collocation_points
Number of collocation points used to estimate expansion coefficients
Specification
Alias: None
Arguments: INTEGER
Child Keywords:
Required/Optional  | 
Description of Group  | 
Dakota Keyword  | 
Dakota Keyword Description  | 
|---|---|---|---|
Optional (Choose One)  | 
Regression Algorithm  | 
Compute the coefficients of a polynomial expansion using least squares  | 
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Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)  | 
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Compute the coefficients of a polynomial expansion by solving the Basis Pursuit \(\ell_1\) -minimization problem using linear programming.  | 
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Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising \(\ell_1\) -minimization problem using second order cone optimization.  | 
|||
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.  | 
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Compute the coefficients of a polynomial expansion by using the LASSO problem.  | 
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Optional  | 
Use cross validation to choose the ‘best’ polynomial order of a polynomial chaos expansion.  | 
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Optional  | 
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  | 
Perform bounds-scaling on response values prior to surrogate emulation  | 
||
Optional  | 
Use derivative data to construct surrogate models  | 
||
Optional  | 
Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.  | 
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Optional  | 
This describes the behavior of reuse of points in constructing polynomial chaos expansion models.  | 
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Optional  | 
Maximum iterations in determining polynomial coefficients  | 
||
Description
Specify the number of collocation points used to estimate expansion coefficients using regression approaches.
A corresponding sequence specification is documented at, e.g.,
collocation_points_sequence and
collocation_points_sequence

