| /external/ceres-solver/internal/ceres/ |
| dynamic_compressed_row_finalizer.h | 42 DynamicCompressedRowSparseMatrix* jacobian = local
44 jacobian->Finalize(num_parameters);
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| 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[4] = x[3]; jacobian[5] = -x[2]; // NOLIN
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| 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();
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| trust_region_minimizer.h | 54 void EstimateScale(const SparseMatrix& jacobian, double* scale) const;
56 const SparseMatrix* jacobian,
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| 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
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| 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);
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| evaluator_test_utils.h | 44 const double jacobian[200]; member in struct:ceres::internal::ExpectedEvaluation
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| 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] -
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| 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(
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| 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()));
138 // Check "A" Jacobian.
140 // Check "B" Jacobian.
146 // Check "C" Jacobian for first parameter block.
155 // Check "C" Jacobian for second parameter block
190 vector<double*> jacobian; local
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
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| 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
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| dogleg_strategy.h | 48 // because the Jacobian is often rank-deficient and in such cases
62 SparseMatrix* jacobian,
84 SparseMatrix* jacobian,
86 void ComputeCauchyPoint(SparseMatrix* jacobian);
87 void ComputeGradient(SparseMatrix* jacobian, const double* residuals);
89 bool ComputeSubspaceModel(SparseMatrix* jacobian);
134 // user has recomputed the Jacobian matrix and new Gauss-Newton
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| 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()));
139 // Check "A" Jacobian.
141 // Check "B" Jacobian.
147 // Check "C" Jacobian for first parameter block.
156 // Check "C" Jacobian for second parameter block
191 vector<double*> jacobian; local
241 vector<double*> jacobian; local
444 vector<double*> jacobian; local
474 vector<double*> jacobian; local
496 vector<double*> jacobian; local
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| 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[i], jacobian_ref[i], kTolerance
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| evaluator.h | 54 // squares objective. This insulates the optimizer from issues like Jacobian
77 // This is used for computing the cost, residual and Jacobian for
82 // The residual, gradients and jacobian pointers can be NULL, in
100 CRSMatrix* jacobian);
102 // Build and return a sparse matrix for storing and working with the Jacobian
103 // of the objective function. The jacobian has dimensions
105 // sparse. Since the sparsity pattern of the Jacobian remains constant over
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 an
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| 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.
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| 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);
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| 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,
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| 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)) {
169 // and all calculations involving the Jacobian have to
172 SparseMatrix* jacobian,
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| levenberg_marquardt_strategy.h | 54 SparseMatrix* jacobian,
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| 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.
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| 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);
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/ |
| DifferentiableMultivariateVectorialFunction.java | 31 * Returns the jacobian function.
32 * @return the jacobian function
34 MultivariateMatrixFunction jacobian(); method in interface:DifferentiableMultivariateVectorialFunction
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| /external/ceres-solver/include/ceres/ |
| local_parameterization.h | 110 // Jacobian which is needed to compute the Jacobian of f w.r.t delta.
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;
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| autodiff_local_parameterization.h | 124 virtual bool ComputeJacobian(const double* x, double* jacobian) const {
136 double* jacobian_ptrs[2] = { NULL, jacobian };
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