예제 #1
0
        public static T FindMin(IFunctional <T> functional, IParametricFunction <T> function, IVector <T> s, IVector <T> p, T eps)
        {
            var la = LinearAlgebra.Value;

            var a = la.Cast(0);
            var b = la.Cast(1e2);
            var x = la.Sum(a, la.Mult(la.Mult(la.Cast(0.5), la.Sub(la.Cast(3), la.Sqrt(la.Cast(5.0)))), (la.Sub(b, a))));
            var y = la.Sum(la.Sub(b, x), a);

            var fx     = function.Bind(s.AddWithCloning(p.MultWithCloning(la.Mult(x, la.Cast(-1)))));
            var fy     = function.Bind(s.AddWithCloning(p.MultWithCloning(la.Mult(y, la.Cast(-1)))));
            var valueX = functional.Value(fx);

            var valueY = functional.Value(fy);

            while (la.Compare(la.Abs(la.Sub(b, a)), la.Cast(1e-5)) == 1)
            {
                if (la.Compare(valueX, valueY) == -1)
                {
                    b      = y;
                    y      = x;
                    fy     = fx;
                    valueY = valueX;
                    x      = la.Sub(la.Sum(b, a), y);
                    fx     = function.Bind(s.AddWithCloning(p.MultWithCloning(la.Mult(x, la.Cast(-1)))));
                    valueX = functional.Value(fx);
                }
                else
                {
                    a      = x;
                    x      = y;
                    fx     = fy;
                    valueX = valueY;
                    y      = la.Sub(la.Sum(b, a), x);
                    fy     = function.Bind(s.AddWithCloning(p.MultWithCloning(la.Mult(y, la.Cast(-1)))));
                    valueY = functional.Value(fy);
                }
            }
            return(la.Div(la.Sum(a, b), la.Cast(2)));
        }
예제 #2
0
        public IVector <T> Minimize(IFunctional <T> objective, IParametricFunction <T> function, IVector <T> initialParameters, IVector <T> minimumParameters = default,
                                    IVector <T> maximumParameters = default)
        {
            var         k     = 0;
            var         la    = LinearAlgebra.Value;
            IVector <T> xPrev = initialParameters.Clone() as IVector <T>;
            IVector <T> xNew  = initialParameters.Clone() as IVector <T>;

            var normalDist = new Normal(Mean, StdDev);
            T   prevValue  = objective.Value(function.Bind(xPrev));

            do
            {
                var t = 20d / Math.Log(k, Math.E);

                for (int i = 0; i < xPrev.Count; i++)
                {
                    var nR = normalDist.Sample() * t;
                    xNew[i] = la.Sum(xPrev[i], la.Cast(nR));
                }

                this.ApplyMinimumAndMaximumValues(minimumParameters, maximumParameters, xNew, la);

                var newValue = objective.Value(function.Bind(xNew));

                var sub = la.Sub(newValue, prevValue);

                if (la.Compare(sub, la.GetZeroValue()) == -1) // || la.Exp(la.Mult(la.Cast(-1/t), sub)) >= rand.NextDouble())
                {
                    prevValue = newValue;
                    xPrev     = xNew.Clone() as IVector <T>;
                }
            } while ((MaxIter.HasValue && MaxIter > k++ && la.Compare(prevValue, Eps) == 1) || (!MaxIter.HasValue && la.Compare(prevValue, Eps) == 1));

            return(xPrev);
        }