Searched defs:jacobian (Results 1 - 25 of 29) sorted by relevance

12

/external/ceres-solver/internal/ceres/
H A Devaluator_test_utils.h44 const double jacobian[200]; member in struct:ceres::internal::ExpectedEvaluation
H A Dblock_evaluate_preparer.cc49 // Point the jacobian blocks directly into the block sparse matrix.
52 SparseMatrix* jacobian,
54 // If the overall jacobian is not available, use the scratch space.
55 if (jacobian == NULL) {
58 jacobian,
64 down_cast<BlockSparseMatrix*>(jacobian)->mutable_values();
50 Prepare(const ResidualBlock* residual_block, int residual_block_index, SparseMatrix* jacobian, double** jacobians) argument
H A Dcorrector.cc46 // and the jacobian are scaled by the squareroot of the derivative
115 double* jacobian) {
117 DCHECK(jacobian != NULL);
133 r_transpose_j += jacobian[r * num_cols + c] * residuals[r];
137 jacobian[r * num_cols + c] = sqrt_rho1_ *
138 (jacobian[r * num_cols + c] -
112 CorrectJacobian(int num_rows, int num_cols, double* residuals, double* jacobian) argument
H A Dcorrector_test.cc60 double jacobian = 10.0; local
74 // The jacobian in this case will be
75 // sqrt(kRho[1]) * (1 - kAlpha) * jacobian.
76 const double kExpectedJacobian = sqrt(kRho[1]) * (1 - kAlpha) * jacobian;
79 c.CorrectJacobian(1.0, 1.0, &residuals, &jacobian);
83 ASSERT_NEAR(kExpectedJacobian, jacobian, 1e-6);
88 double jacobian = 10.0; local
102 // The jacobian in this case will be
103 // sqrt(kRho[1]) * jacobian.
104 const double kExpectedJacobian = sqrt(kRho[1]) * jacobian;
116 double jacobian = 10.0; local
147 double jacobian[2 * 3]; local
215 double jacobian[2 * 3]; local
[all...]
H A Dnormal_prior_test.cc70 double * jacobian = new double[num_rows * num_cols]; local
74 prior.Evaluate(&x, residuals.data(), &jacobian);
82 MatrixRef J(jacobian, num_rows, num_cols);
87 delete []jacobian;
H A Devaluator.h80 // The residual, gradients and jacobian pointers can be NULL, in
98 CRSMatrix* jacobian);
101 // of the objective function. The jacobian has dimensions
113 // the jacobian for use with CHOLMOD, where as BlockOptimizationProblem
114 // creates a BlockSparseMatrix representation of the jacobian for use in the
131 // residuals, and jacobian in the corresponding arguments. Both residuals and
132 // jacobian are optional; to avoid computing them, pass NULL.
135 // values array of the jacobian is modified.
144 SparseMatrix* jacobian) = 0;
153 SparseMatrix* jacobian) {
149 Evaluate(const double* state, double* cost, double* residuals, double* gradient, SparseMatrix* jacobian) argument
[all...]
H A Dlevenberg_marquardt_strategy.cc67 SparseMatrix* jacobian,
70 CHECK_NOTNULL(jacobian);
74 const int num_parameters = jacobian->num_cols();
80 jacobian->SquaredColumnNorm(diagonal_.data());
104 // Then x can be found as x = -y, but the inputs jacobian and residuals
107 linear_solver_->Solve(jacobian, residuals, solve_options, step);
122 jacobian,
65 ComputeStep( const TrustRegionStrategy::PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals, double* step) argument
H A Dautodiff_local_parameterization_test.cc65 double jacobian[9]; local
66 parameterization.ComputeJacobian(x, jacobian);
70 EXPECT_EQ(jacobian[k], (i == j) ? 1.0 : 0.0);
117 double jacobian[12]; local
120 parameterization.ComputeJacobian(x, jacobian);
135 EXPECT_TRUE(IsFinite(jacobian[i]));
136 EXPECT_NEAR(jacobian[i], jacobian_ref[i], kTolerance)
139 << "\n Actual \n" << ConstMatrixRef(jacobian, 4, 3);
H A Ddense_jacobian_writer.h31 // A jacobian writer that writes to dense Eigen matrices.
58 // them over to the larger jacobian later.
72 SparseMatrix* jacobian) {
74 if (jacobian != NULL) {
75 dense_jacobian = down_cast<DenseSparseMatrix*>(jacobian);
82 // Now copy the jacobians for each parameter into the dense jacobian matrix.
