, 1, 1>( |
| 69 | new QuadraticCostFunctor); |
| 70 | } |
| 71 | }; |
| 72 | |
| 73 | struct RememberingCallback : public IterationCallback { |
| 74 | explicit RememberingCallback(double *x) : calls(0), x(x) {} |
| 75 | virtual ~RememberingCallback() {} |
| 76 | virtual CallbackReturnType operator()(const IterationSummary& summary) { |
| 77 | x_values.push_back(*x); |
| 78 | return SOLVER_CONTINUE; |
| 79 | } |
| 80 | int calls; |
| 81 | double *x; |
| 82 | vector<double> x_values; |
| 83 | }; |
| 84 | |
| 85 | TEST(Solver, UpdateStateEveryIterationOption) { |
| 86 | double x = 50.0; |
| 87 | const double original_x = x; |
| 88 | |
| 89 | scoped_ptr<CostFunction> cost_function(QuadraticCostFunctor::Create()); |
| 90 | Problem::Options problem_options; |
| 91 | problem_options.cost_function_ownership = DO_NOT_TAKE_OWNERSHIP; |
| 92 | Problem problem(problem_options); |
| 93 | problem.AddResidualBlock(cost_function.get(), NULL, &x); |
| 94 | |
| 95 | Solver::Options options; |
| 96 | options.linear_solver_type = DENSE_QR; |
| 97 | |
| 98 | RememberingCallback callback(&x); |
| 99 | options.callbacks.push_back(&callback); |
| 100 | |
| 101 | Solver::Summary summary; |
| 102 | |
| 103 | int num_iterations; |
| 104 | |
| 105 | // First try: no updating. |
| 106 | Solve(options, &problem, &summary); |
| 107 | num_iterations = summary.num_successful_steps + |
| 108 | summary.num_unsuccessful_steps; |
| 109 | EXPECT_GT(num_iterations, 1); |
| 110 | for (int i = 0; i < callback.x_values.size(); ++i) { |
| 111 | EXPECT_EQ(50.0, callback.x_values[i]); |
| 112 | } |
| 113 | |
| 114 | // Second try: with updating |
| 115 | x = 50.0; |
| 116 | options.update_state_every_iteration = true; |
| 117 | callback.x_values.clear(); |
| 118 | Solve(options, &problem, &summary); |
| 119 | num_iterations = summary.num_successful_steps + |
| 120 | summary.num_unsuccessful_steps; |
| 121 | EXPECT_GT(num_iterations, 1); |
| 122 | EXPECT_EQ(original_x, callback.x_values[0]); |
| 123 | EXPECT_NE(original_x, callback.x_values[1]); |
| 124 | } |
| 125 | |
| 126 | // The parameters must be in separate blocks so that they can be individually |
| 127 | // set constant or not. |
| 128 | struct Quadratic4DCostFunction { |
| 129 | template <typename T> bool operator()(const T* const x, |
| 130 | const T* const y, |
| 131 | const T* const z, |
| 132 | const T* const w, |
| 133 | T* residual) const { |
| 134 | // A 4-dimension axis-aligned quadratic. |
| 135 | residual[0] = T(10.0) - *x + |
| 136 | T(20.0) - *y + |
| 137 | T(30.0) - *z + |
| 138 | T(40.0) - *w; |
| 139 | return true; |
| 140 | } |
| 141 | |
| 142 | static CostFunction* Create() { |
| 143 | return new AutoDiffCostFunction<Quadratic4DCostFunction, 1, 1, 1, 1, 1>( |
| 144 | new Quadratic4DCostFunction); |
| 145 | } |
| 146 | }; |
| 147 | |
| 148 | // A cost function that simply returns its argument. |
| 149 | class UnaryIdentityCostFunction : public SizedCostFunction<1, 1> { |
| 150 | public: |
| 151 | virtual bool Evaluate(double const* const* parameters, |
| 152 | double* residuals, |
| 153 | double** jacobians) const { |
| 154 | residuals[0] = parameters[0][0]; |
| 155 | if (jacobians != NULL && jacobians[0] != NULL) { |
| 156 | jacobians[0][0] = 1.0; |
| 157 | } |
| 158 | return true; |
| 159 | } |
| 160 | }; |
| 161 | |
| 162 | TEST(Solver, TrustRegionProblemHasNoParameterBlocks) { |
| 163 | Problem problem; |
| 164 | Solver::Options options; |
| 165 | options.minimizer_type = TRUST_REGION; |
| 166 | Solver::Summary summary; |
| 167 | Solve(options, &problem, &summary); |
| 168 | EXPECT_EQ(summary.termination_type, CONVERGENCE); |
| 169 | EXPECT_EQ(summary.message, |
| 170 | "Function tolerance reached. " |
| 171 | "No non-constant parameter blocks found."); |
| 172 | } |
| 173 | |
| 174 | TEST(Solver, LineSearchProblemHasNoParameterBlocks) { |
| 175 | Problem problem; |
| 176 | Solver::Options options; |
| 177 | options.minimizer_type = LINE_SEARCH; |
| 178 | Solver::Summary summary; |
| 179 | Solve(options, &problem, &summary); |
| 180 | EXPECT_EQ(summary.termination_type, CONVERGENCE); |
| 181 | EXPECT_EQ(summary.message, |
| 182 | "Function tolerance reached. " |
| 183 | "No non-constant parameter blocks found."); |
| 184 | } |
| 185 | |
| 186 | TEST(Solver, TrustRegionProblemHasZeroResiduals) { |
