Dakota Reference Manual: least_squares

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

- Main Page
- Related Pages

All Pages
least_squares

Compute the coefficients of a polynomial expansion using least squares

## Specification

Alias: none

Argument(s): none

Default: svd

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional (Choose One) | LSQ Regression Approach (Group 1) | svd | Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.
equality_constrained | Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.

## Description

Compute the coefficients of a polynomial expansion using least squares. Specifically SVD-based least-squares will be used for solving over-determined systems. For the situation when the number of function values is smaller than the number of terms in a PCE, but the total number of samples including gradient values is greater than the number of terms, the resulting over-determined system will be solved using equality constrained least squares