121 lines
3.9 KiB
C++
121 lines
3.9 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: keir@google.com (Keir Mierle)
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#ifndef CERES_INTERNAL_CGNR_LINEAR_OPERATOR_H_
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#define CERES_INTERNAL_CGNR_LINEAR_OPERATOR_H_
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#include <algorithm>
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#include "ceres/linear_operator.h"
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#include "ceres/internal/scoped_ptr.h"
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#include "ceres/internal/eigen.h"
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namespace ceres {
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namespace internal {
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class SparseMatrix;
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// A linear operator which takes a matrix A and a diagonal vector D and
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// performs products of the form
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//
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// (A^T A + D^T D)x
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//
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// This is used to implement iterative general sparse linear solving with
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// conjugate gradients, where A is the Jacobian and D is a regularizing
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// parameter. A brief proof that D^T D is the correct regularizer:
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//
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// Given a regularized least squares problem:
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//
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// min ||Ax - b||^2 + ||Dx||^2
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// x
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//
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// First expand into matrix notation:
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//
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// (Ax - b)^T (Ax - b) + xD^TDx
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//
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// Then multiply out to get:
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//
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// = xA^TAx - 2b^T Ax + b^Tb + xD^TDx
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//
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// Take the derivative:
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//
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// 0 = 2A^TAx - 2A^T b + 2 D^TDx
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// 0 = A^TAx - A^T b + D^TDx
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// 0 = (A^TA + D^TD)x - A^T b
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//
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// Thus, the symmetric system we need to solve for CGNR is
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//
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// Sx = z
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//
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// with S = A^TA + D^TD
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// and z = A^T b
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//
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// Note: This class is not thread safe, since it uses some temporary storage.
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class CgnrLinearOperator : public LinearOperator {
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public:
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CgnrLinearOperator(const LinearOperator& A, const double *D)
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: A_(A), D_(D), z_(new double[A.num_rows()]) {
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}
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virtual ~CgnrLinearOperator() {}
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virtual void RightMultiply(const double* x, double* y) const {
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std::fill(z_.get(), z_.get() + A_.num_rows(), 0.0);
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// z = Ax
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A_.RightMultiply(x, z_.get());
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// y = y + Atz
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A_.LeftMultiply(z_.get(), y);
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// y = y + DtDx
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if (D_ != NULL) {
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int n = A_.num_cols();
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VectorRef(y, n).array() += ConstVectorRef(D_, n).array().square() *
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ConstVectorRef(x, n).array();
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}
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}
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virtual void LeftMultiply(const double* x, double* y) const {
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RightMultiply(x, y);
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}
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virtual int num_rows() const { return A_.num_cols(); }
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virtual int num_cols() const { return A_.num_cols(); }
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private:
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const LinearOperator& A_;
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const double* D_;
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scoped_array<double> z_;
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};
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} // namespace internal
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} // namespace ceres
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#endif // CERES_INTERNAL_CGNR_LINEAR_OPERATOR_H_
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