Dakota Reference Manual: mixed_hessians

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mixed_hessians

Hessians are needed and will be obtained from a mix of numerical, analytic, and "quasi" sources

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | id_numerical_hessians | Identify which numerical-Hessian corresponds to which response
| Optional (Choose One) | Step Scaling (Group 1) | relative | (Default) Scale step size by the parameter value
absolute | Do not scale step-size
bounds | Scale step-size by the domain of the parameter
| Optional (Choose One) | Finite Difference Type (Group 2) | forward | (Default) Use forward differences
central | Use central differences
| Optional | id_quasi_hessians | Identify which quasi-Hessian corresponds to which response
| Optional | id_analytic_hessians | Identify which analytical Hessian corresponds to which response

## Description

The `mixed_hessians` specification means that some Hessian information is available directly from the simulation (analytic) whereas the rest will have to be estimated by finite differences (numerical) or approximated by secant updating. As for mixed gradients, this specification allows the user to make use of as much analytic information as is available and then estimate/approximate the rest.

The `id_analytic_hessians` list specifies by number the functions which have analytic Hessians, and the `id_numerical_hessians` and `id_quasi_hessians` lists specify by number the functions which must use numerical Hessians and secant Hessian updates, respectively. Each function identifier, from 1 through the total number of functions, must appear once and only once within the union of the `id_analytic_hessians`, `id_numerical_hessians`, and `id_quasi_hessians` lists.

The `fd_hessian_step_size` and `bfgs`, `damped` `bfgs`, or `sr1` secant update selections are as described previously in responses and pertain to those functions listed by the `id_numerical_hessians` and `id_quasi_hessians` lists.

## See Also

These keywords may also be of interest:

- no_hessians
- numerical_hessians
- quasi_hessians
- analytic_hessians