Pub Date : 2023-09-25DOI: 10.1007/s10589-023-00526-8
Felipe Fidalgo, Emerson Castelani, Guilherme Philippi
{"title":"A numerical-and-computational study on the impact of using quaternions in the branch-and-prune algorithm for exact discretizable distance geometry problems","authors":"Felipe Fidalgo, Emerson Castelani, Guilherme Philippi","doi":"10.1007/s10589-023-00526-8","DOIUrl":"https://doi.org/10.1007/s10589-023-00526-8","url":null,"abstract":"","PeriodicalId":55227,"journal":{"name":"Computational Optimization and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135815603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-23DOI: 10.1007/s10589-023-00528-6
Patrick R. Johnstone, Jonathan Eckstein, Thomas Flynn, Shinjae Yoo
{"title":"Stochastic projective splitting","authors":"Patrick R. Johnstone, Jonathan Eckstein, Thomas Flynn, Shinjae Yoo","doi":"10.1007/s10589-023-00528-6","DOIUrl":"https://doi.org/10.1007/s10589-023-00528-6","url":null,"abstract":"","PeriodicalId":55227,"journal":{"name":"Computational Optimization and Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135958374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.1007/s10589-023-00529-5
Shuotao Diao, Suvrajeet Sen
Abstract This paper studies a fusion of concepts from stochastic programming and non-parametric statistical learning in which data is available in the form of covariates interpreted as predictors and responses. Such models are designed to impart greater agility, allowing decisions under uncertainty to adapt to the knowledge of predictors (leading indicators). This paper studies two classes of methods for such joint prediction-optimization models. One of the methods may be classified as a first-order method, whereas the other studies piecewise linear approximations. Both of these methods are based on coupling non-parametric estimation for predictive purposes, and optimization for decision-making within one unified framework. In addition, our study incorporates several non-parametric estimation schemes, including k nearest neighbors ( k NN) and other standard kernel estimators. Our computational results demonstrate that the new algorithms proposed in this paper outperform traditional approaches which were not designed for streaming data applications requiring simultaneous estimation and optimization as important design features for such algorithms. For instance, coupling k NN with Stochastic Decomposition (SD) turns out to be over 40 times faster than an online version of Benders Decomposition while finding decisions of similar quality. Such computational results motivate a paradigm shift in optimization algorithms that are intended for modern streaming applications.
{"title":"Distribution-free algorithms for predictive stochastic programming in the presence of streaming data","authors":"Shuotao Diao, Suvrajeet Sen","doi":"10.1007/s10589-023-00529-5","DOIUrl":"https://doi.org/10.1007/s10589-023-00529-5","url":null,"abstract":"Abstract This paper studies a fusion of concepts from stochastic programming and non-parametric statistical learning in which data is available in the form of covariates interpreted as predictors and responses. Such models are designed to impart greater agility, allowing decisions under uncertainty to adapt to the knowledge of predictors (leading indicators). This paper studies two classes of methods for such joint prediction-optimization models. One of the methods may be classified as a first-order method, whereas the other studies piecewise linear approximations. Both of these methods are based on coupling non-parametric estimation for predictive purposes, and optimization for decision-making within one unified framework. In addition, our study incorporates several non-parametric estimation schemes, including k nearest neighbors ( k NN) and other standard kernel estimators. Our computational results demonstrate that the new algorithms proposed in this paper outperform traditional approaches which were not designed for streaming data applications requiring simultaneous estimation and optimization as important design features for such algorithms. For instance, coupling k NN with Stochastic Decomposition (SD) turns out to be over 40 times faster than an online version of Benders Decomposition while finding decisions of similar quality. Such computational results motivate a paradigm shift in optimization algorithms that are intended for modern streaming applications.","PeriodicalId":55227,"journal":{"name":"Computational Optimization and Applications","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1007/s10589-023-00523-x
A. Greco, F. Cannizzaro, R. Bruno, A. Pluchino
Abstract An innovative strategy for an optimal design of planar frames able to resist seismic excitations is proposed. The optimal design is performed considering the cross sections of beams and columns as design variables. The procedure is based on genetic algorithms (GA) that are performed according to a nested structure suitable to be implemented in parallel on several computing devices. In particular, this bi-level optimization involves two nested genetic algorithms. The first external one seeks the size of the structural elements of the frame which corresponds to the most performing solution associated with the highest value of an appropriate fitness function. The latter function takes into account, among other considerations, the seismic safety factor and the failure mode that are calculated by means of the second internal algorithm. The proposed procedure aims at representing a prompt performance-based design procedure which observes earthquake engineering principles, that is displacement capacity and energy dissipation, although based on a limit analysis, thus avoiding the need of performing cumbersome nonlinear analyses. The details of the proposed procedure are provided and applications to the seismic design of two frames of different size are described.
