// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
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// 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.
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// used to endorse or promote products derived from this software without
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#ifndef CERES_INTERNAL_TRUST_REGION_STRATEGY_H_
#define CERES_INTERNAL_TRUST_REGION_STRATEGY_H_
#include "ceres/types.h"
namespace ceres {
namespace internal {
class LinearSolver;
class SparseMatrix;
// Interface for classes implementing various trust region strategies
// for nonlinear least squares problems.
//
// The object is expected to maintain and update a trust region
// radius, which it then uses to solve for the trust region step using
// the jacobian matrix and residual vector.
//
// Here the term trust region radius is used loosely, as the strategy
// is free to treat it as guidance and violate it as need be. e.g.,
// the LevenbergMarquardtStrategy uses the inverse of the trust region
// radius to scale the damping term, which controls the step size, but
// does not set a hard limit on its size.
class TrustRegionStrategy {
public:
struct Options {
Options()
: trust_region_strategy_type(LEVENBERG_MARQUARDT),
initial_radius(1e4),
max_radius(1e32),
lm_min_diagonal(1e-6),
lm_max_diagonal(1e32),
dogleg_type(TRADITIONAL_DOGLEG) {
}
TrustRegionStrategyType trust_region_strategy_type;
// Linear solver used for actually solving the trust region step.
LinearSolver* linear_solver;
double initial_radius;
double max_radius;
// Minimum and maximum values of the diagonal damping matrix used
// by LevenbergMarquardtStrategy. The DoglegStrategy also uses
// these bounds to construct a regularizing diagonal to ensure
// that the Gauss-Newton step computation is of full rank.
double lm_min_diagonal;
double lm_max_diagonal;
// Further specify which dogleg method to use
DoglegType dogleg_type;
};
// Per solve options.
struct PerSolveOptions {
// Forcing sequence for inexact solves.
double eta;
};
struct Summary {
Summary()
: residual_norm(0.0),
num_iterations(-1),
termination_type(FAILURE) {
}
// If the trust region problem is,
//
// 1/2 x'Ax + b'x + c,
//
// then
//
// residual_norm = |Ax -b|
double residual_norm;
// Number of iterations used by the linear solver. If a linear
// solver was not called (e.g., DogLegStrategy after an
// unsuccessful step), then this would be zero.
int num_iterations;
// Status of the linear solver used to solve the Newton system.
LinearSolverTerminationType termination_type;
};
virtual ~TrustRegionStrategy();
// Use the current radius to solve for the trust region step.
virtual Summary ComputeStep(const PerSolveOptions& per_solve_options,
SparseMatrix* jacobian,
const double* residuals,
double* step) = 0;
// Inform the strategy that the current step has been accepted, and
// that the ratio of the decrease in the non-linear objective to the
// decrease in the trust region model is step_quality.
virtual void StepAccepted(double step_quality) = 0;
// Inform the strategy that the current step has been rejected, and
// that the ratio of the decrease in the non-linear objective to the
// decrease in the trust region model is step_quality.
virtual void StepRejected(double step_quality) = 0;
// Inform the strategy that the current step has been rejected
// because it was found to be numerically invalid.
// StepRejected/StepAccepted will not be called for this step, and
// the strategy is free to do what it wants with this information.
virtual void StepIsInvalid() = 0;
// Current trust region radius.
virtual double Radius() const = 0;
// Factory.
static TrustRegionStrategy* Create(const Options& options);
};
} // namespace internal
} // namespace ceres
#endif // CERES_INTERNAL_TRUST_REGION_STRATEGY_H_