Dakota Reference Manual: package_optpp

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package_optpp

## Description

The OPT++ library[62] contains primarily gradient-based nonlinear programming optimizers for unconstrained, bound-constrained, and nonlinearly constrained minimization: Polak-Ribiere conjugate gradient (Dakota's `optpp_cg` method), quasi-Newton (Dakota's `optpp_q_newton` method), finite difference Newton (Dakota's `optpp_fd_newton` method), and full Newton (Dakota's `optpp_newton` method).

The conjugate gradient method is strictly unconstrained, and each of the Newton-based methods are automatically bound to the appropriate OPT++ algorithm based on the user constraint specification (unconstrained, bound-constrained, or generally-constrained). In the generally-constrained case, the Newton methods use a nonlinear interior-point approach to manage the constraints. The library also contains a direct search algorithm, PDS (parallel direct search, Dakota's `optpp_pds` method), which supports bound constraints.

Controls

1. `max_iterations`
2. `max_function_evaluations`
3. `convergence_tolerance`
4. `output`
5. `speculative`

Concurrency

OPT++'s gradient-based methods are not parallel algorithms and cannot directly take advantage of concurrent function evaluations. However, if `numerical_gradients` with `method_source` `dakota` is specified, a parallel Dakota configuration can utilize concurrent evaluations for the finite difference gradient computations.

Constraints

Linear constraint specifications are supported by each of the Newton methods (`optpp_newton`, `optpp_q_newton`, `optpp_fd_newton`, and `optpp_g_newton`)

`optpp_cg` must be unconstrained

`optpp_pds` can be, at most, bound-constrained.

## Related Topics

## Related Keywords

- optpp_cg : A conjugate gradient optimization method
- optpp_fd_newton : Finite Difference Newton optimization method
- optpp_g_newton : Newton method based least-squares calbration
- optpp_newton : Newton method based optimization
- optpp_pds : Simplex-based derivative free optimization method
- optpp_q_newton : Quasi-Newton optimization method