Sameer Agarwal | 3e8d192 | 2012-11-28 17:20:22 -0800 | [diff] [blame] | 1 | // Ceres Solver - A fast non-linear least squares minimizer |
| 2 | // Copyright 2012 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 | // Limited memory positive definite approximation to the inverse |
Sameer Agarwal | 9883fc3 | 2012-11-30 12:32:43 -0800 | [diff] [blame] | 32 | // Hessian, using the LBFGS algorithm |
| 33 | |
| 34 | #ifndef CERES_INTERNAL_LOW_RANK_INVERSE_HESSIAN_H_ |
| 35 | #define CERES_INTERNAL_LOW_RANK_INVERSE_HESSIAN_H_ |
Sameer Agarwal | 3e8d192 | 2012-11-28 17:20:22 -0800 | [diff] [blame] | 36 | |
| 37 | #include "ceres/internal/eigen.h" |
| 38 | #include "ceres/linear_operator.h" |
| 39 | |
| 40 | namespace ceres { |
| 41 | namespace internal { |
| 42 | |
Sameer Agarwal | 9883fc3 | 2012-11-30 12:32:43 -0800 | [diff] [blame] | 43 | // LowRankInverseHessian is a positive definite approximation to the |
| 44 | // Hessian using the limited memory variant of the |
| 45 | // Broyden-Fletcher-Goldfarb-Shanno (BFGS)secant formula for |
| 46 | // approximating the Hessian. |
| 47 | // |
| 48 | // Other update rules like the Davidon-Fletcher-Powell (DFP) are |
| 49 | // possible, but the BFGS rule is considered the best performing one. |
| 50 | // |
| 51 | // The limited memory variant was developed by Nocedal and further |
| 52 | // enhanced with scaling rule by Byrd, Nocedal and Schanbel. |
| 53 | // |
| 54 | // Nocedal, J. (1980). "Updating Quasi-Newton Matrices with Limited |
| 55 | // Storage". Mathematics of Computation 35 (151): 773–782. |
| 56 | // |
| 57 | // Byrd, R. H.; Nocedal, J.; Schnabel, R. B. (1994). |
| 58 | // "Representations of Quasi-Newton Matrices and their use in |
| 59 | // Limited Memory Methods". Mathematical Programming 63 (4): |
| 60 | class LowRankInverseHessian : public LinearOperator { |
Sameer Agarwal | 3e8d192 | 2012-11-28 17:20:22 -0800 | [diff] [blame] | 61 | public: |
| 62 | // num_parameters is the row/column size of the Hessian. |
| 63 | // max_num_corrections is the rank of the Hessian approximation. |
| 64 | // The approximation uses: |
| 65 | // 2 * max_num_corrections * num_parameters + max_num_corrections |
| 66 | // doubles. |
Sameer Agarwal | 9883fc3 | 2012-11-30 12:32:43 -0800 | [diff] [blame] | 67 | LowRankInverseHessian(int num_parameters, int max_num_corrections); |
| 68 | virtual ~LowRankInverseHessian() {} |
Sameer Agarwal | 3e8d192 | 2012-11-28 17:20:22 -0800 | [diff] [blame] | 69 | |
Sameer Agarwal | 9883fc3 | 2012-11-30 12:32:43 -0800 | [diff] [blame] | 70 | // Update the low rank approximation. delta_x is the change in the |
| 71 | // domain of Hessian, and delta_gradient is the change in the |
| 72 | // gradient. The update copies the delta_x and delta_gradient |
| 73 | // vectors, and gets rid of the oldest delta_x and delta_gradient |
| 74 | // vectors if the number of corrections is already equal to |
| 75 | // max_num_corrections. |
Sameer Agarwal | 3e8d192 | 2012-11-28 17:20:22 -0800 | [diff] [blame] | 76 | bool Update(const Vector& delta_x, const Vector& delta_gradient); |
| 77 | |
| 78 | // LinearOperator interface |
| 79 | virtual void RightMultiply(const double* x, double* y) const; |
| 80 | virtual void LeftMultiply(const double* x, double* y) const { |
Sameer Agarwal | 509f68c | 2013-02-20 01:39:03 -0800 | [diff] [blame] | 81 | RightMultiply(x, y); |
Sameer Agarwal | 3e8d192 | 2012-11-28 17:20:22 -0800 | [diff] [blame] | 82 | } |
| 83 | virtual int num_rows() const { return num_parameters_; } |
| 84 | virtual int num_cols() const { return num_parameters_; } |
| 85 | |
| 86 | private: |
| 87 | const int num_parameters_; |
| 88 | const int max_num_corrections_; |
| 89 | int num_corrections_; |
| 90 | double diagonal_; |
| 91 | Matrix delta_x_history_; |
| 92 | Matrix delta_gradient_history_; |
| 93 | Vector delta_x_dot_delta_gradient_; |
| 94 | }; |
| 95 | |
| 96 | } // namespace internal |
| 97 | } // namespace ceres |
Sameer Agarwal | 9883fc3 | 2012-11-30 12:32:43 -0800 | [diff] [blame] | 98 | |
| 99 | #endif // CERES_INTERNAL_LOW_RANK_INVERSE_HESSIAN_H_ |