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Stochastic global optimization methods. part 1: clustering methods

Authors:

A. H. G Rinnooy Kan,

G. T. TimmerAuthors Info & Claims

Mathematical Programming: Series A and B, Volume 39, Issue 1

Pages 27 - 56

Published: 01 October 1987 Publication History

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Wang YBalakrishnan SSingh A(2018)Optimization of smooth functions with noisy observationsProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327345.3327347(4343-4354)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327345.3327347
Malherbe CVayatis N(2017)Global optimization of Lipschitz functionsProceedings of the 34th International Conference on Machine Learning - Volume 7010.5555/3305890.3305920(2314-2323)Online publication date: 6-Aug-2017
https://dl.acm.org/doi/10.5555/3305890.3305920
Chen WMa YLefebvre SXin SMartínez Jwang w(2017)Fabricable tile decorsACM Transactions on Graphics10.1145/3130800.313081736:6(1-15)Online publication date: 20-Nov-2017
https://dl.acm.org/doi/10.1145/3130800.3130817
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Index Terms

Stochastic global optimization methods. part 1: clustering methods
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
      1. Computations in finite fields
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Stochastic global optimization methods part I: Clustering methods

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In Part II of our paper, two stochastic methods for global optimization are described that, with probability 1, find all relevant local minima of the objective function with the smallest possible number of local searches. The computational performance ...

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cover image Mathematical Programming: Series A and B

Mathematical Programming: Series A and B Volume 39, Issue 1

October 1987

66 pages

ISSN:0025-5610

Editor:
M. J. Todd
Cornell Univ., Ithaca, NY

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 1987

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Wang YBalakrishnan SSingh A(2018)Optimization of smooth functions with noisy observationsProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327345.3327347(4343-4354)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327345.3327347
Malherbe CVayatis N(2017)Global optimization of Lipschitz functionsProceedings of the 34th International Conference on Machine Learning - Volume 7010.5555/3305890.3305920(2314-2323)Online publication date: 6-Aug-2017
https://dl.acm.org/doi/10.5555/3305890.3305920
Chen WMa YLefebvre SXin SMartínez Jwang w(2017)Fabricable tile decorsACM Transactions on Graphics10.1145/3130800.313081736:6(1-15)Online publication date: 20-Nov-2017
https://dl.acm.org/doi/10.1145/3130800.3130817
Song HLee JHan WMatwin SYu SFarooq F(2017)PAMAEProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3098098(1087-1096)Online publication date: 13-Aug-2017
https://dl.acm.org/doi/10.1145/3097983.3098098
Pence ICesmeli MSenel FCetisli B(2016)A new unconstrained global optimization method based on clustering and parabolic approximationExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.02.03655:C(493-507)Online publication date: 15-Aug-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.02.036
Peng ZWu DZhu W(2016)The robust constant and its applications in random global search for unconstrained global optimizationJournal of Global Optimization10.1007/s10898-014-0256-164:3(469-482)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1007/s10898-014-0256-1
Jian NHenderson S(2015)An introduction to simulation optimizationProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888819(1780-1794)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888819
Samuelsson C(2015)The Stochastic Simplex Bisection AlgorithmProcedia Computer Science10.1016/j.procs.2015.05.21551:C(855-864)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.procs.2015.05.215
Custódio AMadeira J(2015)GLODSJournal of Global Optimization10.1007/s10898-014-0224-962:1(1-28)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1007/s10898-014-0224-9
Hadjidoukas PVoglis CDimakopoulos VLagaris IPapageorgiou D(2014)Supporting adaptive and irregular parallelism for non-linear numerical optimizationApplied Mathematics and Computation10.5555/2942969.2943091231:C(544-559)Online publication date: 15-Mar-2014
https://dl.acm.org/doi/10.5555/2942969.2943091
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Recommendations

Stochastic global optimization methods part I: Clustering methods

Stochastic Optimization Methods: Applications in Engineering and Operations Research

Stochastic global optimization methods part II: Multi level methods

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