/external/ceres-solver/internal/ceres/ |
H A D | dynamic_compressed_row_finalizer.h | 42 DynamicCompressedRowSparseMatrix* jacobian = local 44 jacobian->Finalize(num_parameters);
|
H A D | local_parameterization.cc | 53 double* jacobian) const { 54 MatrixRef(jacobian, size_, size_) = Matrix::Identity(size_, size_); 100 double* jacobian) const { 101 MatrixRef m(jacobian, constancy_mask_.size(), local_size_); 133 double* jacobian) const { 134 jacobian[0] = -x[1]; jacobian[1] = -x[2]; jacobian[2] = -x[3]; // NOLINT 135 jacobian[3] = x[0]; jacobian[ [all...] |
H A D | block_evaluate_preparer.cc | 49 // 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 D | trust_region_minimizer.h | 54 void EstimateScale(const SparseMatrix& jacobian, double* scale) const; 56 const SparseMatrix* jacobian,
|
H A D | corrector.cc | 46 // and the jacobian are scaled by the squareroot of the derivative 121 double* jacobian) { 123 DCHECK(jacobian != NULL); 127 VectorRef(jacobian, num_rows * num_cols) *= sqrt_rho1_; 146 r_transpose_j += jacobian[r * num_cols + c] * residuals[r]; 150 jacobian[r * num_cols + c] = sqrt_rho1_ * 151 (jacobian[r * num_cols + c] - 118 CorrectJacobian(const int num_rows, const int num_cols, double* residuals, double* jacobian) argument
|
H A D | dynamic_compressed_row_jacobian_writer.cc | 48 // Initialize `jacobian` with zero number of `max_num_nonzeros`. 52 DynamicCompressedRowSparseMatrix* jacobian = local 58 program_, jacobian); 60 return jacobian; 67 DynamicCompressedRowSparseMatrix* jacobian = local 79 // `residual_offset` is the residual row in the global jacobian. 80 // Empty the jacobian rows. 81 jacobian->ClearRows(residual_offset, num_residuals); 98 jacobian->InsertEntry(
|
H A D | dynamic_autodiff_cost_function_test.cc | 119 // 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 D | corrector_test.cc | 60 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 D | dogleg_strategy.h | 62 SparseMatrix* jacobian, 84 SparseMatrix* jacobian, 86 void ComputeCauchyPoint(SparseMatrix* jacobian); 87 void ComputeGradient(SparseMatrix* jacobian, const double* residuals); 89 bool ComputeSubspaceModel(SparseMatrix* jacobian);
|
H A D | dynamic_numeric_diff_cost_function_test.cc | 120 // Prepare the jacobian. 124 vector<double*> jacobian; local 125 jacobian.push_back(jacobian_vect[0].data()); 126 jacobian.push_back(jacobian_vect[1].data()); 128 // Test jacobian computation. 131 jacobian.data())); 187 // Prepare the jacobian. 191 vector<double*> jacobian; local 192 jacobian.push_back(NULL); 193 jacobian 241 vector<double*> jacobian; local 444 vector<double*> jacobian; local 474 vector<double*> jacobian; local 496 vector<double*> jacobian; local [all...] |
H A D | autodiff_local_parameterization_test.cc | 64 double jacobian[9]; local 65 parameterization.ComputeJacobian(x, jacobian); 69 EXPECT_EQ(jacobian[k], (i == j) ? 1.0 : 0.0); 105 double jacobian[9]; local 106 parameterization.ComputeJacobian(x, jacobian); 110 EXPECT_NEAR(jacobian[k], (i == j) ? 1.2345 : 0.0, kTolerance); 157 double jacobian[12]; local 160 parameterization.ComputeJacobian(x, jacobian); 175 EXPECT_TRUE(IsFinite(jacobian[i])); 176 EXPECT_NEAR(jacobian[ [all...] |
H A D | block_evaluate_preparer.h | 31 // A evaluate preparer which puts jacobian the evaluated jacobian blocks 33 // The evaluator takes care to avoid evaluating the jacobian for fixed 57 // Point the jacobian blocks directly into the block sparse matrix, if 58 // jacobian is non-null. Otherwise, uses an internal per-thread buffer to 62 SparseMatrix* jacobian, 68 // For the case that the overall jacobian is not available, but the
|
H A D | corrector.h | 42 // to the residual and jacobian of a least squares problem based on a 47 // corresponding corrections to the residual and jacobian. For the 64 // CorrectResidual, because the jacobian correction depends on the 71 // jacobian = sqrt(rho[1]) * jacobian - 72 // sqrt(rho[1]) * alpha / sq_norm * residuals residuals' * jacobian. 74 // The method assumes that the jacobian has row-major storage. It is 76 // jacobian is not null. 80 double* jacobian);
|
H A D | evaluator.h | 82 // The residual, gradients and jacobian pointers can be NULL, in 100 CRSMatrix* jacobian); 103 // of the objective function. The jacobian has dimensions 115 // the jacobian for use with CHOLMOD, where as BlockOptimizationProblem 116 // creates a BlockSparseMatrix representation of the jacobian for use in the 133 // residuals, and jacobian in the corresponding arguments. Both residuals and 134 // jacobian are optional; to avoid computing them, pass NULL. 137 // values array of the jacobian is modified. 146 SparseMatrix* jacobian) = 0; 155 SparseMatrix* jacobian) { 151 Evaluate(const double* state, double* cost, double* residuals, double* gradient, SparseMatrix* jacobian) argument [all...] |
H A D | evaluator_test_utils.h | 44 const double jacobian[200]; member in struct:ceres::internal::ExpectedEvaluation
|
H A D | scratch_evaluate_preparer.h | 33 // care to avoid evaluating the jacobian for fixed parameters. 57 SparseMatrix* jacobian, 61 // Scratch space for the jacobians; each jacobian is packed one after another.
