Order for tensor-products of Gaussian quadrature rules


  • Alias: None

  • Arguments: INTEGER

Child Keywords:


Description of Group

Dakota Keyword

Dakota Keyword Description



A set of weights specifying the realtive importance of each uncertain variable (dimension)

Optional (Choose One)

Quadrature Rule Nesting


Enforce use of nested quadrature rules if available


Enforce use of non-nested quadrature rules


Multidimensional integration by a tensor-product of Gaussian quadrature rules (specified with quadrature_order, and, optionally, dimension_preference). The default rule selection is to employ non_nested Gauss rules including Gauss-Hermite (for normals or transformed normals), Gauss-Legendre (for uniforms or transformed uniforms), Gauss-Jacobi (for betas), Gauss-Laguerre (for exponentials), generalized Gauss-Laguerre (for gammas), and numerically-generated Gauss rules (for other distributions when using an Extended basis). For the case of p_refinement or the case of an explicit nested override, Gauss-Hermite rules are replaced with Genz-Keister nested rules and Gauss-Legendre rules are replaced with Gauss-Patterson nested rules, both of which exchange lower integrand precision for greater point reuse. By specifying a dimension_preference, where higher preference leads to higher order polynomial resolution, the tensor grid may be rendered anisotropic. The dimension specified to have highest preference will be set to the specified quadrature_order and all other dimensions will be reduced in proportion to their reduced preference; any non-integral portion is truncated. To synchronize with tensor-product integration, a tensor-product expansion is used, where the order \(p_i\) of the expansion in each dimension is selected to be half of the integrand precision available from the rule in use, rounded down. In the case of non-nested Gauss rules with integrand precision \(2m_i-1\) , \(p_i\) is one less than the quadrature order \(m_i\) in each dimension (a one-dimensional expansion contains the same number of terms, \(p+1\) , as the number of Gauss points). The total number of terms, N, in a tensor-product expansion involving n uncertain input variables is .. math:: N ~=~ 1 + P ~=~ prod_{i=1}^{n} (p_i + 1) In some advanced use cases (e.g., multifidelity UQ), multiple grid resolutions can be employed; for this reason, the quadrature_order specification supports an array input.

A corresponding sequence specification is documented at, e.g., quadrature_order_sequence and quadrature_order_sequence