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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 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
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// 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: sameeragarwal@google.com (Sameer Agarwal)
// keir@google.com (Keir Mierle)
//
// The Problem object is used to build and hold least squares problems.
#ifndef CERES_PUBLIC_PROBLEM_H_
#define CERES_PUBLIC_PROBLEM_H_
#include <array>
#include <cstddef>
#include <map>
#include <memory>
#include <set>
#include <vector>
#include "ceres/context.h"
#include "ceres/internal/disable_warnings.h"
#include "ceres/internal/export.h"
#include "ceres/internal/port.h"
#include "ceres/types.h"
namespace ceres {
class CostFunction;
class EvaluationCallback;
class LossFunction;
class Manifold;
class Solver;
struct CRSMatrix;
namespace internal {
class Preprocessor;
class ProblemImpl;
class ParameterBlock;
class ResidualBlock;
} // namespace internal
// A ResidualBlockId is an opaque handle clients can use to remove residual
// blocks from a Problem after adding them.
using ResidualBlockId = internal::ResidualBlock*;
// A class to represent non-linear least squares problems. Such
// problems have a cost function that is a sum of error terms (known
// as "residuals"), where each residual is a function of some subset
// of the parameters. The cost function takes the form
//
// N 1
// SUM --- loss( || r_i1, r_i2,..., r_ik ||^2 ),
// i=1 2
//
// where
//
// r_ij is residual number i, component j; the residual is a function of some
// subset of the parameters x1...xk. For example, in a structure from
// motion problem a residual might be the difference between a measured
// point in an image and the reprojected position for the matching
// camera, point pair. The residual would have two components, error in x
// and error in y.
//
// loss(y) is the loss function; for example, squared error or Huber L1
// loss. If loss(y) = y, then the cost function is non-robustified
// least squares.
//
// This class is specifically designed to address the important subset of
// "sparse" least squares problems, where each component of the residual depends
// only on a small number number of parameters, even though the total number of
// residuals and parameters may be very large. This property affords tremendous
// gains in scale, allowing efficient solving of large problems that are
// otherwise inaccessible.
//
// The canonical example of a sparse least squares problem is
// "structure-from-motion" (SFM), where the parameters are points and cameras,
// and residuals are reprojection errors. Typically a single residual will
// depend only on 9 parameters (3 for the point, 6 for the camera).
//
// To create a least squares problem, use the AddResidualBlock() and
// AddParameterBlock() methods, documented below. Here is an example least
// squares problem containing 3 parameter blocks of sizes 3, 4 and 5
// respectively and two residual terms of size 2 and 6:
//
// double x1[] = { 1.0, 2.0, 3.0 };
// double x2[] = { 1.0, 2.0, 3.0, 5.0 };
// double x3[] = { 1.0, 2.0, 3.0, 6.0, 7.0 };
//
// Problem problem;
//
// problem.AddResidualBlock(new MyUnaryCostFunction(...), nullptr, x1);
// problem.AddResidualBlock(new MyBinaryCostFunction(...), nullptr, x2, x3);
//
// Please see cost_function.h for details of the CostFunction object.
class CERES_EXPORT Problem {
public:
struct CERES_EXPORT Options {
// These flags control whether the Problem object owns the CostFunctions,
// LossFunctions, and Manifolds passed into the Problem.
//
// If set to TAKE_OWNERSHIP, then the problem object will delete the
// corresponding object on destruction. The destructor is careful to delete
// the pointers only once, since sharing objects is allowed.
Ownership cost_function_ownership = TAKE_OWNERSHIP;
Ownership loss_function_ownership = TAKE_OWNERSHIP;
Ownership manifold_ownership = TAKE_OWNERSHIP;
// If true, trades memory for faster RemoveResidualBlock() and
// RemoveParameterBlock() operations.
//
// By default, RemoveParameterBlock() and RemoveResidualBlock() take time
// proportional to the size of the entire problem. If you only ever remove
// parameters or residuals from the problem occasionally, this might be
// acceptable. However, if you have memory to spare, enable this option to
// make RemoveParameterBlock() take time proportional to the number of
// residual blocks that depend on it, and RemoveResidualBlock() take (on
// average) constant time.