|
H A D | levenberg_marquardt_strategy_test.cc | 114 Matrix jacobian(2, 3); 115 jacobian.setZero(); 116 jacobian(0, 0) = 0.0; 117 jacobian(0, 1) = 1.0; 118 jacobian(1, 1) = 1.0; 119 jacobian(0, 2) = 100.0; 123 DenseSparseMatrix dsm(jacobian);
|
H A D | levenberg_marquardt_strategy.cc | 67 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); 125 jacobian, 65 ComputeStep( const TrustRegionStrategy::PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals, double* step) argument
|
H A D | dogleg_strategy.cc | 79 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); 152 if (!ComputeSubspaceModel(jacobian)) { 172 SparseMatrix* jacobian, 175 jacobian 77 ComputeStep( const TrustRegionStrategy::PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals, double* step) argument 171 ComputeGradient( SparseMatrix* jacobian, const double* residuals) argument 181 ComputeCauchyPoint(SparseMatrix* jacobian) argument 514 ComputeGaussNewtonStep( const PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals) argument 645 ComputeSubspaceModel(SparseMatrix* jacobian) argument [all...] |
H A D | dense_jacobian_writer.h | 31 // 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 D | local_parameterization_test.cc | 55 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 D | evaluator_test.cc | 70 // evaluator into the "local" jacobian. In the tests, the "subset 72 // from these jacobians. Put values in the jacobian that make this 82 MatrixRef jacobian(jacobians[k], 86 jacobian.col(j).setConstant(kFactor * (j + 1)); 155 scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian()); 159 ASSERT_EQ(expected_num_rows, jacobian->num_rows()); 160 ASSERT_EQ(expected_num_cols, jacobian->num_cols()); 169 expected_jacobian != NULL ? jacobian.get() : NULL)); 173 jacobian->ToDenseMatrix(&actual_jacobian); 197 (i & 4) ? expected.jacobian 578 double* jacobian = jacobians[0]; local [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/ |
H A D | DifferentiableMultivariateVectorialFunction.java | 31 * Returns the jacobian function. 32 * @return the jacobian function 34 MultivariateMatrixFunction jacobian(); method in interface:DifferentiableMultivariateVectorialFunction
|
/external/ceres-solver/include/ceres/ |
H A D | local_parameterization.h | 124 // The jacobian of Plus(x, delta) w.r.t delta at delta = 0. 125 virtual bool ComputeJacobian(const double* x, double* jacobian) const = 0; 145 double* jacobian) const; 163 double* jacobian) const; 185 double* jacobian) const;
|
/external/apache-commons-math/src/main/java/org/apache/commons/math/estimation/ |
H A D | LevenbergMarquardtEstimator.java | 32 * to their jacobian column norm. Only the rank of the matrix and some loop bounds 118 /** Norms of the columns of the jacobian matrix. */ 127 /** Rank of the jacobian matrix. */ 146 * and the columns of the jacobian. */ 212 * between the function vector and the columns of the jacobian 279 // compute the Q.R. decomposition of the jacobian matrix 287 // so let jacobian contain the R matrix with its diagonal elements 290 jacobian[k * cols + pk] = diagR[pk]; 296 // of the initial jacobian 314 // check orthogonality between function vector and jacobian column [all...] |