Abstract
We introduce an abstract definition of pattern search methods for solving nonlinear unconstrained optimization problems. Our definition unifies an important collection of optimization methods that neither compute nor explicitly approximate derivatives. We exploit our characterization of pattern search methods to establish a global convergence theory that does not enforce a notion of sufficient decrease. Our analysis is possible because the iterates of a pattern search method lie on a scaled, translated integer lattice. This allows us to relax the classical requirements on the acceptance of the step, at the expense of stronger conditions on the form of the step, and still guarantee global convergence.
Keywords
Iterated functionMathematicsConvergence (economics)ExploitMathematical optimizationPattern searchIterated local searchAlgorithmInteger (computer science)Nonlinear systemRate of convergenceSearch algorithmLocal search (optimization)Computer scienceKey (lock)
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Publication Info
- Year
- 1997
- Type
- article
- Volume
- 7
- Issue
- 1
- Pages
- 1-25
- Citations
- 1296
- Access
- Closed
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Cite This
Virginia Torczon
(1997).
On the Convergence of Pattern Search Algorithms.
SIAM Journal on Optimization
, 7
(1)
, 1-25.
https://doi.org/10.1137/s1052623493250780
Identifiers
- DOI
- 10.1137/s1052623493250780