// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 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
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
// keir@google.com (Keir Mierle)
#ifndef CERES_INTERNAL_EVALUATOR_H_
#define CERES_INTERNAL_EVALUATOR_H_
#include <map>
#include <string>
#include <vector>
#include "ceres/execution_summary.h"
#include "ceres/internal/port.h"
#include "ceres/types.h"
namespace ceres {
struct CRSMatrix;
namespace internal {
class Program;
class SparseMatrix;
// The Evaluator interface offers a way to interact with a least squares cost
// function that is useful for an optimizer that wants to minimize the least
// squares objective. This insulates the optimizer from issues like Jacobian
// storage, parameterization, etc.
class Evaluator {
public:
virtual ~Evaluator();
struct Options {
Options()
: num_threads(1),
num_eliminate_blocks(-1),
linear_solver_type(DENSE_QR),
dynamic_sparsity(false) {}
int num_threads;
int num_eliminate_blocks;
LinearSolverType linear_solver_type;
bool dynamic_sparsity;
};
static Evaluator* Create(const Options& options,
Program* program,
string* error);
// This is used for computing the cost, residual and Jacobian for
// returning to the user. For actually solving the optimization
// problem, the optimization algorithm uses the ProgramEvaluator
// objects directly.
//
// The residual, gradients and jacobian pointers can be NULL, in
// which case they will not be evaluated. cost cannot be NULL.
//
// The parallelism of the evaluator is controlled by num_threads; it
// should be at least 1.
//
// Note: That this function does not take a parameter vector as
// input. The parameter blocks are evaluated on the values contained
// in the arrays pointed to by their user_state pointers.
//
// Also worth noting is that this function mutates program by
// calling Program::SetParameterOffsetsAndIndex() on it so that an
// evaluator object can be constructed.
static bool Evaluate(Program* program,
int num_threads,
double* cost,
vector<double>* residuals,
vector<double>* gradient,
CRSMatrix* jacobian);
// Build and return a sparse matrix for storing and working with the Jacobian
// of the objective function. The jacobian has dimensions
// NumEffectiveParameters() by NumParameters(), and is typically extremely
// sparse. Since the sparsity pattern of the Jacobian remains constant over
// the lifetime of the optimization problem, this method is used to
// instantiate a SparseMatrix object with the appropriate sparsity structure
// (which can be an expensive operation) and then reused by the optimization
// algorithm and the various linear solvers.
//
// It is expected that the classes implementing this interface will be aware
// of their client's requirements for the kind of sparse matrix storage and
// layout that is needed for an efficient implementation. For example
// CompressedRowOptimizationProblem creates a compressed row representation of
// the jacobian for use with CHOLMOD, where as BlockOptimizationProblem
// creates a BlockSparseMatrix representation of the jacobian for use in the
// Schur complement based methods.
virtual SparseMatrix* CreateJacobian() const = 0;
// Options struct to control Evaluator::Evaluate;
struct EvaluateOptions {
EvaluateOptions()
: apply_loss_function(true) {
}
// If false, the loss function correction is not applied to the
// residual blocks.
bool apply_loss_function;
};
// Evaluate the cost function for the given state. Returns the cost,
// residuals, and jacobian in the corresponding arguments. Both residuals and
// jacobian are optional; to avoid computing them, pass NULL.
//
// If non-NULL, the Jacobian must have a suitable sparsity pattern; only the
// values array of the jacobian is modified.
//
// state is an array of size NumParameters(), cost is a pointer to a single
// double, and residuals is an array of doubles of size NumResiduals().
virtual bool Evaluate(const EvaluateOptions& evaluate_options,
const double* state,
double* cost,
double* residuals,
double* gradient,
SparseMatrix* jacobian) = 0;
// Variant of Evaluator::Evaluate where the user wishes to use the
// default EvaluateOptions struct. This is mostly here as a
// convenience method.
bool Evaluate(const double* state,
double* cost,
double* residuals,
double* gradient,
SparseMatrix* jacobian) {
return Evaluate(EvaluateOptions(),
state,
cost,
residuals,
gradient,
jacobian);
}
// Make a change delta (of size NumEffectiveParameters()) to state (of size
// NumParameters()) and store the result in state_plus_delta.
//
// In the case that there are no parameterizations used, this is equivalent to
//
// state_plus_delta[i] = state[i] + delta[i] ;
//
// however, the mapping is more complicated in the case of parameterizations
// like quaternions. This is the same as the "Plus()" operation in
// local_parameterization.h, but operating over the entire state vector for a
// problem.
virtual bool Plus(const double* state,
const double* delta,
double* state_plus_delta) const = 0;
// The number of parameters in the optimization problem.
virtual int NumParameters() const = 0;
// This is the effective number of parameters that the optimizer may adjust.
// This applies when there are parameterizations on some of the parameters.
virtual int NumEffectiveParameters() const = 0;
// The number of residuals in the optimization problem.
virtual int NumResiduals() const = 0;
// The following two methods return copies instead of references so
// that the base class implementation does not have to worry about
// life time issues. Further, these calls are not expected to be
// frequent or performance sensitive.
virtual map<string, int> CallStatistics() const {
return map<string, int>();
}
virtual map<string, double> TimeStatistics() const {
return map<string, double>();
}
};
} // namespace internal
} // namespace ceres
#endif // CERES_INTERNAL_EVALUATOR_H_