sandia_sgmg, a C++ code which constructs a sparse grid whose factors are possibly distinct 1D quadrature rules.
sandia_sgmg is a variant of the sparse_grid_mixed_growth() library. It has the same capabilities as that library, but it uses a different interface to the sandia_rules() routines which compute points and weights of quadrature rules. This interface is supplied by a small number of functions in the sandia_rules2() library.
Instead of passing the parameters needed by some of those rules as function arguments, these values are made available indirectly. This library implements that indirect storage using a function called parameter, which must therefore be supplied by the user as part of every program that calls the library. Refer to the source code of the example programs to see how parameter is defined and used.
For each spatial dimension, the user controls the routines that determine the order of the 1D quadrature rule, and that evaluate the quadrature points and weights. This information is supplied in arrays of pointers to functions. Many common functions for determining the order, points and weights are available in the SANDIA_RULES library.
The 1D quadrature rules are designed to approximate an integral of the form:
Integral ( A < X < B ) F(X) W(X) dXwhere W(X) is a weight function, by the quadrature sum:
Sum ( 1 <= I <= ORDER) F(X(I)) * W(I)where the set of X values are known as abscissas and the set of W values are known as weights.
Note that the letter W, unfortunately, is used to denote both the weight function in the original integral, and the vector of weight values in the quadrature sum.
|Fejer Type 2
A sparse grid is a quadrature rule for a multidimensional integral. It is formed by taking a certain linear combination of lower-order product rules. The product rules, in turn, are formed as direct products of 1D quadrature rules. It is common to form a sparse grid in which the 1D component quadrature rules are the same. This package, however, is intended to produce sparse grids based on sums of product rules for which the rule chosen for each spatial dimension may be freely chosen from the set listed above.
These sparse grids are still indexed by a number known as level, and assembled as a sum of low order product rules. As the value of level increases, the point growth becomes more complicated. This is because the 1D rules have somewhat varying point growth patterns to begin with, and the varying levels of nestedness have a dramatic effect on the sparsity of the total grid.
Since a sparse grid is made up of a combination of product grids, it is frequently the case that many of the product grids include the same point. For efficiency, it is usually desirable to merge or consolidate such duplicate points when expressing the resulting sparse grid rule. It is possible to "logically" determine when a duplicate point will be generated; however, this logic changes depending on the specific 1-dimensional rules being used, and the tests can become quite elaborate. Moreover, for rules which include real parameters, the determination of duplication can require a numerical tolerance.
In order to simplify the matter of the detection of duplicate points, the codes presented begin by generating the entire "naive" set of points. Then a user-specified tolerance TOL is used to determine when two points are equal. If the maximum difference between any two components is less than or equal to TOL, the points are declared to be equal.
A reasonable value for TOL might be the square root of the machine precision. Setting TOL to zero means that only points which are identical to the last significant digit are taken to be duplicates. Setting TOL to a negative value means that no duplicate points will be eliminated - in other words, this choice produces the full or "naive" grid.
A scalar quantity GROWTH selects the rule order O for level L from the three growth options for each 1D rule. In the case of exponentially growing rules, the slow and moderate growth rules choose O indirectly, by imposing a requirement on P, the degree of precision of the rule.
|O=L+1 for linear rules, P=2*L+1 for exponential
|O=2*L+1 for linear rules, P=4*L+1 for exponential
|O=2*L+1 for linear rules, O = next rule in sequence
The code described and made available on this web page is distributed under the GNU LGPL license.
sandia_sgmg is available in a C++ version.
nint_exactness_mixed, a C++ program which measures the polynomial exactness of a multidimensional quadrature rule based on a mixture of 1D quadrature rule factors.
quad_rule, a C++ library which defines quadrature rules for various intervals and weight functions.
sandia_rules, a C++ library which produces 1D quadrature rules of Chebyshev, Clenshaw Curtis, Fejer 2, Gegenbauer, generalized Hermite, generalized Laguerre, Hermite, Jacobi, Laguerre, Legendre and Patterson types.
sandia_rules2, a C++ library which contains a very small selection of functions which serve as an interface between sandia_sgmg or sandia_sgmgA and SANDIA_RULES.
sandia_sparse, a C++ library which computes the points and weights of a Smolyak sparse grid, based on a variety of 1-dimensional quadrature rules.
sgmg, a dataset directory which contains multidimensional Smolyak sparse grids based on a mixed set of 1D factor rules and a choice of growth rules.
sgmg, a C++ library which creates a sparse grid dataset based on a mixed set of 1D factor rules, and experiments with the use of a linear growth rate for the quadrature rules.
sgmga, a C++ library which creates sparse grids based on a mixture of 1D quadrature rules, allowing anisotropic weights for each dimension.
smolpack, a C library which implements Novak and Ritter's method for estimating the integral of a function over a multidimensional hypercube using sparse grids, by Knut Petras.
sparse_grid_mixed, a C++ library which creates sparse grids based on a mix of 1D rules.