{"title":"A nested genetic algorithm strategy for an optimal seismic design of frames","authors":"A. Greco, F. Cannizzaro, R. Bruno, A. Pluchino","doi":"10.1007/s10589-023-00523-x","DOIUrl":"https://doi.org/10.1007/s10589-023-00523-x","url":null,"abstract":"Abstract An innovative strategy for an optimal design of planar frames able to resist seismic excitations is proposed. The optimal design is performed considering the cross sections of beams and columns as design variables. The procedure is based on genetic algorithms (GA) that are performed according to a nested structure suitable to be implemented in parallel on several computing devices. In particular, this bi-level optimization involves two nested genetic algorithms. The first external one seeks the size of the structural elements of the frame which corresponds to the most performing solution associated with the highest value of an appropriate fitness function. The latter function takes into account, among other considerations, the seismic safety factor and the failure mode that are calculated by means of the second internal algorithm. The proposed procedure aims at representing a prompt performance-based design procedure which observes earthquake engineering principles, that is displacement capacity and energy dissipation, although based on a limit analysis, thus avoiding the need of performing cumbersome nonlinear analyses. The details of the proposed procedure are provided and applications to the seismic design of two frames of different size are described.","PeriodicalId":55227,"journal":{"name":"Computational Optimization and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135014763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1007/s10589-023-00517-9
Robert Baier, Elza Farkhi
Abstract We consider differential inclusions with strengthened one-sided Lipschitz (SOSL) right-hand sides. The class of SOSL multivalued maps is wider than the class of Lipschitz ones and a subclass of the class of one-sided Lipschitz maps. We prove a Filippov approximation theorem for the solutions of such differential inclusions with perturbations in the right-hand side, both of the set of the velocities (outer perturbations) and of the state (inner perturbations). The obtained estimate of the distance between the approximate and exact solution extends the known Filippov estimate for Lipschitz maps to SOSL ones and improves the order of approximation with respect to the inner perturbation known for one-sided Lipschitz (OSL) right-hand sides from $$frac{1}{2}$$ 12 to 1.
{"title":"A Filippov approximation theorem for strengthened one-sided Lipschitz differential inclusions","authors":"Robert Baier, Elza Farkhi","doi":"10.1007/s10589-023-00517-9","DOIUrl":"https://doi.org/10.1007/s10589-023-00517-9","url":null,"abstract":"Abstract We consider differential inclusions with strengthened one-sided Lipschitz (SOSL) right-hand sides. The class of SOSL multivalued maps is wider than the class of Lipschitz ones and a subclass of the class of one-sided Lipschitz maps. We prove a Filippov approximation theorem for the solutions of such differential inclusions with perturbations in the right-hand side, both of the set of the velocities (outer perturbations) and of the state (inner perturbations). The obtained estimate of the distance between the approximate and exact solution extends the known Filippov estimate for Lipschitz maps to SOSL ones and improves the order of approximation with respect to the inner perturbation known for one-sided Lipschitz (OSL) right-hand sides from $$frac{1}{2}$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:math> to 1.","PeriodicalId":55227,"journal":{"name":"Computational Optimization and Applications","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135936322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-30DOI: 10.1007/s10589-023-00519-7
R. Rockafellar
{"title":"Generalizations of the proximal method of multipliers in convex optimization","authors":"R. Rockafellar","doi":"10.1007/s10589-023-00519-7","DOIUrl":"https://doi.org/10.1007/s10589-023-00519-7","url":null,"abstract":"","PeriodicalId":55227,"journal":{"name":"Computational Optimization and Applications","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49318103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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