public Vector CalculateIteration()
{
calc_u = DifferentialEquation.GetSolutionTask1(manage_p, manage_f, phi, nu, L, T, aa, GRID_SIZE, TIME_SIZE);
Matrix KSI = DifferentialEquation.GetSolutionTask2(y, calc_u, nu, L, T, aa, GRID_SIZE, TIME_SIZE);
Vector ksi_l = new Vector(TIME_SIZE);
for (int i = 0; i < TIME_SIZE; i++)
{
ksi_l[i] = KSI[i, GRID_SIZE - 1];
}
double alpha = alpha_old;
double tau = MyMath.GetStep(TIME_SIZE, 0d, T);
double h = MyMath.GetStep(GRID_SIZE, 0d, L);
switch (pickAlpha)
{
case PickAlpha.Lipshiz:
alpha = ChooseAlpha_LipMethod();
break;
case PickAlpha.Divide:
if (ITERATION > 0)
{
alpha = ChooseAlpha_DivideMethod(alpha_old);
}
break;
case PickAlpha.Lipshiz_CG:
if (ITERATION > 0)
{
alpha = ChooseAplpha_LipMethodForConditionalGradient(KSI, manage_f, alpha, tau, h);
}
//alpha = 1d / (ITERATION + 1);
break;
case PickAlpha.SUM:
alpha = ChooseAlpha_Sum();
break;
case PickAlpha.Projection:
alpha = ChooseAlpha_Projection(KSI, manage_f, alpha, R, tau, h);
break;
default:
break;
}
manage_p = DifferentialEquation.GrdientProjection(manage_p, ksi_l, alpha, aa, P_MIN, P_MAX);
//manage_f = DifferentialEquation.GrdientProjectionByF(manage_f, KSI, alpha, R, h, tau);
ITERATION++;
calc_u_old = calc_u;
alpha_old = alpha;
return(manage_p);
}
public double ChooseAlpha_Projection(Matrix KSI, Matrix f, double alpha, double R, double tau, double h)
{
Vector u_temp = new Vector();
double EPS = 0.001d;
double NORM = 0;
do
{
Matrix f_new = DifferentialEquation.GrdientProjectionByF(f, KSI, alpha, R, h, tau);
f_new = Pu(f_new, R, h, tau);
u_temp = DifferentialEquation.GetSolutionTask1(manage_p, f_new, phi, nu, L, T, aa, GRID_SIZE, TIME_SIZE);
NORM = Calculate_L2NORM(f - f_new, h, tau);
alpha = alpha / 2.0;
} while (Functional_J(calc_u) - Functional_J(u_temp) < EPS * NORM * NORM);
return(alpha * 2.0);
}
