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An Implicit Filtering Algorithm for Optimization of Functions with Many Local Minima
Abstract
In this paper we describe and analyze an algorithm for certain box constrained optimization problems that may have several local minima. A paradigm for these problems is one in which the function to be minimized is the sum of a simple function, such as a convex quadratic, and high frequency, low amplitude terms that cause local minima away from the global minimum of the simple function. Our method is gradient based and therefore the performance can be improved by use of quasi-Newton methods.
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Cited by
Derivative-free optimization: a review of algorithms and comparison of software implementations
Rios, L. M., & Sahinidis, N. V. (2013) · Journal of Global Optimization · doi:10.1007/s10898-012-9951-y · Cited by 1,276
Benchmarking Derivative-Free Optimization Algorithms
Moré, J. J., & Wild, S. M. (2009) · SIAM Journal on Optimization · doi:10.1137/080724083 · Cited by 573
Metadata
- Authors
- Gilmore, P., & Kelley, C. T.
- Venue
- SIAM Journal on Optimization
- Published
- 1995-05
- Volume
- 5
- Issue
- 2
- Pages
- 269-285
- ISSN
- 1052-6234 (ISSN-L)
- 1095-7189
- OpenAlex
- W1989288162
- Cited by
- 171
- References
- 0
- FWCI
- 5.01
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gilmore, P., & Kelley, C. T. (1995). An Implicit Filtering Algorithm for Optimization of Functions with Many Local Minima. SIAM Journal on Optimization. https://doi.org/10.1137/0805015
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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