Begins Dakota method selection and behavioral settings.

Topics

This keyword is related to the topics:

block

Specification

Alias: none

Argument(s): none

	Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
	Optional		id_method	Name the method block; helpful when there are multiple
	Optional		output	Control how much method information is written to the screen and output file
	Optional		max_iterations	Stopping criteria based on number of iterations
	Optional		max_function_evaluations	Stopping criteria based on number of function evaluations
	Optional		speculative	Compute speculative gradients
	Optional		convergence_tolerance	Stopping criterion based on convergence of the objective function
	Optional		constraint_tolerance	The maximum allowable value of constraint violation still considered to be feasible
	Optional		scaling	Turn on scaling for variables, responses, and constraints
	Optional		final_solutions	Number of designs returned as the best solutions
	Required (Choose One)	Group 1	hybrid	Strategy in which a set of methods synergistically seek an optimal design
			multi_start	Multi-Start Optimization Method
			pareto_set	Pareto set optimization
			surrogate_based_local	Local Surrogate Based Optimization
			surrogate_based_global	Global Surrogate Based Optimization
			dot_frcg	A conjugate gradient optimization method
			dot_mmfd	Method of feasible directions
			dot_bfgs	A conjugate gradient optimization method
			dot_slp	Sequential Linear Programming
			dot_sqp	Sequential Quadratic Program
			dot	Access to methods in the DOT package
			conmin_frcg	A conjugate gradient optimization method
			conmin_mfd	Method of feasible directions
			conmin	Access to methods in the CONMIN library
			dl_solver	(Experimental) Dynamically-loaded solver
			npsol_sqp	Sequential Quadratic Program
			nlssol_sqp	Sequential Quadratic Program for nonlinear least squares
			stanford	Select methods from the Stanford package
			nlpql_sqp	Sequential Quadratic Program
			optpp_cg	A conjugate gradient optimization method
			optpp_q_newton	Quasi-Newton optimization method
			optpp_fd_newton	Finite Difference Newton optimization method
			optpp_g_newton	Newton method based least-squares calbration
			optpp_newton	Newton method based optimization
			optpp_pds	Simplex-based derivative free optimization method
			asynch_pattern_search	Pattern search, derivative free optimization method
			mesh_adaptive_search	Finds optimal variable values using adaptive mesh-based search
			moga	Multi-objective Genetic Algorithm (a.k.a Evolutionary Algorithm)
			soga	Single-objective Genetic Algorithm (a.k.a Evolutionary Algorithm)
			coliny_pattern_search	Pattern search, derivative free optimization method
			coliny_solis_wets	Simple greedy local search method
			coliny_cobyla	Constrained Optimization BY Linear Approximations (COBYLA)
			coliny_direct	DIviding RECTangles method
			coliny_ea	Evolutionary Algorithm
			coliny_beta	(Experimental) Coliny beta solver
			nl2sol	Trust-region method for nonlinear least squares
			nonlinear_cg	(Experimental) nonlinear conjugate gradient optimization
			ncsu_direct	DIviding RECTangles method
			genie_opt_darts	Voronoi-based high-dimensional global Lipschitzian optimization
			genie_direct	Classical high-dimensional global Lipschitzian optimization Classical high-dimensional global Lipschitzian optimization
			efficient_global	Global Surrogate Based Optimization, a.k.a. EGO
			polynomial_chaos	Uncertainty quantification using polynomial chaos expansions
			stoch_collocation	Uncertainty quantification with stochastic collocation
			sampling	Randomly samples variables according to their distributions
			importance_sampling	Importance sampling
			gpais	Gaussian Process Adaptive Importance Sampling
			adaptive_sampling	(Experimental) Build a GP surrogate and refine it adaptively
			pof_darts	Probability-of-Failure (POF) darts is a novel method for estimating the probability of failure based on random sphere-packing.
			efficient_subspace	(Experimental) efficient subspace method (ESM)
			global_evidence	Evidence theory with evidence measures computed with global optimization methods
			global_interval_est	Interval analysis using global optimization methods
			bayes_calibration	Bayesian calibration
			dace	Design and Analysis of Computer Experiments
			fsu_cvt	Design of Computer Experiments - Centroidal Voronoi Tessellation
			psuade_moat	Morris One-at-a-Time
			local_evidence	Evidence theory with evidence measures computed with local optimization methods
			local_interval_est	Interval analysis using local optimization
			local_reliability	Local reliability method
			global_reliability	Global reliability methods
			fsu_quasi_mc	Design of Computer Experiments - Quasi-Monte Carlo sampling
			vector_parameter_study	Samples variables along a user-defined vector
			list_parameter_study	Samples variables as a specified values
			centered_parameter_study	Samples variables along points moving out from a center point
			multidim_parameter_study	Samples variables on full factorial grid of study points
richardson_extrap	Estimate order of convergence of a response as model fidelity increases

