Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Specify the number of collocation points used to estimate PCE coefficients using regression or orthogonalleastinterpolation.
Alias: none
Argument(s): INTEGERLIST
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Optional  ratio_order  Specify a nonlinear 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 minimization problem using linear programming.  
basis_pursuit_denoising  Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising 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 subsampled tensorproduct 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. 
Specify the number of collocation points used to estimate PCE coefficients using regression or orthogonalleastinterpolation.