quasi_hessians
Hessians are needed and will be approximated by secant updates (BFGS or SR1) from a series of gradient evaluations
Specification
Alias: None
Arguments: None
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
|---|---|---|---|
Required (Choose One) |
Quasi-Hessian Approximation |
Use BFGS method to compute quasi-hessians |
|
Use the Symmetric Rank 1 update method to compute quasi-Hessians |
Description
The quasi_hessians specification means that Hessian information is
needed and will be approximated using secant updates (sometimes called
“quasi-Newton updates”, though any algorithm that approximates
Newton’s method is a quasi-Newton method).
Compared to finite difference numerical Hessians, secant approximations do not expend additional function evaluations in estimating all of the second-order information for every point of interest. Rather, they accumulate approximate curvature information over time using the existing gradient evaluations.
The supported secant approximations include the
Broyden-Fletcher-Goldfarb-Shanno (BFGS) update (specified with the
keyword bfgs) and the Symmetric Rank 1 (SR1) update (specified with
the keyword sr1).
