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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Strategy in which a set of methods synergistically seek an optimal design
Alias: none
Argument(s): none
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Required (Choose One) | Hybrid Method Type (Group 1) | sequential | Methods are run one at a time, in sequence | |
embedded | A subordinate local method provides periodic refinements to a top-level global method | |||
collaborative | Multiple methods run concurrently and share information |
In a hybrid minimization method (hybrid
), a set of methods synergistically seek an optimal design. The relationships among the methods are categorized as:
The goal in each case is to exploit the strengths of different optimization and nonlinear least squares algorithms at different stages of the minimization process. Global + local hybrids (e.g., genetic algorithms combined with nonlinear programming) are a common example in which the desire for identification of a global optimum is balanced with the need for efficient navigation to a local optimum.