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Direct Search Methods on Parallel Machines
Abstract
Direct search methods are methods designed to solve unconstrained minimization problems of the form: $$\mathop {\min }\limits_{x \in {\mathbb{R}^n}} f(x),$$ where f: ℝn → ℝ. These methods are distinguished by the fact that they neither use nor require explicit derivative information; the search for a local minimizer is driven solely by function information. Popular methods in this class include the factorial design algorithm of Box [1], the pattern search algorithm of Hooke and Jeeves [4], and the simplex method of Neider and Mead [5].
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Cited by
Pattern Search Algorithms for Bound Constrained Minimization
Lewis, R. M., & Torczon, V. (1999) · SIAM Journal on Optimization · doi:10.1137/s1052623496300507 · Cited by 494
Self-citationA Survey of Medical Image Registration on Multicore and the GPU
Shams, R., Sadeghi, P., Kennedy, R., & Hartley, R. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2009.935387 · Cited by 198
Metadata
- Authors
- Dennis, J. E., & Torczon, V.
- Venue
- Operations Research ’91
- Publisher
- Physica-Verlag HD
- Published
- 1992
- Pages
- 5-6
- OpenAlex
- W2107073443
- Cited by
- 279
- References
- 0
- FWCI
- 25.23
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Dennis, J. E., & Torczon, V. (1992). Direct Search Methods on Parallel Machines. Operations Research ’91. https://doi.org/10.1007/978-3-642-48417-9_2
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
