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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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The OPT++ library[60] 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.
Method Independent Controls
These are specified directly under the method block.
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.
1.8.4 
