diff --git a/modules/optim/doc/conjugate_gradient.rst b/modules/optim/doc/conjugate_gradient.rst
new file mode 100644
index 000000000..cd9697465
--- /dev/null
+++ b/modules/optim/doc/conjugate_gradient.rst
@@ -0,0 +1,11 @@
+Conjugate Gradient
+=======================
+
+.. highlight:: cpp
+
+optim::ConjGradSolver
+---------------------------------
+
+.. ocv:class:: optim::ConjGradSolver
+
+This class is used
diff --git a/modules/optim/include/opencv2/optim.hpp b/modules/optim/include/opencv2/optim.hpp
index c1e7819b6..0a460cce0 100644
--- a/modules/optim/include/opencv2/optim.hpp
+++ b/modules/optim/include/opencv2/optim.hpp
@@ -55,6 +55,7 @@ public:
     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;
@@ -86,17 +87,7 @@ CV_EXPORTS_W Ptr<DownhillSolver> createDownhillSolver(const Ptr<Solver::Function
         TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
 
 //! conjugate gradient method
-class CV_EXPORTS ConjGradSolver : public Solver
-{
-public:
-    class CV_EXPORTS Function : public Solver::Function
-    {
-    public:
-       //! gradient is like the first derivative for multivar function
-       virtual void getGradient(const double* x,double* buf)const=0;
-       //! Jacobian is like the second derivative
-       virtual void getJacobian(const double* x)const=0;
-    };
+class CV_EXPORTS ConjGradSolver : public Solver{
 };
 
 CV_EXPORTS_W Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f=Ptr<ConjGradSolver::Function>(),
diff --git a/modules/optim/src/conjugate_gradient.cpp b/modules/optim/src/conjugate_gradient.cpp
new file mode 100644
index 000000000..7e555c6cc
--- /dev/null
+++ b/modules/optim/src/conjugate_gradient.cpp
@@ -0,0 +1,77 @@
+#include "precomp.hpp"
+#include "debug.hpp"
+
+namespace cv{namespace optim{
+
+    class ConjGradSolverImpl : public ConjGradSolver
+    {
+    public:
+        Ptr<Function> getFunction() const;
+        void setFunction(const Ptr<Function>& f);
+        TermCriteria getTermCriteria() const;
+        ConjGradSolverImpl();
+        void setTermCriteria(const TermCriteria& termcrit);
+        double minimize(InputOutputArray x);
+    protected:
+        Ptr<Solver::Function> _Function;
+        TermCriteria _termcrit;
+        Mat_<double> d,r,buf_x,r_old;
+    private:
+    };
+
+    double ConjGradSolverImpl::minimize(InputOutputArray x){
+        CV_Assert(_Function.empty()==false);
+        dprintf(("termcrit:\n\ttype: %d\n\tmaxCount: %d\n\tEPS: %g\n",_termcrit.type,_termcrit.maxCount,_termcrit.epsilon));
+
+        Mat x_mat=x.getMat();
+        CV_Assert(MIN(x_mat.rows,x_mat.cols)==1);
+        int ndim=MAX(x_mat.rows,x_mat.cols);
+        CV_Assert(x_mat.type()==CV_64FC1);
+
+        d.create(1,ndim);
+        r.create(1,ndim);
+        r_old.create(1,ndim);
+
+        Mat_<double> proxy_x;
+        if(x_mat.rows>1){
+            buf_x.create(1,ndim);
+            Mat_<double> proxy(ndim,1,(double*)buf_x.data);
+            x_mat.copyTo(proxy);
+            proxy_x=buf_x;
+        }else{
+            proxy_x=x_mat;
+        }
+
+        //here everything goes. check that everything is setted properly
+
+        if(x_mat.rows>1){
+            Mat(ndim, 1, CV_64F, (double*)proxy_x.data).copyTo(x);
+        }
+        return 0.0;
+    }
+    ConjGradSolverImpl::ConjGradSolverImpl(){
+        _Function=Ptr<Function>();
+    }
+    Ptr<Solver::Function> ConjGradSolverImpl::getFunction()const{
+        return _Function;
+    }
+    void ConjGradSolverImpl::setFunction(const Ptr<Function>& f){
+        _Function=f;
+    }
+    TermCriteria ConjGradSolverImpl::getTermCriteria()const{
+        return _termcrit;
+    }
+    void ConjGradSolverImpl::setTermCriteria(const TermCriteria& termcrit){
+        CV_Assert((termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && termcrit.epsilon>0 && termcrit.maxCount>0) ||
+                ((termcrit.type==TermCriteria::MAX_ITER) && termcrit.maxCount>0));
+        _termcrit=termcrit;
+    }
+    // both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does.
+    Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f, TermCriteria termcrit){
+        ConjGradSolver *CG=new ConjGradSolverImpl();
+        CG->setFunction(f);
+        CG->setTermCriteria(termcrit);
+        return Ptr<ConjGradSolver>(CG);
+    }
+}}
+                               
diff --git a/modules/optim/src/simplex.cpp b/modules/optim/src/simplex.cpp
index f45d0ce0b..54de6ed8c 100644
--- a/modules/optim/src/simplex.cpp
+++ b/modules/optim/src/simplex.cpp
@@ -19,6 +19,8 @@ namespace cv{namespace optim{
         Ptr<Solver::Function> _Function;
         TermCriteria _termcrit;
         Mat _step;
+        Mat_<double> buf_x;
+
     private:
         inline void createInitialSimplex(Mat_<double>& simplex,Mat& step);
         inline double innerDownhillSimplex(cv::Mat_<double>& p,double MinRange,double MinError,int& nfunk,
@@ -209,7 +211,10 @@ namespace cv{namespace optim{
         Mat_<double> proxy_x;
 