//
// The increase in memory usage is two-fold: an additional hash set per
// parameter block containing all the residuals that depend on the parameter
// block; and a hash set in the problem containing all residuals.
bool enable_fast_removal = false;
// By default, Ceres performs a variety of safety checks when constructing
// the problem. There is a small but measurable performance penalty to these
// checks, typically around 5% of construction time. If you are sure your
// problem construction is correct, and 5% of the problem construction time
// is truly an overhead you want to avoid, then you can set
// disable_all_safety_checks to true.
//
// WARNING: Do not set this to true, unless you are absolutely sure of what
// you are doing.
bool disable_all_safety_checks = false;
// A Ceres global context to use for solving this problem. This may help to
// reduce computation time as Ceres can reuse expensive objects to create.
// The context object can be nullptr, in which case Ceres may create one.
//
// Ceres does NOT take ownership of the pointer.
Context* context = nullptr;
// Using this callback interface, Ceres can notify you when it is about to
// evaluate the residuals or jacobians. With the callback, you can share
// computation between residual blocks by doing the shared computation in
// EvaluationCallback::PrepareForEvaluation() before Ceres calls
// CostFunction::Evaluate(). It also enables caching results between a pure
// residual evaluation and a residual & jacobian evaluation.
//
// Problem DOES NOT take ownership of the callback.
//
// NOTE: Evaluation callbacks are incompatible with inner iterations. So
// calling Solve with Solver::Options::use_inner_iterations = true on a
// Problem with a non-null evaluation callback is an error.
EvaluationCallback* evaluation_callback = nullptr;
};
// The default constructor is equivalent to the invocation
// Problem(Problem::Options()).
Problem();
explicit Problem(const Options& options);
Problem(Problem&&);
Problem& operator=(Problem&&);
Problem(const Problem&) = delete;
Problem& operator=(const Problem&) = delete;
~Problem();
// Add a residual block to the overall cost function. The cost function
// carries with its information about the sizes of the parameter blocks it
// expects. The function checks that these match the sizes of the parameter
// blocks listed in parameter_blocks. The program aborts if a mismatch is
// detected. loss_function can be nullptr, in which case the cost of the term
// is just the squared norm of the residuals.
//
// The user has the option of explicitly adding the parameter blocks using
// AddParameterBlock. This causes additional correctness checking; however,
// AddResidualBlock implicitly adds the parameter blocks if they are not
// present, so calling AddParameterBlock explicitly is not required.
//
// The Problem object by default takes ownership of the cost_function and
// loss_function pointers (See Problem::Options to override this behaviour).
// These objects remain live for the life of the Problem object. If the user
// wishes to keep control over the destruction of these objects, then they can
// do this by setting the corresponding enums in the Options struct.
//
// Note: Even though the Problem takes ownership of cost_function and
// loss_function, it does not preclude the user from re-using them in another
// residual block. The destructor takes care to call delete on each
// cost_function or loss_function pointer only once, regardless of how many
// residual blocks refer to them.
//
// Example usage:
//
// double x1[] = {1.0, 2.0, 3.0};
// double x2[] = {1.0, 2.0, 5.0, 6.0};
// double x3[] = {3.0, 6.0, 2.0, 5.0, 1.0};
//
// Problem problem;
//
// problem.AddResidualBlock(new MyUnaryCostFunction(...), nullptr, x1);
// problem.AddResidualBlock(new MyBinaryCostFunction(...), nullptr, x2, x1);
//
// Add a residual block by listing the parameter block pointers directly
// instead of wapping them in a container.
template <typename... Ts>
ResidualBlockId AddResidualBlock(CostFunction* cost_function,
LossFunction* loss_function,
double* x0,
Ts*... xs) {
const std::array<double*, sizeof...(Ts) + 1> parameter_blocks{{x0, xs...}};
return AddResidualBlock(cost_function,
loss_function,
parameter_blocks.data(),
static_cast<int>(parameter_blocks.size()));
}
// Add a residual block by providing a vector of parameter blocks.