| 187 | Problem problem; |
| 188 | double x = 1; |
| 189 | problem.AddParameterBlock(&x, 1); |
| 190 | Solver::Options options; |
| 191 | options.minimizer_type = TRUST_REGION; |
| 192 | Solver::Summary summary; |
| 193 | Solve(options, &problem, &summary); |
| 194 | EXPECT_EQ(summary.termination_type, CONVERGENCE); |
| 195 | EXPECT_EQ(summary.message, |
| 196 | "Function tolerance reached. " |
| 197 | "No non-constant parameter blocks found."); |
| 198 | } |
| 199 | |
| 200 | TEST(Solver, LineSearchProblemHasZeroResiduals) { |
| 201 | Problem problem; |
| 202 | double x = 1; |
| 203 | problem.AddParameterBlock(&x, 1); |
| 204 | Solver::Options options; |
| 205 | options.minimizer_type = LINE_SEARCH; |
| 206 | Solver::Summary summary; |
| 207 | Solve(options, &problem, &summary); |
| 208 | EXPECT_EQ(summary.termination_type, CONVERGENCE); |
| 209 | EXPECT_EQ(summary.message, |
| 210 | "Function tolerance reached. " |
| 211 | "No non-constant parameter blocks found."); |
| 212 | } |
| 213 | |
| 214 | TEST(Solver, TrustRegionProblemIsConstant) { |
| 215 | Problem problem; |
| 216 | double x = 1; |
| 217 | problem.AddResidualBlock(new UnaryIdentityCostFunction, NULL, &x); |
| 218 | problem.SetParameterBlockConstant(&x); |
| 219 | Solver::Options options; |
| 220 | options.minimizer_type = TRUST_REGION; |
| 221 | Solver::Summary summary; |
| 222 | Solve(options, &problem, &summary); |
| 223 | EXPECT_EQ(summary.termination_type, CONVERGENCE); |
| 224 | EXPECT_EQ(summary.initial_cost, 1.0 / 2.0); |
| 225 | EXPECT_EQ(summary.final_cost, 1.0 / 2.0); |
| 226 | } |
| 227 | |
| 228 | TEST(Solver, LineSearchProblemIsConstant) { |
| 229 | Problem problem; |
| 230 | double x = 1; |
| 231 | problem.AddResidualBlock(new UnaryIdentityCostFunction, NULL, &x); |
| 232 | problem.SetParameterBlockConstant(&x); |
| 233 | Solver::Options options; |
| 234 | options.minimizer_type = LINE_SEARCH; |
| 235 | Solver::Summary summary; |
| 236 | Solve(options, &problem, &summary); |
| 237 | EXPECT_EQ(summary.termination_type, CONVERGENCE); |
| 238 | EXPECT_EQ(summary.initial_cost, 1.0 / 2.0); |
| 239 | EXPECT_EQ(summary.final_cost, 1.0 / 2.0); |
| 240 | } |
| 241 | |
| 242 | #if defined(CERES_NO_SUITESPARSE) |
| 243 | TEST(Solver, SparseNormalCholeskyNoSuiteSparse) { |
| 244 | Solver::Options options; |
| 245 | options.sparse_linear_algebra_library_type = SUITE_SPARSE; |
| 246 | options.linear_solver_type = SPARSE_NORMAL_CHOLESKY; |
| 247 | string message; |
| 248 | EXPECT_FALSE(options.IsValid(&message)); |
| 249 | } |
| 250 | #endif |
| 251 | |
| 252 | #if defined(CERES_NO_CXSPARSE) |
| 253 | TEST(Solver, SparseNormalCholeskyNoCXSparse) { |
| 254 | Solver::Options options; |
| 255 | options.sparse_linear_algebra_library_type = CX_SPARSE; |
| 256 | options.linear_solver_type = SPARSE_NORMAL_CHOLESKY; |
| 257 | string message; |
| 258 | EXPECT_FALSE(options.IsValid(&message)); |
| 259 | } |
| 260 | #endif |
| 261 | |
| 262 | TEST(Solver, IterativeLinearSolverForDogleg) { |
| 263 | Solver::Options options; |
| 264 | options.trust_region_strategy_type = DOGLEG; |
| 265 | string message; |
| 266 | options.linear_solver_type = ITERATIVE_SCHUR; |
| 267 | EXPECT_FALSE(options.IsValid(&message)); |
| 268 | |
| 269 | options.linear_solver_type = CGNR; |
| 270 | EXPECT_FALSE(options.IsValid(&message)); |
| 271 | } |
| 272 | |
| 273 | TEST(Solver, LinearSolverTypeNormalOperation) { |
| 274 | Solver::Options options; |
| 275 | options.linear_solver_type = DENSE_QR; |
| 276 | |
| 277 | string message; |
| 278 | EXPECT_TRUE(options.IsValid(&message)); |
| 279 | |
| 280 | options.linear_solver_type = DENSE_NORMAL_CHOLESKY; |
| 281 | EXPECT_TRUE(options.IsValid(&message)); |
| 282 | |
| 283 | options.linear_solver_type = DENSE_SCHUR; |
| 284 | EXPECT_TRUE(options.IsValid(&message)); |
| 285 | |
| 286 | options.linear_solver_type = SPARSE_SCHUR; |
| 287 | #if defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CXSPARSE) |
| 288 | EXPECT_FALSE(options.IsValid(&message)); |
| 289 | #else |
| 290 | EXPECT_TRUE(options.IsValid(&message)); |
| 291 | #endif |
| 292 | |
| 293 | options.linear_solver_type = ITERATIVE_SCHUR; |
| 294 | EXPECT_TRUE(options.IsValid(&message)); |
| 295 | } |
| 296 | |
| 297 | } // namespace internal |
| 298 | } // namespace ceres |