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Minimization by Random Search Techniques
Abstract
We give two general convergence proofs for random search algorithms. We review the literature and show how our results extend those available for specific variants of the conceptual algorithm studied here. We then exploit the convergence results to examine convergence rates and to actually design implementable methods. Finally we report on some computational experience.
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Cited by
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955
Stochastic global optimization methods part I: Clustering methods
Rinnooy Kan, A. H. G., & Timmer, G. T. (1987) · Mathematical Programming · doi:10.1007/bf02592070 · Cited by 463
Integration of ATM call admission control and link capacity control by distributed neural networks
Hiramatsu, A. (1991) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.103558 · Cited by 78
Refai, H., El-Gazar, A. A., Ragab, G. M., Hassan, D. H., Ahmed, O. S., Hussein, R. A., Shabana, S., Waffo-Téguo, P., Valls, J., Al-Mokaddem, A. K., et al. (2023) · Marine Drugs · doi:10.3390/md21030149 · Cited by 36
Metadata
- Authors
- Solis, F. J., & Wets, R. J.-B.
- Venue
- Mathematics of Operations Research
- Published
- 1981-02
- Volume
- 6
- Issue
- 1
- Pages
- 19-30
- ISSN
- 0364-765X (ISSN-L)
- 1526-5471
- OpenAlex
- W2123859926
- Cited by
- 1,671
- References
- 0
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- closed
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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.