ResidualBlockId AddResidualBlock(
CostFunction* cost_function,
LossFunction* loss_function,
const std::vector<double*>& parameter_blocks);
// Add a residual block by providing a pointer to the parameter block array
// and the number of parameter blocks.
ResidualBlockId AddResidualBlock(CostFunction* cost_function,
LossFunction* loss_function,
double* const* const parameter_blocks,
int num_parameter_blocks);
// Add a parameter block with appropriate size to the problem. Repeated calls
// with the same arguments are ignored. Repeated calls with the same double
// pointer but a different size will result in a crash.
void AddParameterBlock(double* values, int size);
// Add a parameter block with appropriate size and Manifold to the
// problem. It is okay for manifold to be nullptr.
//
// Repeated calls with the same arguments are ignored. Repeated calls
// with the same double pointer but a different size results in a crash
// (unless Solver::Options::disable_all_safety_checks is set to true).
//
// Repeated calls with the same double pointer and size but different Manifold
// is equivalent to calling SetManifold(manifold), i.e., any previously
// associated Manifold object will be replaced with the manifold.
void AddParameterBlock(double* values, int size, Manifold* manifold);
// Remove a parameter block from the problem. The Manifold of the parameter
// block, if it exists, will persist until the deletion of the problem
// (similar to cost/loss functions in residual block removal). Any residual
// blocks that depend on the parameter are also removed, as described above
// in RemoveResidualBlock().
//
// If Problem::Options::enable_fast_removal is true, then the removal is fast
// (almost constant time). Otherwise, removing a parameter block will incur a
// scan of the entire Problem object.
//
// WARNING: Removing a residual or parameter block will destroy the implicit
// ordering, rendering the jacobian or residuals returned from the solver
// uninterpretable. If you depend on the evaluated jacobian, do not use
// remove! This may change in a future release.
void RemoveParameterBlock(const double* values);
// Remove a residual block from the problem. Any parameters that the residual
// block depends on are not removed. The cost and loss functions for the
// residual block will not get deleted immediately; won't happen until the
// problem itself is deleted.
//
// WARNING: Removing a residual or parameter block will destroy the implicit
// ordering, rendering the jacobian or residuals returned from the solver
// uninterpretable. If you depend on the evaluated jacobian, do not use
// remove! This may change in a future release.
void RemoveResidualBlock(ResidualBlockId residual_block);
// Hold the indicated parameter block constant during optimization.
void SetParameterBlockConstant(const double* values);
// Allow the indicated parameter block to vary during optimization.
void SetParameterBlockVariable(double* values);
// Returns true if a parameter block is set constant, and false otherwise. A
// parameter block may be set constant in two ways: either by calling
// SetParameterBlockConstant or by associating a Manifold with a zero
// dimensional tangent space with it.
bool IsParameterBlockConstant(const double* values) const;
// Set the Manifold for the parameter block. Calling SetManifold with nullptr
// will clear any previously set Manifold for the parameter block.
//
// Repeated calls will result in any previously associated Manifold object to
// be replaced with the manifold.
//
// The manifold is owned by the Problem by default (See Problem::Options to
// override this behaviour).
//
// It is acceptable to set the same Manifold for multiple parameter blocks.
void SetManifold(double* values, Manifold* manifold);
// Get the Manifold object associated with this parameter block.
//
// If there is no Manifold object associated then nullptr is returned.
const Manifold* GetManifold(const double* values) const;
// Returns true if a Manifold is associated with this parameter block, false
// otherwise.
bool HasManifold(const double* values) const;
// Set the lower/upper bound for the parameter at position "index".
void SetParameterLowerBound(double* values, int index, double lower_bound);
void SetParameterUpperBound(double* values, int index, double upper_bound);
// Get the lower/upper bound for the parameter at position "index". If the
// parameter is not bounded by the user, then its lower bound is
// -std::numeric_limits<double>::max() and upper bound is
// std::numeric_limits<double>::max().
