Nonautonomous stochastic search for global minimum in continuous optimization
Various iterative stochastic optimization schemes can be represented as discrete-time Markov processes defined by the nonautonomous equation X t + 1 = T t ( X t , Y t ) , where Y t is an independent sequence and T t is a sequence of mappings. This paper presents a general framework for the study of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tarłowski, Dawid [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: In silico drug repurposing in COVID-19: A network-based analysis - Sibilio, Pasquale ELSEVIER, 2021, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:412 ; year:2014 ; number:2 ; day:15 ; month:04 ; pages:631-645 ; extent:15 |
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| DOI / URN: |
10.1016/j.jmaa.2013.10.070 |
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| 520 | |a Various iterative stochastic optimization schemes can be represented as discrete-time Markov processes defined by the nonautonomous equation X t + 1 = T t ( X t , Y t ) , where Y t is an independent sequence and T t is a sequence of mappings. This paper presents a general framework for the study of the stability and convergence of such optimization processes. Some applications are given: the mathematical convergence analysis of two optimization methods, the elitist evolution strategy ( μ + λ ) and the grenade explosion method, is presented. | ||
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Various iterative stochastic optimization schemes can be represented as discrete-time Markov processes defined by the nonautonomous equation X t + 1 = T t ( X t , Y t ) , where Y t is an independent sequence and T t is a sequence of mappings. This paper presents a general framework for the study of the stability and convergence of such optimization processes. Some applications are given: the mathematical convergence analysis of two optimization methods, the elitist evolution strategy ( μ + λ ) and the grenade explosion method, is presented. |
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Various iterative stochastic optimization schemes can be represented as discrete-time Markov processes defined by the nonautonomous equation X t + 1 = T t ( X t , Y t ) , where Y t is an independent sequence and T t is a sequence of mappings. This paper presents a general framework for the study of the stability and convergence of such optimization processes. Some applications are given: the mathematical convergence analysis of two optimization methods, the elitist evolution strategy ( μ + λ ) and the grenade explosion method, is presented. |
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Various iterative stochastic optimization schemes can be represented as discrete-time Markov processes defined by the nonautonomous equation X t + 1 = T t ( X t , Y t ) , where Y t is an independent sequence and T t is a sequence of mappings. This paper presents a general framework for the study of the stability and convergence of such optimization processes. Some applications are given: the mathematical convergence analysis of two optimization methods, the elitist evolution strategy ( μ + λ ) and the grenade explosion method, is presented. |
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