Dakota Reference Manual  Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Constrained Optimization BY Linear Approximations (COBYLA)


This keyword is related to the topics:


Alias: none

Argument(s): none

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional initial_delta Initial step size for non-gradient based optimizers
Optional threshold_delta Stopping criteria based on step length or pattern size
Optional solution_target Stopping criteria based on objective function value
Optional seed

Seed of the random number generator

Optional show_misc_options Show algorithm parameters not exposed in Dakota input
Optional misc_options Set method options not available through Dakota spec
Optional model_pointer

Identifier for model block to be used by a method


The Constrained Optimization BY Linear Approximations (COBYLA) algorithm is an extension to the Nelder-Mead simplex algorithm for handling general linear/nonlinear constraints and is invoked using the coliny_cobyla group specification. The COBYLA algorithm employs linear approximations to the objective and constraint functions, the approximations being formed by linear interpolation at N+1 points in the space of the variables. We regard these interpolation points as vertices of a simplex. The step length parameter controls the size of the simplex and it is reduced automatically from initial_delta to threshold_delta. One advantage that COBYLA has over many of its competitors is that it treats each constraint individually when calculating a change to the variables, instead of lumping the constraints together into a single penalty function.

See the page package_scolib for important information regarding all SCOLIB methods

coliny_cobyla is inherently serial.

Stopping Critieria

DIRECT can be terminated with:

COBYLA currently only supports termination based on the max_function_evaluations and solution_target specifications.

See Also

These keywords may also be of interest: