QLNet.Problem.setGradientNormValue C# (CSharp) Метод

        public void setGradientNormValue(double squaredNorm)
        {
            squaredNorm_ = squaredNorm;
        }

        //! minimize the optimization problem P
        public override EndCriteria.Type minimize(Problem P, EndCriteria endCriteria)
        {
            EndCriteria.Type ecType = EndCriteria.Type.None;
            P.reset();
            Vector x_ = P.currentValue();
            int    iterationNumber_ = 0;
            int    stationaryStateIterationNumber_ = 0;

            lineSearch_.searchDirection = new Vector(x_.Count);
            bool end;

            // function and squared norm of gradient values;
            double normdiff;
            // classical initial value for line-search step
            double t = 1.0;
            // Set gold at the size of the optimization problem search direction
            Vector gold  = new Vector(lineSearch_.searchDirection.Count);
            Vector gdiff = new Vector(lineSearch_.searchDirection.Count);

            P.setFunctionValue(P.valueAndGradient(gold, x_));
            lineSearch_.searchDirection = gold * -1.0;
            P.setGradientNormValue(Vector.DotProduct(gold, gold));
            normdiff = Math.Sqrt(P.gradientNormValue());

            do
            {
                // Linesearch
                t = lineSearch_.value(P, ref ecType, endCriteria, t);

                if (!(lineSearch_.succeed()))
                {
                    throw new ApplicationException("line-search failed!");
                }

                // End criteria
                // FIXME: it's never been used! ???
                // , normdiff
                end = endCriteria.value(iterationNumber_, ref stationaryStateIterationNumber_, true, P.functionValue(), Math.Sqrt(P.gradientNormValue()), lineSearch_.lastFunctionValue(), Math.Sqrt(lineSearch_.lastGradientNorm2()), ref ecType);

                // Updates
                // New point
                x_ = lineSearch_.lastX();
                // New function value
                P.setFunctionValue(lineSearch_.lastFunctionValue());
                // New gradient and search direction vectors
                gdiff    = gold - lineSearch_.lastGradient();
                normdiff = Math.Sqrt(Vector.DotProduct(gdiff, gdiff));
                gold     = lineSearch_.lastGradient();
                lineSearch_.searchDirection = gold * -1.0;
                // New gradient squared norm
                P.setGradientNormValue(lineSearch_.lastGradientNorm2());

                // Increase interation number
                ++iterationNumber_;
            } while (end == false);

            P.setCurrentValue(x_);
            return(ecType);
        }