method

Begins Dakota method selection and behavioral settings.

Topics

block

Specification

• Alias: None

• Arguments: None

Child Keywords:

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		final_solutions	Number of designs returned as the best solutions
Required (Choose One)	Method (Iterative Algorithm)	hybrid	Strategy in which a set of methods synergistically seek an optimal design
		multi_start	Multi-Start Optimization Method
		pareto_set	Pareto set optimization
		branch_and_bound	(Experimental Capability) Solves a mixed integer nonlinear optimization problem
		surrogate_based_local	Local Surrogate Based Optimization
		surrogate_based_global	Adaptive Global Surrogate-Based Optimization
		dot_frcg	DOT conjugate gradient optimization method
		dot_mmfd	DOT modified method of feasible directions
		dot_bfgs	DOT BFGS optimization method
		dot_slp	DOT Sequential Linear Program
		dot_sqp	DOT Sequential Quadratic Program
		conmin_frcg	CONMIN conjugate gradient optimization method
		conmin_mfd	CONMIN method of feasible directions
		dl_solver	(Experimental) Dynamically-loaded solver
		npsol_sqp	NPSOL Sequential Quadratic Program
		nlssol_sqp	Sequential Quadratic Program for nonlinear least squares
		nlpql_sqp	NLPQL 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
		demo_tpl	Third-party optimization library integration demonstration.
		rol	Rapid Optimization Library (ROL) is a large-scale optimization package within Trilinos.
		asynch_pattern_search	Pattern search, derivative free optimization method
		mesh_adaptive_search	Finds optimal variable values using adaptive mesh-based search
		nowpac	Gradient-free inequality-constrained optimization using Nonlinear Optimization With Path Augmented Constraints (NOWPAC).
		snowpac	Stochastic version of NOWPAC that incorporates error estimates and noise mitigation.
		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
		surrogate_based_uq	Generic UQ method for constructing and interrogating a surrogate model.
		function_train	UQ method leveraging a functional tensor train surrogate model.
		multifidelity_function_train	Multifidelity uncertainty quantification using function train expansions
		multilevel_function_train	Multilevel uncertainty quantification using function train expansions
		polynomial_chaos	Uncertainty quantification using polynomial chaos expansions
		multifidelity_polynomial_chaos	Multifidelity uncertainty quantification using polynomial chaos expansions
		multilevel_polynomial_chaos	Multilevel uncertainty quantification using polynomial chaos expansions
		stoch_collocation	Uncertainty quantification with stochastic collocation
		multifidelity_stoch_collocation	Multifidelity uncertainty quantification using stochastic collocation
		sampling	Randomly samples variables according to their distributions
		multilevel_sampling	Multilevel Monte Carlo (MLMC) sampling method for UQ
		multifidelity_sampling	Multifidelity Monte Carlo sampling method for UQ
		multilevel_multifidelity_sampling	Multilevel-Multifidelity sampling methods for UQ
		approximate_control_variate	Approximate control variate (ACV) sampling methods for UQ
		multilevel_blue	The multilevel best linear unbiased estimator (ML BLUE) sampling method for UQ
		importance_sampling	Importance sampling
		gpais	Gaussian Process Adaptive Importance Sampling
		adaptive_sampling	(Experimental) Adaptively refine a Gaussian process surrogate
		pof_darts	Probability-of-Failure (POF) darts is a novel method for estimating the probability of failure based on random sphere-packing.
		rkd_darts	Recursive k-d (RKD) Darts: Recursive Hyperplane Sampling for Numerical Integration of High-Dimensional Functions.
		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. A method block contains the various keywords necessary to select 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.

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: OPT++).

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