69 Write(int residual_id, int residual_offset, double **jacobians, SparseMatrix* jacobian) argument
H A Ddogleg_strategy_test.cc77 Matrix jacobian = sqrtD * basis; local
78 jacobian_.reset(new DenseSparseMatrix(jacobian));
82 residual_ = -jacobian * minimum;
105 Matrix jacobian = Ddiag.asDiagonal(); local
106 jacobian_.reset(new DenseSparseMatrix(jacobian));
110 residual_ = -jacobian * minimum;
H A Dresidual_block_utils_test.cc62 double jacobian; local
63 double* jacobians[] = { &jacobian };
H A Dblock_jacobian_writer.cc47 // per-parameter jacobian goes where in the overall program jacobian.
63 // are there. This will determine where the F blocks start in the jacobian
64 // matrix. Also compute the number of jacobian blocks.
135 // Create evaluate prepareres that point directly into the final jacobian. This
206 BlockSparseMatrix* jacobian = new BlockSparseMatrix(bs); local
207 CHECK_NOTNULL(jacobian);
208 return jacobian;
H A Dcompressed_row_jacobian_writer.cc50 // Count the number of jacobian nonzeros.
64 // Allocate storage for the jacobian with some extra space at the end.
65 // Allocate more space than needed to store the jacobian so that when the LM
68 CompressedRowSparseMatrix* jacobian = local
76 int* rows = jacobian->mutable_rows();
77 int* cols = jacobian->mutable_cols();
112 // parameter vector. This code mirrors that in Write(), where jacobian
121 // This is the position in the values array of the jacobian where this
122 // row of the jacobian block should go.
140 vector<int>& col_blocks = *(jacobian
159 CompressedRowSparseMatrix* jacobian = local
[all...]
H A Dlocal_parameterization_test.cc55 double jacobian[9]; local
56 parameterization.ComputeJacobian(x, jacobian);
60 EXPECT_EQ(jacobian[k], (i == j) ? 1.0 : 0.0);
106 double jacobian[4 * 3]; local
107 parameterization.ComputeJacobian(x, jacobian);
113 EXPECT_EQ(jacobian[jacobian_cursor], delta_cursor == k ? 1.0 : 0.0);
118 EXPECT_EQ(jacobian[jacobian_cursor], 0.0);
184 // Autodiff jacobian at delta_x = 0.
188 double jacobian[12]; local
189 param.ComputeJacobian(x, jacobian);
[all...]
H A Ddogleg_strategy.cc79 SparseMatrix* jacobian,
82 CHECK_NOTNULL(jacobian);
86 const int n = jacobian->num_cols();
121 jacobian->SquaredColumnNorm(diagonal_.data());
127 ComputeGradient(jacobian, residuals);
128 ComputeCauchyPoint(jacobian);
131 ComputeGaussNewtonStep(per_solve_options, jacobian, residuals);
148 if (!ComputeSubspaceModel(jacobian)) {
168 SparseMatrix* jacobian,
171 jacobian
77 ComputeStep( const TrustRegionStrategy::PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals, double* step) argument
167 ComputeGradient( SparseMatrix* jacobian, const double* residuals) argument
177 ComputeCauchyPoint(SparseMatrix* jacobian) argument
510 ComputeGaussNewtonStep( const PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals) argument
637 ComputeSubspaceModel(SparseMatrix* jacobian) argument
[all...]
H A Ddynamic_autodiff_cost_function_test.cc119 // Prepare the jacobian.
123 vector<double*> jacobian; local
124 jacobian.push_back(jacobian_vect[0].data());
125 jacobian.push_back(jacobian_vect[1].data());
127 // Test jacobian computation.
130 jacobian.data()));
186 // Prepare the jacobian.
190 vector<double*> jacobian; local
191 jacobian.push_back(NULL);
192 jacobian
240 vector<double*> jacobian; local
443 vector<double*> jacobian; local
473 vector<double*> jacobian; local
495 vector<double*> jacobian; local
687 vector<double*> jacobian; local
713 vector<double*> jacobian; local
744 vector<double*> jacobian; local
[all...]
H A Devaluator_test.cc69 // evaluator into the "local" jacobian. In the tests, the "subset
71 // from these jacobians. Put values in the jacobian that make this
81 MatrixRef jacobian(jacobians[k],
85 jacobian.col(j).setConstant(kFactor * (j + 1));
130 scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());
134 ASSERT_EQ(expected_num_rows, jacobian->num_rows());
135 ASSERT_EQ(expected_num_cols, jacobian->num_cols());
144 expected_jacobian != NULL ? jacobian.get() : NULL));
148 jacobian->ToDenseMatrix(&actual_jacobian);
172 (i & 4) ? expected.jacobian
551 double* jacobian = jacobians[0]; local
[all...]
