Dakota contains algorithms for optimization with gradient and nongradient-based methods uncertainty quanti?cation with sampling, reliability, and stochastic expansion methods parameter estimation with nonlinear least squares methods and sensitivity/variance analysis with design of experiments and parameter study methods.
The Dakota toolkit provides a ?exible and extensible interface between simulation codes and iterative analysis methods. This report serves as a user?s manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example = , By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a ?exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers.
These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty.
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