QLNet.Problem.functionValue C# (CSharp) Метод

        public double functionValue()
        {
            return functionValue_.GetValueOrDefault();
        }

        //! Perform line search
        public override double value(Problem P, ref EndCriteria.Type ecType, EndCriteria endCriteria, double t_ini)
        {
            //OptimizationMethod& method = P.method();
            Constraint constraint = P.constraint();

            succeed_ = true;
            bool   maxIter = false;
            double qtold;
            double t          = t_ini;
            int    loopNumber = 0;

            double q0  = P.functionValue();
            double qp0 = P.gradientNormValue();

            qt_  = q0;
            qpt_ = (gradient_.Count == 0) ? qp0 : -Vector.DotProduct(gradient_, searchDirection_);

            // Initialize gradient
            gradient_ = new Vector(P.currentValue().Count);
            // Compute new point
            xtd_ = (Vector)P.currentValue().Clone();
            t    = update(ref xtd_, searchDirection_, t, constraint);
            // Compute function value at the new point
            qt_ = P.value(xtd_);

            // Enter in the loop if the criterion is not satisfied
            if ((qt_ - q0) > -alpha_ * t * qpt_)
            {
                do
                {
                    loopNumber++;
                    // Decrease step
                    t *= beta_;
                    // Store old value of the function
                    qtold = qt_;
                    // New point value
                    xtd_ = P.currentValue();
                    t    = update(ref xtd_, searchDirection_, t, constraint);

                    // Compute function value at the new point
                    qt_ = P.value(xtd_);
                    P.gradient(gradient_, xtd_);
                    // and it squared norm
                    maxIter = endCriteria.checkMaxIterations(loopNumber, ref ecType);
                } while ((((qt_ - q0) > (-alpha_ * t * qpt_)) || ((qtold - q0) <= (-alpha_ * t * qpt_ / beta_))) && (!maxIter));
            }

            if (maxIter)
            {
                succeed_ = false;
            }

            // Compute new gradient
            P.gradient(gradient_, xtd_);
            // and it squared norm
            qpt_ = Vector.DotProduct(gradient_, gradient_);

            // Return new step value
            return(t);
        }