double GetParameterLowerBound(const double* values, int index) const;
double GetParameterUpperBound(const double* values, int index) const;
// Number of parameter blocks in the problem. Always equals
// parameter_blocks().size() and parameter_block_sizes().size().
int NumParameterBlocks() const;
// The size of the parameter vector obtained by summing over the sizes of all
// the parameter blocks.
int NumParameters() const;
// Number of residual blocks in the problem. Always equals
// residual_blocks().size().
int NumResidualBlocks() const;
// The size of the residual vector obtained by summing over the sizes of all
// of the residual blocks.
int NumResiduals() const;
// The size of the parameter block.
int ParameterBlockSize(const double* values) const;
// The dimension of the tangent space of the Manifold for the parameter block.
// If there is no Manifold associated with this parameter block, then
// ParameterBlockTangentSize = ParameterBlockSize.
int ParameterBlockTangentSize(const double* values) const;
// Is the given parameter block present in this problem or not?
bool HasParameterBlock(const double* values) const;
// Fills the passed parameter_blocks vector with pointers to the parameter
// blocks currently in the problem. After this call, parameter_block.size() ==
// NumParameterBlocks.
void GetParameterBlocks(std::vector<double*>* parameter_blocks) const;
// Fills the passed residual_blocks vector with pointers to the residual
// blocks currently in the problem. After this call, residual_blocks.size() ==
// NumResidualBlocks.
void GetResidualBlocks(std::vector<ResidualBlockId>* residual_blocks) const;
// Get all the parameter blocks that depend on the given residual block.
void GetParameterBlocksForResidualBlock(
const ResidualBlockId residual_block,
std::vector<double*>* parameter_blocks) const;
// Get the CostFunction for the given residual block.
const CostFunction* GetCostFunctionForResidualBlock(
const ResidualBlockId residual_block) const;
// Get the LossFunction for the given residual block. Returns nullptr
// if no loss function is associated with this residual block.
const LossFunction* GetLossFunctionForResidualBlock(
const ResidualBlockId residual_block) const;
// Get all the residual blocks that depend on the given parameter block.
//
// If Problem::Options::enable_fast_removal is true, then getting the residual
// blocks is fast and depends only on the number of residual
// blocks. Otherwise, getting the residual blocks for a parameter block will
// incur a scan of the entire Problem object.
void GetResidualBlocksForParameterBlock(
const double* values,
std::vector<ResidualBlockId>* residual_blocks) const;
// Options struct to control Problem::Evaluate.
struct EvaluateOptions {
// The set of parameter blocks for which evaluation should be
// performed. This vector determines the order that parameter blocks occur
// in the gradient vector and in the columns of the jacobian matrix. If
// parameter_blocks is empty, then it is assumed to be equal to vector
// containing ALL the parameter blocks. Generally speaking the parameter
// blocks will occur in the order in which they were added to the
// problem. But, this may change if the user removes any parameter blocks
// from the problem.
//
// NOTE: This vector should contain the same pointers as the ones used to
// add parameter blocks to the Problem. These parameter block should NOT
// point to new memory locations. Bad things will happen otherwise.
std::vector<double*> parameter_blocks;
// The set of residual blocks to evaluate. This vector determines the order
// in which the residuals occur, and how the rows of the jacobian are
// ordered. If residual_blocks is empty, then it is assumed to be equal to
// the vector containing ALL the residual blocks. Generally speaking the
// residual blocks will occur in the order in which they were added to the
// problem. But, this may change if the user removes any residual blocks
// from the problem.
std::vector<ResidualBlockId> residual_blocks;
// Even though the residual blocks in the problem may contain loss
// functions, setting apply_loss_function to false will turn off the
// application of the loss function to the output of the cost function. This
// is of use for example if the user wishes to analyse the solution quality
// by studying the distribution of residuals before and after the solve.
bool apply_loss_function = true;
int num_threads = 1;
};
// Evaluate Problem. Any of the output pointers can be nullptr. Which residual
// blocks and parameter blocks are used is controlled by the EvaluateOptions
// struct above.
