blob: 3cf1c3282253f9b6e36357e8b10e6600c9a69f0a [file] [log] [blame]
// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2017 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// 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: mierle@gmail.com (Keir Mierle)
//
// WARNING WARNING WARNING
// WARNING WARNING WARNING Tiny solver is experimental and will change.
// WARNING WARNING WARNING
//
// A tiny least squares solver using Levenberg-Marquardt, intended for solving
// small dense problems with low latency and low overhead. The implementation
// takes care to do all allocation up front, so that no memory is allocated
// during solving. This is especially useful when solving many similar problems;
// for example, inverse pixel distortion for every pixel on a grid.
//
// Note: This code has no depedencies beyond Eigen, including on other parts of
// Ceres, so it is possible to take this file alone and put it in another
// project without the rest of Ceres.
//
// Algorithm based off of:
//
// [1] K. Madsen, H. Nielsen, O. Tingleoff.
// Methods for Non-linear Least Squares Problems.
// http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf
#ifndef CERES_PUBLIC_TINY_SOLVER_H_
#define CERES_PUBLIC_TINY_SOLVER_H_
#include <cassert>
#include <cmath>
#include "Eigen/Dense"
namespace ceres {
// To use tiny solver, create a class or struct that allows computing the cost
// function (described below). This is similar to a ceres::CostFunction, but is
// different to enable statically allocating all memory for the solve
// (specifically, enum sizes). Key parts are the Scalar typedef, the enums to
// describe problem sizes (needed to remove all heap allocations), and the
// operator() overload to evaluate the cost and (optionally) jacobians.
//
// struct TinySolverCostFunctionTraits {
// typedef double Scalar;
// enum {
// NUM_RESIDUALS = <int> OR Eigen::Dynamic,
// NUM_PARAMETERS = <int> OR Eigen::Dynamic,
// };
// bool operator()(const double* parameters,
// double* residuals,
// double* jacobian) const;
//
// int NumResiduals(); -- Needed if NUM_RESIDUALS == Eigen::Dynamic.
// int NumParameters(); -- Needed if NUM_PARAMETERS == Eigen::Dynamic.
// }
//
// For operator(), the size of the objects is:
//
// double* parameters -- NUM_PARAMETERS or NumParameters()
// double* residuals -- NUM_RESIDUALS or NumResiduals()
// double* jacobian -- NUM_RESIDUALS * NUM_PARAMETERS in column-major format
// (Eigen's default); or NULL if no jacobian requested.
//
// An example (fully statically sized):
//
// struct MyCostFunctionExample {
// typedef double Scalar;
// enum {
// NUM_RESIDUALS = 2,
// NUM_PARAMETERS = 3,
// };
// bool operator()(const double* parameters,
// double* residuals,
// double* jacobian) const {
// residuals[0] = x + 2*y + 4*z;
// residuals[1] = y * z;
// if (jacobian) {
// jacobian[0 * 2 + 0] = 1; // First column (x).
// jacobian[0 * 2 + 1] = 0;
//
// jacobian[1 * 2 + 0] = 2; // Second column (y).
// jacobian[1 * 2 + 1] = z;
//
// jacobian[2 * 2 + 0] = 4; // Third column (z).
// jacobian[2 * 2 + 1] = y;
// }
// return true;
// }
// };
//
// The solver supports either statically or dynamically sized cost
// functions. If the number of residuals is dynamic then the Function
// must define:
//
// int NumResiduals() const;
//
// If the number of parameters is dynamic then the Function must
// define:
//
// int NumParameters() const;
//
template<typename Function,
typename LinearSolver = Eigen::LDLT<
Eigen::Matrix<typename Function::Scalar,
Function::NUM_PARAMETERS,
Function::NUM_PARAMETERS>>>
class TinySolver {
public:
enum {
NUM_RESIDUALS = Function::NUM_RESIDUALS,
NUM_PARAMETERS = Function::NUM_PARAMETERS
};
typedef typename Function::Scalar Scalar;
typedef typename Eigen::Matrix<Scalar, NUM_PARAMETERS, 1> Parameters;
enum Status {
GRADIENT_TOO_SMALL, // eps > max(J'*f(x))
RELATIVE_STEP_SIZE_TOO_SMALL, // eps > ||dx|| / (||x|| + eps)
COST_TOO_SMALL, // eps > ||f(x)||^2 / 2
HIT_MAX_ITERATIONS,
// TODO(sameeragarwal): Deal with numerical failures.
