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#ifndef __OPENCV_OPTIM_HPP__
#define __OPENCV_OPTIM_HPP__

#include "opencv2/core.hpp"

namespace cv{namespace optim
{
class CV_EXPORTS Solver : public Algorithm
{
public:
    class CV_EXPORTS Function
    {
    public:
       virtual ~Function() {}
       virtual double calc(const double* x) const = 0;
       virtual void getGradient(const double* /*x*/,double* /*grad*/) {}
    };

    virtual Ptr<Function> getFunction() const = 0;
    virtual void setFunction(const Ptr<Function>& f) = 0;

    virtual TermCriteria getTermCriteria() const = 0;
    virtual void setTermCriteria(const TermCriteria& termcrit) = 0;

    // x contain the initial point before the call and the minima position (if algorithm converged) after. x is assumed to be (something that
    // after getMat() will return) row-vector or column-vector. *It's size  and should
    // be consisted with previous dimensionality data given, if any (otherwise, it determines dimensionality)*
    virtual double minimize(InputOutputArray x) = 0;
};

//! downhill simplex class
class CV_EXPORTS DownhillSolver : public Solver
{
public:
    //! returns row-vector, even if the column-vector was given
    virtual void getInitStep(OutputArray step) const=0;
    //!This should be called at least once before the first call to minimize() and step is assumed to be (something that
    //! after getMat() will return) row-vector or column-vector. *It's dimensionality determines the dimensionality of a problem.*
    virtual void setInitStep(InputArray step)=0;
};

// both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does.
CV_EXPORTS_W Ptr<DownhillSolver> createDownhillSolver(const Ptr<Solver::Function>& f=Ptr<Solver::Function>(),
        InputArray initStep=Mat_<double>(1,1,0.0),
        TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));

//! conjugate gradient method
class CV_EXPORTS ConjGradSolver : public Solver{
};

CV_EXPORTS_W Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f=Ptr<ConjGradSolver::Function>(),
        TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));

//!the return codes for solveLP() function
enum
{
    SOLVELP_UNBOUNDED    = -2, //problem is unbounded (target function can achieve arbitrary high values)
    SOLVELP_UNFEASIBLE    = -1, //problem is unfeasible (there are no points that satisfy all the constraints imposed)
    SOLVELP_SINGLE    = 0, //there is only one maximum for target function
    SOLVELP_MULTI    = 1 //there are multiple maxima for target function - the arbitrary one is returned
};

CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z);
CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
}}// cv

#endif