Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Pattern search, derivative free optimization method
This keyword is related to the topics:
Alias: coliny_apps
Argument(s): none
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Optional  initial_delta  Initial step size for nongradient based optimizers  
Optional  contraction_factor  Amount by which step length is rescaled  
Optional  threshold_delta  Stopping criteria based on step length or pattern size  
Optional  solution_target  Stopping criteria based on objective function value  
Optional  synchronization  Select how Dakota schedules function evaluations in a pattern search  
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  linear_inequality_constraint_matrix  Define coefficients of the linear inequality constraints  
Optional  linear_inequality_lower_bounds  Define lower bounds for the linear inequality constraint  
Optional  linear_inequality_upper_bounds  Define upper bounds for the linear inequality constraint  
Optional  linear_inequality_scale_types  Specify how each linear inequality constraint is scaled  
Optional  linear_inequality_scales  Define the characteristic values to scale linear inequalities  
Optional  linear_equality_constraint_matrix  Define coefficients of the linear equalities  
Optional  linear_equality_targets  Define target values for the linear equality constraints  
Optional  linear_equality_scale_types  Specify how each linear equality constraint is scaled  
Optional  linear_equality_scales  Define the characteristic values to scale linear equalities  
Optional  constraint_tolerance  The maximum allowable value of constraint violation still considered to be feasible  
Optional  max_function_evaluations  Stopping criteria based on number of function evaluations  
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 [36] 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 threshold_delta:
step length used to determine convergence, must be greater than or equal to 4.4e16 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 functionbase 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 norm merit_max_smooth:
based on smoothed norm merit1:
based on norm merit1_smooth:
based on smoothed norm merit2:
based on norm merit2_smooth:
based on smoothed norm merit2_squared:
based on normThe 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 solution