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Diffstat (limited to 'extern/libmv/third_party/ceres/internal/ceres/polynomial.h')
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-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2012 Google Inc. All rights reserved.
-// http://code.google.com/p/ceres-solver/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-// this list of conditions and the following disclaimer in the documentation
-// and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-// used to endorse or promote products derived from this software without
-// specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: moll.markus@arcor.de (Markus Moll)
-// sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_INTERNAL_POLYNOMIAL_SOLVER_H_
-#define CERES_INTERNAL_POLYNOMIAL_SOLVER_H_
-
-#include <vector>
-#include "ceres/internal/eigen.h"
-#include "ceres/internal/port.h"
-
-namespace ceres {
-namespace internal {
-
-// All polynomials are assumed to be the form
-//
-// sum_{i=0}^N polynomial(i) x^{N-i}.
-//
-// and are given by a vector of coefficients of size N + 1.
-
-// Evaluate the polynomial at x using the Horner scheme.
-inline double EvaluatePolynomial(const Vector& polynomial, double x) {
- double v = 0.0;
- for (int i = 0; i < polynomial.size(); ++i) {
- v = v * x + polynomial(i);
- }
- return v;
-}
-
-// Use the companion matrix eigenvalues to determine the roots of the
-// polynomial.
-//
-// This function returns true on success, false otherwise.
-// Failure indicates that the polynomial is invalid (of size 0) or
-// that the eigenvalues of the companion matrix could not be computed.
-// On failure, a more detailed message will be written to LOG(ERROR).
-// If real is not NULL, the real parts of the roots will be returned in it.
-// Likewise, if imaginary is not NULL, imaginary parts will be returned in it.
-bool FindPolynomialRoots(const Vector& polynomial,
- Vector* real,
- Vector* imaginary);
-
-// Return the derivative of the given polynomial. It is assumed that
-// the input polynomial is at least of degree zero.
-Vector DifferentiatePolynomial(const Vector& polynomial);
-
-// Find the minimum value of the polynomial in the interval [x_min,
-// x_max]. The minimum is obtained by computing all the roots of the
-// derivative of the input polynomial. All real roots within the
-// interval [x_min, x_max] are considered as well as the end points
-// x_min and x_max. Since polynomials are differentiable functions,
-// this ensures that the true minimum is found.
-void MinimizePolynomial(const Vector& polynomial,
- double x_min,
- double x_max,
- double* optimal_x,
- double* optimal_value);
-
-// Structure for storing sample values of a function.
-//
-// Clients can use this struct to communicate the value of the
-// function and or its gradient at a given point x.
-struct FunctionSample {
- FunctionSample()
- : x(0.0),
- value(0.0),
- value_is_valid(false),
- gradient(0.0),
- gradient_is_valid(false) {
- }
- string ToDebugString() const;
-
- double x;
- double value; // value = f(x)
- bool value_is_valid;
- double gradient; // gradient = f'(x)
- bool gradient_is_valid;
-};
-
-// Given a set of function value and/or gradient samples, find a
-// polynomial whose value and gradients are exactly equal to the ones
-// in samples.
-//
-// Generally speaking,
-//
-// degree = # values + # gradients - 1
-//
-// Of course its possible to sample a polynomial any number of times,
-// in which case, generally speaking the spurious higher order
-// coefficients will be zero.
-Vector FindInterpolatingPolynomial(const vector<FunctionSample>& samples);
-
-// Interpolate the function described by samples with a polynomial,
-// and minimize it on the interval [x_min, x_max]. Depending on the
-// input samples, it is possible that the interpolation or the root
-// finding algorithms may fail due to numerical difficulties. But the
-// function is guaranteed to return its best guess of an answer, by
-// considering the samples and the end points as possible solutions.
-void MinimizeInterpolatingPolynomial(const vector<FunctionSample>& samples,
- double x_min,
- double x_max,
- double* optimal_x,
- double* optimal_value);
-
-} // namespace internal
-} // namespace ceres
-
-#endif // CERES_INTERNAL_POLYNOMIAL_SOLVER_H_