};
struct Options {
Options()
: gradient_tolerance(1e-10),
parameter_tolerance(1e-8),
cost_threshold(std::numeric_limits<Scalar>::epsilon()),
initial_trust_region_radius(1e4),
max_num_iterations(50) {}
Scalar gradient_tolerance; // eps > max(J'*f(x))
Scalar parameter_tolerance; // eps > ||dx|| / ||x||
Scalar cost_threshold; // eps > ||f(x)||
Scalar initial_trust_region_radius;
int max_num_iterations;
};
struct Summary {
Summary()
: initial_cost(-1),
final_cost(-1),
gradient_max_norm(-1),
iterations(0),
status(HIT_MAX_ITERATIONS) {}
Scalar initial_cost; // 1/2 ||f(x)||^2
Scalar final_cost; // 1/2 ||f(x)||^2
Scalar gradient_max_norm; // max(J'f(x))
int iterations;
Status status;
};
bool Update(const Function& function, const Parameters &x) {
if (!function(x.data(), error_.data(), jacobian_.data())) {
return false;
}
error_ = -error_;
// On the first iteration, compute a diagonal (Jacobi) scaling
// matrix, which we store as a vector.
if (summary.iterations == 0) {
// jacobi_scaling = 1 / (1 + diagonal(J'J))
//
// 1 is added to the denominator to regularize small diagonal
// entries.
jacobi_scaling_ = 1.0 / (1.0 + jacobian_.colwise().norm().array());
}
// This explicitly computes the normal equations, which is numerically
// unstable. Nevertheless, it is often good enough and is fast.
//
// TODO(sameeragarwal): Refactor this to allow for DenseQR
// factorization.
jacobian_ = jacobian_ * jacobi_scaling_.asDiagonal();
jtj_ = jacobian_.transpose() * jacobian_;
g_ = jacobian_.transpose() * error_;
summary.gradient_max_norm = g_.array().abs().maxCoeff();
cost_ = error_.squaredNorm() / 2;
return true;
}
const Summary& Solve(const Function& function, Parameters* x_and_min) {
Initialize<NUM_RESIDUALS, NUM_PARAMETERS>(function);
assert(x_and_min);
Parameters& x = *x_and_min;
summary = Summary();
// TODO(sameeragarwal): Deal with failure here.
Update(function, x);
summary.initial_cost = cost_;
summary.final_cost = cost_;
if (summary.gradient_max_norm < options.gradient_tolerance) {
summary.status = GRADIENT_TOO_SMALL;
return summary;
}
if (cost_ < options.cost_threshold) {
summary.status = COST_TOO_SMALL;
return summary;
}
Scalar u = 1.0 / options.initial_trust_region_radius;
Scalar v = 2;
for (summary.iterations = 1;
summary.iterations < options.max_num_iterations;
summary.iterations++) {
jtj_regularized_ = jtj_;
const Scalar min_diagonal = 1e-6;
const Scalar max_diagonal = 1e32;
for (int i = 0; i < lm_diagonal_.rows(); ++i) {
lm_diagonal_[i] = std::sqrt(
u * std::min(std::max(jtj_(i, i), min_diagonal), max_diagonal));
jtj_regularized_(i, i) += lm_diagonal_[i] * lm_diagonal_[i];
}
// TODO(sameeragarwal): Check for failure and deal with it.
linear_solver_.compute(jtj_regularized_);
lm_step_ = linear_solver_.solve(g_);
dx_ = jacobi_scaling_.asDiagonal() * lm_step_;
// Adding parameter_tolerance to x.norm() ensures that this
// works if x is near zero.