H A Dminimizer.h112 jacobian = NULL;
176 SparseMatrix* jacobian; member in struct:ceres::internal::Minimizer::Options
H A Dproblem.cc185 CRSMatrix* jacobian) {
190 jacobian);
181 Evaluate(const EvaluateOptions& evaluate_options, double* cost, vector<double>* residuals, vector<double>* gradient, CRSMatrix* jacobian) argument
H A Dprogram_evaluator.h32 // and stores the result into a jacobian. The particular type of jacobian is
36 // pointers to the jacobian blocks where the cost function evaluates to.
38 // jacobian blocks in the passed sparse matrix.
55 // SparseMatrix* jacobian,
60 // // Create a jacobian that this writer can write. Same as
69 // // larger sparse jacobian.
73 // SparseMatrix* jacobian);
130 SparseMatrix* jacobian) {
132 ScopedExecutionTimer call_type_timer(gradient == NULL && jacobian
125 Evaluate(const Evaluator::EvaluateOptions& evaluate_options, const double* state, double* cost, double* residuals, double* gradient, SparseMatrix* jacobian) argument
[all...]
H A Dtrust_region_minimizer.cc64 void TrustRegionMinimizer::EstimateScale(const SparseMatrix& jacobian, argument
66 jacobian.SquaredColumnNorm(scale);
67 for (int i = 0; i < jacobian.num_cols(); ++i) {
90 SparseMatrix* jacobian = CHECK_NOTNULL(options_.jacobian); local
130 jacobian)) {
171 EstimateScale(*jacobian, scale.data());
172 jacobian->ScaleColumns(scale.data());
220 jacobian,
243 jacobian
[all...]
H A Dtrust_region_minimizer_test.cc53 // indicate which of the four variables/columns of the jacobian are
90 SparseMatrix* jacobian) {
124 if (jacobian != NULL) {
126 dense_jacobian = down_cast<DenseSparseMatrix*>(jacobian);
227 scoped_ptr<SparseMatrix> jacobian(powell_evaluator.CreateJacobian());
234 minimizer_options.jacobian = jacobian.get();
85 Evaluate(const Evaluator::EvaluateOptions& evaluate_options, const double* state, double* cost, double* residuals, double* gradient, SparseMatrix* jacobian) argument
H A Dcovariance_test.cc129 const double* jacobian)
130 : jacobian_(jacobian, jacobian + num_residuals * parameter_block_size) {
214 virtual bool ComputeJacobian(const double* x, double* jacobian) const {
215 jacobian[0] = x[0];
216 jacobian[1] = x[1];
239 double jacobian[] = { 1.0, 0.0, 0.0, 1.0}; local
240 problem_.AddResidualBlock(new UnaryCostFunction(2, 2, jacobian), NULL, x);
244 double jacobian[] = { 2.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 2.0 }; local
245 problem_.AddResidualBlock(new UnaryCostFunction(3, 3, jacobian), NUL
127 UnaryCostFunction(const int num_residuals, const int16 parameter_block_size, const double* jacobian) argument
249 double jacobian = 5.0; local
629 double jacobian[] = { 1.0, 0.0, 0.0, 1.0}; local
634 double jacobian[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }; local
639 double jacobian = 5.0; local
[all...]
/external/ceres-solver/include/ceres/internal/
H A Dnumeric_diff.h94 double *jacobian) {
107 Map<JacobianMatrix> parameter_jacobian(jacobian,
141 // Compute this column of the jacobian in 3 steps:
190 double *jacobian) {
89 EvaluateJacobianForParameterBlock( const CostFunctor* functor, double const* residuals_at_eval_point, const double relative_step_size, double **parameters, double *jacobian) argument
185 EvaluateJacobianForParameterBlock( const CostFunctor* functor, double const* residuals_at_eval_point, const double relative_step_size, double **parameters, double *jacobian) argument
/external/eigen/unsupported/Eigen/src/AutoDiff/
H A DAutoDiffVector.h73 : m_values(other.values()), m_jacobian(other.jacobian())
77 : m_values(other.values()), m_jacobian(other.jacobian())
84 m_jacobian = other.jacobian();
91 m_jacobian = other.jacobian();
98 inline const JacobianType& jacobian() const { return m_jacobian; } function in class:Eigen::AutoDiffVector
99 inline JacobianType& jacobian() { return m_jacobian; } function in class:Eigen::AutoDiffVector
111 m_jacobian + other.jacobian());
119 m_jacobian += other.jacobian();
133 m_jacobian - other.jacobian());
141 m_jacobian -= other.jacobian();
[all...]

Completed in 772 milliseconds

12