         if(x_mat.rows>1){
-            proxy_x=x_mat.t();
+            buf_x.create(1,_step.cols);
+            Mat_<double> proxy(_step.cols,1,(double*)buf_x.data);
+            x_mat.copyTo(proxy);
+            proxy_x=buf_x;
         }else{
             proxy_x=x_mat;
         }
diff --git a/modules/optim/test/test_conjugate_gradient.cpp b/modules/optim/test/test_conjugate_gradient.cpp
new file mode 100644
index 000000000..2456a5cab
--- /dev/null
+++ b/modules/optim/test/test_conjugate_gradient.cpp
@@ -0,0 +1,61 @@
+#include "test_precomp.hpp"
+#include <cstdlib>
+
+static void mytest(cv::Ptr<cv::optim::ConjGradSolver> solver,cv::Ptr<cv::optim::Solver::Function> ptr_F,cv::Mat& x,
+        cv::Mat& etalon_x,double etalon_res){
+    solver->setFunction(ptr_F);
+    //int ndim=MAX(step.cols,step.rows);
+    double res=solver->minimize(x);
+    std::cout<<"res:\n\t"<<res<<std::endl;
+    std::cout<<"x:\n\t"<<x<<std::endl;
+    std::cout<<"etalon_res:\n\t"<<etalon_res<<std::endl;
+    std::cout<<"etalon_x:\n\t"<<etalon_x<<std::endl;
+    double tol=solver->getTermCriteria().epsilon;
+    ASSERT_TRUE(std::abs(res-etalon_res)<tol);
+    /*for(cv::Mat_<double>::iterator it1=x.begin<double>(),it2=etalon_x.begin<double>();it1!=x.end<double>();it1++,it2++){
+        ASSERT_TRUE(std::abs((*it1)-(*it2))<tol);
+    }*/
+    std::cout<<"--------------------------\n";
+}
+
+class SphereF:public cv::optim::Solver::Function{
+public:
+    double calc(const double* x)const{
+        return x[0]*x[0]+x[1]*x[1]+x[2]*x[2]+x[3]*x[3];
+    }
+    void getGradient(const double* x,double* grad){
+        for(int i=0;i<4;i++,grad++,x++){
+            grad[0]=2*x[0];
+        }
+    }
+};
+//TODO: test transp/usual x
+/*class RosenbrockF:public cv::optim::Solver::Function{
+    double calc(const double* x)const{
+        return 100*(x[1]-x[0]*x[0])*(x[1]-x[0]*x[0])+(1-x[0])*(1-x[0]);
+    }
+};*/
+
+TEST(Optim_ConjGrad, regression_basic){
+    cv::Ptr<cv::optim::ConjGradSolver> solver=cv::optim::createConjGradSolver();
+#if 1
+    {
+        cv::Ptr<cv::optim::Solver::Function> ptr_F(new SphereF());
+        cv::Mat x=(cv::Mat_<double>(1,2)<<1.0,1.0),
+            etalon_x=(cv::Mat_<double>(1,2)<<0.0,0.0);
+        double etalon_res=0.0;
+        return;
+        mytest(solver,ptr_F,x,etalon_x,etalon_res);
+    }
+#endif
+#if 0
+    {
+        cv::Ptr<cv::optim::Solver::Function> ptr_F(new RosenbrockF());
+        cv::Mat x=(cv::Mat_<double>(2,1)<<0.0,0.0),
+            step=(cv::Mat_<double>(2,1)<<0.5,+0.5),
+            etalon_x=(cv::Mat_<double>(2,1)<<1.0,1.0);
+        double etalon_res=0.0;
+        mytest(solver,ptr_F,x,step,etalon_x,etalon_res);
+    }
+#endif
+}