const Scalar parameter_tolerance =
options.parameter_tolerance *
(x.norm() + options.parameter_tolerance);
if (dx_.norm() < parameter_tolerance) {
summary.status = RELATIVE_STEP_SIZE_TOO_SMALL;
break;
}
x_new_ = x + dx_;
// TODO(keir): Add proper handling of errors from user eval of cost
// functions.
function(&x_new_[0], &f_x_new_[0], NULL);
const Scalar cost_change = (2 * cost_ - f_x_new_.squaredNorm());
// TODO(sameeragarwal): Better more numerically stable evaluation.
const Scalar model_cost_change = lm_step_.dot(2 * g_ - jtj_ * lm_step_);
// rho is the ratio of the actual reduction in error to the reduction
// in error that would be obtained if the problem was linear. See [1]
// for details.
Scalar rho(cost_change / model_cost_change);
if (rho > 0) {
// Accept the Levenberg-Marquardt step because the linear
// model fits well.
x = x_new_;
// TODO(sameeragarwal): Deal with failure.
Update(function, x);
if (summary.gradient_max_norm < options.gradient_tolerance) {
summary.status = GRADIENT_TOO_SMALL;
break;
}
if (cost_ < options.cost_threshold) {
summary.status = COST_TOO_SMALL;
break;
}
Scalar tmp = Scalar(2 * rho - 1);
u = u * std::max(1 / 3., 1 - tmp * tmp * tmp);
v = 2;
continue;
}
// Reject the update because either the normal equations failed to solve
// or the local linear model was not good (rho < 0). Instead, increase u
// to move closer to gradient descent.
u *= v;
v *= 2;
}
summary.final_cost = cost_;
return summary;
}
Options options;
Summary summary;
private:
// Preallocate everything, including temporary storage needed for solving the
// linear system. This allows reusing the intermediate storage across solves.
LinearSolver linear_solver_;
Scalar cost_;
Parameters dx_, x_new_, g_, jacobi_scaling_, lm_diagonal_, lm_step_;
Eigen::Matrix<Scalar, NUM_RESIDUALS, 1> error_, f_x_new_;
Eigen::Matrix<Scalar, NUM_RESIDUALS, NUM_PARAMETERS> jacobian_;
Eigen::Matrix<Scalar, NUM_PARAMETERS, NUM_PARAMETERS> jtj_, jtj_regularized_;
// The following definitions are needed for template metaprogramming.
template <bool Condition, typename T>
struct enable_if;
template <typename T>
struct enable_if<true, T> {
typedef T type;
};
// The number of parameters and residuals are dynamically sized.
template <int R, int P>
typename enable_if<(R == Eigen::Dynamic && P == Eigen::Dynamic), void>::type
Initialize(const Function& function) {
Initialize(function.NumResiduals(), function.NumParameters());
}
// The number of parameters is dynamically sized and the number of
// residuals is statically sized.
template <int R, int P>
typename enable_if<(R == Eigen::Dynamic && P != Eigen::Dynamic), void>::type
Initialize(const Function& function) {
Initialize(function.NumResiduals(), P);
}
// The number of parameters is statically sized and the number of
// residuals is dynamically sized.
template <int R, int P>
typename enable_if<(R != Eigen::Dynamic && P == Eigen::Dynamic), void>::type
Initialize(const Function& function) {
Initialize(R, function.NumParameters());
}
// The number of parameters and residuals are statically sized.
template <int R, int P>
typename enable_if<(R != Eigen::Dynamic && P != Eigen::Dynamic), void>::type
Initialize(const Function& /* function */) {}
void Initialize(int num_residuals, int num_parameters) {
dx_.resize(num_parameters);
x_new_.resize(num_parameters);
g_.resize(num_parameters);
jacobi_scaling_.resize(num_parameters);
lm_diagonal_.resize(num_parameters);
lm_step_.resize(num_parameters);
error_.resize(num_residuals);
f_x_new_.resize(num_residuals);
jacobian_.resize(num_residuals, num_parameters);
jtj_.resize(num_parameters, num_parameters);
jtj_regularized_.resize(num_parameters, num_parameters);
}
};
} // namespace ceres
#endif // CERES_PUBLIC_TINY_SOLVER_H_