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
- {
- 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(Mat z_in):z(z_in){}
- ~LPFunction(){};
- const 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
- {
- 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(Mat A_in, Mat b_in):A(A_in),b(b_in){}
- const Mat& getA()const{return A;}
- const Mat& getb()const{return b;}
- };
-
- LPSolver(){}
- double solve(const Function& F,const Constraints& C, OutputArray result)const;
-};
-
//!the return codes for solveLP() function
enum
{
#endif
}
-double LPSolver::solve(const Function& F,const Constraints& C, OutputArray result)const{
- return 0.0;
-}
-
-double LPSolver::LPFunction::calc(InputArray args)const{
- dprintf("call to LPFunction::calc()\n");
- return 0.0;
-}
-
-
void const print_matrix(const Mat& X){
#ifdef ALEX_DEBUG
dprintf("\ttype:%d vs %d,\tsize: %d-on-%d\n",X.type(),CV_64FC1,X.rows,X.cols);
}
}
}}
-/*FIXME (possible optimizations)
- * use iterator-style (as in ddc0010e7... commit version of this file)
- * remove calls to pivot inside the while-loops
- */
//ASSERT_EQ(res,1);
}
}
-
-//TODO
-// get optimal solution from initial (0,0,...,0) - DONE
-// milestone: pass first test (wo initial solution) - DONE
- //
- // ??how_check_multiple_solutions & pass_test - DONE
- // Blands_rule - DONE
- // (assert, assign) - DONE
- //
- // (&1tests on cycling)
- // make_more_clear
- // wrap in OOP
- //
- // non-trivial tests
- // pull-request
- //
- // study hill and other algos
- //
-// ??how to get smallest l2 norm
-// FUTURE: compress&debug-> more_tests(Cormen) -> readNumRecipes-> fast&stable || hill_climbing