//
// Note 1: The evaluation will use the values stored in the memory locations
// pointed to by the parameter block pointers used at the time of the
// construction of the problem. i.e.,
//
// Problem problem;
// double x = 1;
// problem.AddResidualBlock(new MyCostFunction, nullptr, &x);
//
// double cost = 0.0;
// problem.Evaluate(Problem::EvaluateOptions(), &cost,
// nullptr, nullptr, nullptr);
//
// The cost is evaluated at x = 1. If you wish to evaluate the problem at x =
// 2, then
//
// x = 2;
// problem.Evaluate(Problem::EvaluateOptions(), &cost,
// nullptr, nullptr, nullptr);
//
// is the way to do so.
//
// Note 2: If no Manifolds are used, then the size of the gradient vector (and
// the number of columns in the jacobian) is the sum of the sizes of all the
// parameter blocks. If a parameter block has a Manifold, then it contributes
// "TangentSize" entries to the gradient vector (and the number of columns in
// the jacobian).
//
// Note 3: This function cannot be called while the problem is being solved,
// for example it cannot be called from an IterationCallback at the end of an
// iteration during a solve.
//
// Note 4: If an EvaluationCallback is associated with the problem, then its
// PrepareForEvaluation method will be called every time this method is called
// with new_point = true.
bool Evaluate(const EvaluateOptions& options,
double* cost,
std::vector<double>* residuals,
std::vector<double>* gradient,
CRSMatrix* jacobian);
// Evaluates the residual block, storing the scalar cost in *cost, the
// residual components in *residuals, and the jacobians between the parameters
// and residuals in jacobians[i], in row-major order.
//
// If residuals is nullptr, the residuals are not computed.
//
// If jacobians is nullptr, no Jacobians are computed. If jacobians[i] is
// nullptr, then the Jacobian for that parameter block is not computed.
//
// It is not okay to request the Jacobian w.r.t a parameter block that is
// constant.
//
// The return value indicates the success or failure. Even if the function
// returns false, the caller should expect the output memory locations to have
// been modified.
//
// The returned cost and jacobians have had robustification and Manifold
// applied already; for example, the jacobian for a 4-dimensional quaternion
// parameter using the "QuaternionParameterization" is num_residuals by 3
// instead of num_residuals by 4.
//
// apply_loss_function as the name implies allows the user to switch the
// application of the loss function on and off.
//
// If an EvaluationCallback is associated with the problem, then its
// PrepareForEvaluation method will be called every time this method is called
// with new_point = true. This conservatively assumes that the user may have
// changed the parameter values since the previous call to evaluate / solve.
// For improved efficiency, and only if you know that the parameter values
// have not changed between calls, see
// EvaluateResidualBlockAssumingParametersUnchanged().
bool EvaluateResidualBlock(ResidualBlockId residual_block_id,
bool apply_loss_function,
double* cost,
double* residuals,
double** jacobians) const;
// Same as EvaluateResidualBlock except that if an EvaluationCallback is
// associated with the problem, then its PrepareForEvaluation method will be
// called every time this method is called with new_point = false.
//
// This means, if an EvaluationCallback is associated with the problem then it
// is the user's responsibility to call PrepareForEvaluation before calling
// this method if necessary, i.e. iff the parameter values have been changed
// since the last call to evaluate / solve.'
//
// This is because, as the name implies, we assume that the parameter blocks
// did not change since the last time PrepareForEvaluation was called (via
// Solve, Evaluate or EvaluateResidualBlock).
bool EvaluateResidualBlockAssumingParametersUnchanged(
ResidualBlockId residual_block_id,
bool apply_loss_function,
double* cost,
double* residuals,
double** jacobians) const;
// Returns reference to the options with which the Problem was constructed.
const Options& options() const;
// Returns pointer to Problem implementation
internal::ProblemImpl* mutable_impl();
private:
std::unique_ptr<internal::ProblemImpl> impl_;
};
} // namespace ceres
#include "ceres/internal/reenable_warnings.h"
#endif // CERES_PUBLIC_PROBLEM_H_