public static void sparse_grid_laguerre(int dim_num, int level_max, int point_num, ref double[] grid_weight, ref double[] grid_point) //****************************************************************************80 // // Purpose: // // SPARSE_GRID_LAGUERRE computes a sparse grid of Gauss-Laguerre points. // // Discussion: // // The quadrature rule is associated with a sparse grid derived from // a Smolyak construction using a 1D Gauss-Laguerre quadrature rule. // // The user specifies: // * the spatial dimension of the quadrature region, // * the level that defines the Smolyak grid. // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 05 July 2008 // // Author: // // John Burkardt // // Reference: // // Fabio Nobile, Raul Tempone, Clayton Webster, // A Sparse Grid Stochastic Collocation Method for Partial Differential // Equations with Random Input Data, // SIAM Journal on Numerical Analysis, // Volume 46, Number 5, 2008, pages 2309-2345. // // Parameters: // // Input, int DIM_NUM, the spatial dimension. // // Input, int LEVEL_MAX, controls the size of the final sparse grid. // // Input, int POINT_NUM, the number of points in the grid, as determined // by SPARSE_GRID_LAGUERRE_SIZE. // // Output, double GRID_WEIGHT[POINT_NUM], the weights. // // Output, double GRID_POINT[DIM_NUM*POINT_NUM], the points. // { int level; int point; for (point = 0; point < point_num; point++) { grid_weight[point] = 0.0; } // // The outer loop generates LEVELs from LEVEL_MIN to LEVEL_MAX. // int point_num2 = 0; int level_min = Math.Max(0, level_max + 1 - dim_num); int[] grid_base2 = new int[dim_num]; int[] level_1d = new int[dim_num]; int[] order_1d = new int[dim_num]; for (level = level_min; level <= level_max; level++) { // // The middle loop generates the next partition LEVEL_1D(1:DIM_NUM) // that adds up to LEVEL. // bool more = false; int h = 0; int t = 0; for (;;) { Comp.comp_next(level, dim_num, ref level_1d, ref more, ref h, ref t); // // Transform each 1D level to a corresponding 1D order. // The relationship is the same as for other OPEN rules. // ClenshawCurtis.level_to_order_open(dim_num, level_1d, ref order_1d); int dim; for (dim = 0; dim < dim_num; dim++) { grid_base2[dim] = order_1d[dim]; } // // The product of the 1D orders gives us the number of points in this grid. // int order_nd = typeMethods.i4vec_product(dim_num, order_1d); // // Compute the weights for this product grid. // double[] grid_weight2 = QuadratureRule.product_weight_laguerre(dim_num, order_1d, order_nd); // // Now determine the coefficient of the weight. // int coeff = (int)(Math.Pow(-1, level_max - level) * Binomial.choose(dim_num - 1, level_max - level)); // // The inner (hidden) loop generates all points corresponding to given grid. // The grid indices will be between -M to +M, where 2*M + 1 = ORDER_1D(DIM). // int[] grid_index2 = Multigrid.multigrid_index_one(dim_num, order_1d, order_nd); for (point = 0; point < order_nd; point++) { QuadratureRule.laguerre_abscissa(dim_num, 1, grid_index2, grid_base2, ref grid_point, gridIndex: +point * dim_num, gridPointIndex: +point_num2 * dim_num); grid_weight[point_num2] = coeff * grid_weight2[point]; point_num2 += 1; } if (!more) { break; } } } }