DAKOTA OPT++ Parallel Direct Search (PDS)

Description
OPT++ PDS is an unconstrained optimization algorithm. This is based on Nelder-Mead Simplex algorithm.

References
DAKOTA Version 5.0 Reference Manual

SearchSchemeSize: This option specifies the number of points to be used in the direct search template by the algorithm. The option must have a positive integer value.

ConvergenceTolerance: Defines the threshold value on relative change in the objective function that indicates convergence. Option must have a value greater than 0.

MaxFunctionEvaluations: Function evaluation is the call to Analyzer to evaluate the objective function at the specified points. The maximum number of function evaluations is an integer limit for evaluations that the algorithm can attain. Algorithm can terminate with this criterion if no other criteria are satisfied. A single iteration can contain multiple function evaluations. MaxFunctionEvaluations must be a positive integer value.

MaxIterations: A single iteration can have multiple function evaluations. This is the integer limit on number of iterations the algorithm can actually run. Option must have a positive integer value.

IntermediateFilesPath: User can specify the location where the intermediate files of optimization should be generated. By default files are written to the user's temporary directory.

Output: This option controls the level of verbosity of messages user can receive from DAKOTA. The options go from Silent to Debug with increasing amount of messages returned from the infrastructure. View Output > Details should show the messages from algorithm. The detailed information can help user analyze the design space and algorithm convergence better.

TabularGraphicsData: Turning this option to true generates a file named dakota_tabular in IntermediateFilesPath directory. This file has the values of design variables, constraints and objective function for each evaluation stored in a tabular format.