Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
 All Pages
collocation_points_sequence


Specify the number of collocation points used to estimate PCE coefficients using regression or orthogonal-least-interpolation.

Specification

Alias: none

Argument(s): INTEGERLIST

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional ratio_order Specify a non-linear the relationship between the expansion order of a polynomial chaos expansion and the number of samples that will be used to compute the PCE coefficients.
Optional
(Choose One)
Group 1 least_squares Compute the coefficients of a polynomial expansion using least squares
orthogonal_matching_pursuit Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
basis_pursuit Compute the coefficients of a polynomial expansion by solving the Basis Pursuit $\ell_1$-minimization problem using linear programming.
basis_pursuit_denoising Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising $\ell_1$-minimization problem using second order cone optimization.
least_angle_regression Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
least_absolute_shrinkage Compute the coefficients of a polynomial expansion by using the LASSO problem.
Optional cross_validation Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.
Optional use_derivatives

Use derivative data to construct surrogate models

Optional tensor_grid Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.
Optional reuse_points This describes the behavior of reuse of points in constructing polynomial chaos expansion models.

Description

Specify the number of collocation points used to estimate PCE coefficients using regression or orthogonal-least-interpolation.