Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
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// This software is provided by the copyright holders and contributors "as is" and
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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//M*/
#ifndef __OPENCV_OPTIM_HPP__
#define __OPENCV_OPTIM_HPP__
#include <iostream>
#include "opencv2/core.hpp"
#include "opencv2/core/mat.hpp"
/*! \namespace cv
Namespace where all the C++ OpenCV functionality resides
*/
namespace cv{namespace optim
{
//! generic class for optimization algorithms */
class CV_EXPORTS Solver : public Algorithm /* Algorithm is the base OpenCV class */
{
public:
class CV_EXPORTS Function
{
public:
virtual ~Function(){}
virtual double calc(InputArray args) const = 0;
};
class CV_EXPORTS Constraints
{
public:
virtual ~Constraints(){}
};
//! could be reused for all the generic algorithms like downhill simplex. Return value is the maximum value of a function*/
virtual double solve(const Function& F,const Constraints& C, OutputArray result) const = 0;
/*virtual void setTermCriteria(const TermCriteria& criteria) = 0;
virtual TermCriteria getTermCriteria() = 0;*/
// more detailed API to be defined later ...
};
class CV_EXPORTS LPSolver : public Solver
{
public:
class CV_EXPORTS LPFunction:public Solver::Function
{
cv::Mat z;
public:
//! Note, that this class is supposed to be immutable, so it's ok to make only a shallow copy of z_in.*/
LPFunction(cv::Mat z_in):z(z_in){}
~LPFunction(){};
const cv::Mat& getz()const{return z;}
double calc(InputArray args)const;
};
//!This class represents constraints for linear problem. There are two matrix stored: m-by-n matrix A and n-by-1 column-vector b.
//!What this represents is the set of constraints Ax\leq b and x\geq 0. It can be shown that any set of linear constraints can be converted
//!this form and **we shall create various constructors for this class that will perform these conversions**.
class CV_EXPORTS LPConstraints:public Solver::Constraints
{
cv::Mat A,b;
public:
~LPConstraints(){};
//! Note, that this class is supposed to be immutable, so it's ok to make only a shallow copy of A_in and b_in.*/
LPConstraints(cv::Mat A_in, cv::Mat b_in):A(A_in),b(b_in){}
const cv::Mat& getA()const{return A;}
const cv::Mat& getb()const{return b;}
};
LPSolver(){}
double solve(const Function& F,const Constraints& C, OutputArray result)const;
};
}}// cv
#endif