Description

The method keyword signifies the start of a block in the Dakota input file. Said block contains the various keywords necessary to specify a method and to control its behavior.

Method Block Requirements

At least one method block must appear in the Dakota input file. Multiple method blocks may be needed to fully define advanced analysis approaches.

Each method block must specify one method and, optionally, any associated keywords that govern the behavior of the method.

The Methods

Each method block must select one method.

Starting with Dakota v6.0, the methods are grouped into two types: standard methods and multi-component methods.

The standard methods are stand-alone and self-contained in the sense that they only require a model to perform a study. They do not call other methods. While methods such as polynomial_chaos and efficient_global internally utilize multiple iterator and surrogate model components, these components are generally hidden from user control due to restrictions on modularity; thus, these methods are stand-alone.

The multi-component group of methods provides a higher level "meta-algorithm" that points to other methods and models that support sub-iteration. For example, in a sequential hybrid method, the hybrid method specification must identify a list of subordinate methods, and the "meta-algorithm" executes these methods in sequence and transfers information between them. Surrogate-based minimizers provide another example in that they point both to other methods (e.g. what optimization method is used to solve the approximate subproblem) as well as to models (e.g. what type of surrogate model is employed). Multi-component methods generally provide some level of "plug and play" modularity, through their flexible support of a variety of method and model selections.

Component-Based Iterator Commands

Component-based iterator specifications include hybrid, multi-start, pareto set, surrogate-based local, surrogate-based global, and branch and bound methods. Whereas a standard iterator specification only needs an optional model pointer string (specified with model_pointer), component-based iterator specifications can include method pointer, method name, and model pointer specifications in order to define the components employed in the "meta-iteration." In particular, these specifications identify one or more methods (by pointer or by name) to specify the subordinate iterators that will be used in the top-level algorithm. Identifying a sub-iterator by name instead of by pointer is a lightweight option that relaxes the need for a separate method specification for the sub-iterator; however, a model pointer may be required in this case to provide the specification connectivity normally supported by the method pointer. Refer to these individual method descriptions for specific requirements for these advanced methods.

Method Independent Controls

In addition to the method, there are 10 optional keywords, which are referred to as method independent controls. These controls are valid for enough methods that it was reasonable to pull them out of the method dependent blocks and consolidate the specifications, however, they are NOT universally respected by all methods.

Examples

Several examples follow. The first example shows a minimal specification for an optimization method.

method
  dot_sqp

This example uses all of the defaults for this method.

A more sophisticated example would be

method,
  id_method = 'NLP1'
  dot_sqp
    max_iterations = 50
    convergence_tolerance = 1e-4
    output verbose
    model_pointer = 'M1'

This example demonstrates the use of identifiers and pointers as well as some method independent and method dependent controls for the sequential quadratic programming (SQP) algorithm from the DOT library. The max_iterations, convergence_tolerance, and output settings are method independent controls, in that they are defined for a variety of methods (see dot for usage of these controls).

The next example shows a specification for a least squares method.

method
  optpp_g_newton
    max_iterations = 10
    convergence_tolerance = 1.e-8
    search_method trust_region
    gradient_tolerance = 1.e-6

Some of the same method independent controls are present along with several method dependent controls (search_method and gradient_tolerance) which are only meaningful for OPT++ methods (see package_optpp).

The next example shows a specification for a nondeterministic method with several method dependent controls (refer to sampling).

method
  sampling
    samples = 100
    seed = 12345
    sample_type lhs
    response_levels = 1000. 500.

The last example shows a specification for a parameter study method where, again, each of the controls are method dependent (refer to vector_parameter_study).

method
  vector_parameter_study
    step_vector = 1. 1. 1.
    num_steps = 10