Pub Date : 2023-09-23DOI: 10.1007/s11590-023-02062-0
A. Gosavi, L. H. Sneed, L. A. Spearing
{"title":"Deep reinforcement learning for approximate policy iteration: convergence analysis and a post-earthquake disaster response case study","authors":"A. Gosavi, L. H. Sneed, L. A. Spearing","doi":"10.1007/s11590-023-02062-0","DOIUrl":"https://doi.org/10.1007/s11590-023-02062-0","url":null,"abstract":"","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135966863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.1007/s11590-023-02059-9
Nenad Mladenović, Angelo Sifaleras, Andrei Sleptchenko
Abstract This special issue contains 15 papers submitted by the participants of the 8th International Conference on Variable Neighborhood Search (ICVNS 2021), which was held in Abu Dhabi, U.A.E., online due to COVID-19 restrictions, on March 22–24, 2021.
{"title":"Special issue dedicated to the 8th International Conference on Variable Neighborhood Search (ICVNS 2021)","authors":"Nenad Mladenović, Angelo Sifaleras, Andrei Sleptchenko","doi":"10.1007/s11590-023-02059-9","DOIUrl":"https://doi.org/10.1007/s11590-023-02059-9","url":null,"abstract":"Abstract This special issue contains 15 papers submitted by the participants of the 8th International Conference on Variable Neighborhood Search (ICVNS 2021), which was held in Abu Dhabi, U.A.E., online due to COVID-19 restrictions, on March 22–24, 2021.","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136154354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1007/s11590-023-02047-z
Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Ruth Misener
Abstract Constrained Bayesian optimization optimizes a black-box objective function subject to black-box constraints. For simplicity, most existing works assume that multiple constraints are independent. To ask, when and how does dependence between constraints help? , we remove this assumption and implement probability of feasibility with dependence (Dep-PoF) by applying multiple output Gaussian processes (MOGPs) as surrogate models and using expectation propagation to approximate the probabilities. We compare Dep-PoF and the independent version PoF. We propose two new acquisition functions incorporating Dep-PoF and test them on synthetic and practical benchmarks. Our results are largely negative: incorporating dependence between the constraints does not help much. Empirically, incorporating dependence between constraints may be useful if: (i) the solution is on the boundary of the feasible region(s) or (ii) the feasible set is very small. When these conditions are satisfied, the predictive covariance matrix from the MOGP may be poorly approximated by a diagonal matrix and the off-diagonal matrix elements may become important. Dep-PoF may apply to settings where (i) the constraints and their dependence are totally unknown and (ii) experiments are so expensive that any slightly better Bayesian optimization procedure is preferred. But, in most cases, Dep-PoF is indistinguishable from PoF.
{"title":"Dependence in constrained Bayesian optimization","authors":"Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Ruth Misener","doi":"10.1007/s11590-023-02047-z","DOIUrl":"https://doi.org/10.1007/s11590-023-02047-z","url":null,"abstract":"Abstract Constrained Bayesian optimization optimizes a black-box objective function subject to black-box constraints. For simplicity, most existing works assume that multiple constraints are independent. To ask, when and how does dependence between constraints help? , we remove this assumption and implement probability of feasibility with dependence (Dep-PoF) by applying multiple output Gaussian processes (MOGPs) as surrogate models and using expectation propagation to approximate the probabilities. We compare Dep-PoF and the independent version PoF. We propose two new acquisition functions incorporating Dep-PoF and test them on synthetic and practical benchmarks. Our results are largely negative: incorporating dependence between the constraints does not help much. Empirically, incorporating dependence between constraints may be useful if: (i) the solution is on the boundary of the feasible region(s) or (ii) the feasible set is very small. When these conditions are satisfied, the predictive covariance matrix from the MOGP may be poorly approximated by a diagonal matrix and the off-diagonal matrix elements may become important. Dep-PoF may apply to settings where (i) the constraints and their dependence are totally unknown and (ii) experiments are so expensive that any slightly better Bayesian optimization procedure is preferred. But, in most cases, Dep-PoF is indistinguishable from PoF.","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136307342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-18DOI: 10.1007/s11590-023-02058-w
Suding Liu
{"title":"Approximation algorithm for solving the 1-line Steiner tree problem with minimum number of Steiner points","authors":"Suding Liu","doi":"10.1007/s11590-023-02058-w","DOIUrl":"https://doi.org/10.1007/s11590-023-02058-w","url":null,"abstract":"","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135153957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-16DOI: 10.1007/s11590-023-02055-z
Emilie Chouzenoux, Jean-Baptiste Fest
We consider the minimization of a differentiable Lipschitz gradient but non necessarily convex, function F defined on $${mathbb {R}}^N$$ . We propose an accelerated gradient descent approach which combines three strategies, namely (i) a variable metric derived from the majorization-minimization principle; (ii) a subspace strategy incorporating information from the past iterates; (iii) a block alternating update. Under the assumption that F satisfies the Kurdyka–Łojasiewicz property, we give conditions under which the sequence generated by the resulting block majorize-minimize subspace algorithm converges to a critical point of the objective function, and we exhibit convergence rates for its iterates.
{"title":"Convergence analysis of block majorize-minimize subspace approach","authors":"Emilie Chouzenoux, Jean-Baptiste Fest","doi":"10.1007/s11590-023-02055-z","DOIUrl":"https://doi.org/10.1007/s11590-023-02055-z","url":null,"abstract":"We consider the minimization of a differentiable Lipschitz gradient but non necessarily convex, function F defined on $${mathbb {R}}^N$$ . We propose an accelerated gradient descent approach which combines three strategies, namely (i) a variable metric derived from the majorization-minimization principle; (ii) a subspace strategy incorporating information from the past iterates; (iii) a block alternating update. Under the assumption that F satisfies the Kurdyka–Łojasiewicz property, we give conditions under which the sequence generated by the resulting block majorize-minimize subspace algorithm converges to a critical point of the objective function, and we exhibit convergence rates for its iterates.","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135304611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-24DOI: 10.1007/s11590-023-02039-z
Yifu Feng, Xin-Na Geng, Dan-Yang Lv, Ji-Bo Wang
{"title":"Scheduling jobs with general linear deterioration to minimize total weighted number of late jobs","authors":"Yifu Feng, Xin-Na Geng, Dan-Yang Lv, Ji-Bo Wang","doi":"10.1007/s11590-023-02039-z","DOIUrl":"https://doi.org/10.1007/s11590-023-02039-z","url":null,"abstract":"","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42403232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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