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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Pattern search, derivative free optimization method
This keyword is related to the topics:
Alias: coliny_apps
Argument(s): none
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Optional | initial_delta | Initial step size for derivative-free optimizers | ||
Optional | contraction_factor | Amount by which step length is rescaled | ||
Optional | variable_tolerance | Step length-based stopping criteria for derivative-free optimizers | ||
Optional | solution_target | Stopping criteria based on objective function value | ||
Optional | synchronization | Select how Dakota schedules a batch of concurrent function evaluations in a parallel algorithm | ||
Optional | merit_function | Balance goals of reducing objective function and satisfying constraints | ||
Optional | constraint_penalty | Multiplier for the penalty function | ||
Optional | smoothing_factor | Smoothing value for smoothed penalty functions | ||
Optional | constraint_tolerance | Maximum allowable constraint violation still considered feasible | ||
Optional | max_function_evaluations | Number of function evaluations allowed for optimizers | ||
Optional | scaling | Turn on scaling for variables, responses, and constraints | ||
Optional | model_pointer | Identifier for model block to be used by a method |
The asynchronous parallel pattern search (APPS) algorithm [37] is a fully asynchronous pattern search technique in that the search along each offset direction continues without waiting for searches along other directions to finish.
Currently, APPS only supports coordinate bases with a total of 2n function evaluations in the pattern, and these patterns may only contract.
Concurrency
APPS exploits parallelism through the use of Dakota's concurrent function evaluations. The variant of the algorithm that is currently exposed, however, limits the amount of concurrency that can be exploited. In particular, APPS can leverage an evaluation concurrency level of at most twice the number of variables. More options that allow for greater evaluation concurrency may be exposed in future releases.
Algorithm Behavior
initial_delta:
the initial step length, must be positive variable_tolerance:
step length used to determine convergence, must be greater than or equal to 4.4e-16 contraction_factor:
amount by which step length is rescaled after unsuccesful iterates, must be strictly between 0 and 1Merit Functions
APPS solves nonlinearly constrained problems by solving a sequence of linearly constrained merit function-base subproblems. There are several exact and smoothed exact penalty functions that can be specified with the merit_function
control. The options are as follows:
merit_max:
based on merit_max_smooth:
based on smoothed merit1:
based on merit1_smooth:
based on smoothed merit2:
based on merit2_smooth:
based on smoothed merit2_squared:
based on The user can also specify the following to affect the merit functions:
constraint_penalty
smoothing_parameter
Method Independent Controls
The only method independent controls that are currently mapped to APPS are:
Note that while APPS treats the constraint tolerance separately for linear and nonlinear constraints, we apply the same value to both if the user specifies constraint_tolerance
.
The APPS internal display level is mapped to the Dakota output
settings as follows:
debug:
display final solution, all input parameters, variable and constraint info, trial points, search directions, and execution details verbose:
display final solution, all input parameters, variable and constraint info, and trial points normal:
display final solution, all input parameters, variable and constraint summaries, and new best points quiet:
display final solution and all input parameters silent:
display final solutionExpected HDF5 Output
If Dakota was built with HDF5 support and run with the hdf5 keyword, this method writes the following results to HDF5: