![]() |
Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
|
Hessians are needed and will be approximated by secant updates (BFGS or SR1) from a series of gradient evaluations
Alias: none
Argument(s): none
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Required (Choose One) | Quasi-Hessian Approximation (Group 1) | bfgs | Use BFGS method to compute quasi-hessians | |
sr1 | Use the Symmetric Rank 1 update method to compute quasi-Hessians |
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
).
These keywords may also be of interest: