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  /external/ceres-solver/internal/ceres/
low_rank_inverse_hessian.h 32 // Hessian, using the LBFGS algorithm
46 // Hessian using the limited memory variant of the
48 // approximating the Hessian.
64 // num_parameters is the row/column size of the Hessian.
65 // max_num_corrections is the rank of the Hessian approximation.
67 // inverse Hessian used during Right/LeftMultiply() is scaled by
68 // the approximate eigenvalue of the true inverse Hessian at the
79 // domain of Hessian, and delta_gradient is the change in the
low_rank_inverse_hessian.cc 45 // Hessian at the k+1-th iteration, s_k = (x_{k+1} - x_{k}) and
46 // y_k = (grad_{k+1} - grad_{k}). As the approximated Hessian must be
60 // to update the Hessian approximation if:
66 // information in the Hessian. For example going from 1e-10 -> 1e-14 improves
139 // Rescale the initial inverse Hessian approximation (H_0) to be iteratively
140 // updated so that it is of similar 'size' to the true inverse Hessian along
151 // the true Hessian (not the inverse) along the most recent search direction
153 // inverse Hessian, and choosing: H_0 = I * \gamma will yield a starting
154 // point that has a similar scale to the true inverse Hessian. This
172 << approximate_eigenvalue_scale_ << " to initial inverse Hessian "
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line_search_direction.cc 115 << "Ceres bug: NextDirection() called on L-BFGS after inverse Hessian "
129 LOG(WARNING) << "Numerical failure in L-BFGS update: inverse Hessian "
155 << " parameters, this will allocate a dense approximate inverse Hessian"
170 << "Ceres bug: NextDirection() called on BFGS after inverse Hessian "
183 // Hessian at the k+1-th iteration, s_k = (x_{k+1} - x_{k}) and
184 // y_k = (grad_{k+1} - grad_{k}). As the approximated Hessian must be
198 // to update the Hessian approximation if:
204 // information in the Hessian. For example going from 1e-10 -> 1e-14
219 // Update dense inverse Hessian approximation.
222 // Rescale the initial inverse Hessian approximation (H_0) to b
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coordinate_descent_minimizer.h 48 // (non-exhaustively) the Hessian matrix into independent sets,
75 // Find a recursive decomposition of the Hessian matrix as a set
corrector.cc 61 // Hessian gets both the scaling and the rank-1 curvature
75 // newton hessian goes from being a full rank correction to a rank
76 // deficient correction making the inversion of the Hessian fraught
corrector_test.cc 163 // Corrected hessian and gradient implied by the modified jacobian
202 // Corrected gradient and hessian.
231 // Corrected hessian and gradient implied by the modified jacobian
263 // Corrected gradient and hessian.
parameter_block_ordering.h 75 // structure reflects the sparsity structure of the Hessian. Each
coordinate_descent_minimizer.cc 257 // Find a recursive decomposition of the Hessian matrix as a set
program.h 122 // is an independent set in the Hessian matrix.
program.cc 367 // is an independent set in the Hessian matrix.
schur_eliminator_impl.h 423 // and the off diagonal blocks in the Guass Newton Hessian.
solver_impl.cc 644 // is an independent set in the Hessian matrix.
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  /external/opencv/cv/src/
cvsurf.cpp 10 * 2.A comparision with original libSurf.so shows that the hessian detector is not a 100% match to their implementation;
122 /* hessian detector */
144 float* hessian = hessians[k]->data.fl; local
148 hessian[i] = hessian[hessian_cols*hessian_rows-1-i] =
151 hessian += (SIZE0/2)*(hessian_cols + 1);
158 trace += hessian_cols, hessian += hessian_cols )
162 hessian[-j-1] = hessian[hessian_cols - SIZE0 + j] =
177 hessian[j] = (float)(dx*dy - dxy*dxy)
196 const float* hessian = hessians[k]->data.fl + i*hessian_cols; local
218 const float* hessian = hessians[z]->data.fl + (j*scale+scaleCache[z]\/2)\/scaleCache[z]-1 + local
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  /external/ceres-solver/include/ceres/
types.h 102 // Block diagonal of the Gauss-Newton Hessian.
192 // algorithms that approximate the Hessian matrix by iteratively refining
198 // equation. The requirement that the Hessian approximation be positive
201 // approximate Hessian by imposing the additional constraints that the
209 // maintains a full, dense approximation to the (inverse) Hessian, L-BFGS
213 // full dense inverse Hessian approximation. This is particularly important
solver.h 175 // The LBFGS hessian approximation is a low rank approximation to
176 // the inverse of the Hessian matrix. The rank of the
185 // 2. The Hessian approximation is constrained to be positive
202 // the initial inverse Hessian approximation is taken to be the Identity.
204 // chosen to approximate an eigenvalue of the true inverse Hessian can
584 // Hessian matrix's sparsity structure in a collection of
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  /external/chromium_org/third_party/webrtc/modules/audio_coding/codecs/isac/main/source/
pitch_estimator.c 530 /* gradient and approximate Hessian (lower triangle) for minimizing the filter's output power */
546 /* add gradient and Hessian (lower triangle) for dampening fast gain changes */
559 /* add gradient and Hessian for dampening gain */
570 /* compute Cholesky factorization of Hessian
  /external/webrtc/src/modules/audio_coding/codecs/isac/main/source/
pitch_estimator.c 530 /* gradient and approximate Hessian (lower triangle) for minimizing the filter's output power */
546 /* add gradient and Hessian (lower triangle) for dampening fast gain changes */
559 /* add gradient and Hessian for dampening gain */
570 /* compute Cholesky factorization of Hessian
  /external/opencv/cv/include/
cv.h 1084 float hessian; member in struct:CvSURFPoint
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  /external/ceres-solver/docs/source/
solving.rst 317 Hessian matrix's sparsity structure into a collection of
378 Here :math:`H(x)` is some approximation to the Hessian of the
407 Hessian is maintained and used to compute a quasi-Newton step
413 inverse Hessian used to compute a quasi-Newton step [Nocedal]_,
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modeling.rst     [all...]
  /external/ceres-solver/examples/
nist.cc 107 "Rank of L-BFGS inverse Hessian approximation in line search.");

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