On approximate solutions for robust semi-infinite multi-objective convex symmetric cone optimization

We present approximate solutions for the robust semi-infinite multi-objective convex symmetric cone programming problem. By using the robust optimization approach, we establish an approximate optimality theorem and approximate duality theorems for approximate solutions in convex symmetric cone optim...

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Vydané v:	Positivity : an international journal devoted to the theory and applications of positivity in analysis Ročník 26; číslo 5
Hlavní autori:	Alzalg, Baha, Oulha, Amira Achouak
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.11.2022 Springer Nature B.V
Predmet:	Algebra Calculus of Variations and Optimal Control; Optimization Econometrics Fourier Analysis Linear programming Mathematics Mathematics and Statistics Multiple objective analysis Operator Theory Optimization Potential Theory Robustness Theorems 90C20 Semi-infinite programming Robust symmetric cone optimization 90C46 90C25 90C34 Approximate duality theorems 90C29 Multi-objective programming Approximate optimality conditions
ISSN:	1385-1292, 1572-9281
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Abstract	We present approximate solutions for the robust semi-infinite multi-objective convex symmetric cone programming problem. By using the robust optimization approach, we establish an approximate optimality theorem and approximate duality theorems for approximate solutions in convex symmetric cone optimization problem involving infinitely many constraints to be satisfied and multiple objectives to be optimized simultaneously under the robust characteristic cone constraint qualification. We also give an example to illustrate the obtained results in an important special case, namely the robust semi-infinite multi-objective convex second-order cone program.
AbstractList	We present approximate solutions for the robust semi-infinite multi-objective convex symmetric cone programming problem. By using the robust optimization approach, we establish an approximate optimality theorem and approximate duality theorems for approximate solutions in convex symmetric cone optimization problem involving infinitely many constraints to be satisfied and multiple objectives to be optimized simultaneously under the robust characteristic cone constraint qualification. We also give an example to illustrate the obtained results in an important special case, namely the robust semi-infinite multi-objective convex second-order cone program.
ArticleNumber	86
Author	Alzalg, Baha Oulha, Amira Achouak
Author_xml	– sequence: 1 givenname: Baha orcidid: 0000-0002-1839-8083 surname: Alzalg fullname: Alzalg, Baha email: b.alzalg@ju.edu.jo organization: Department of Mathematics, The University of Jordan, Department of Computer Science and Engineering, The Ohio State University – sequence: 2 givenname: Amira Achouak surname: Oulha fullname: Oulha, Amira Achouak organization: Department of Mathematics, The University of Jordan, Department of Mathematics, University of Bachir El Ibrahimi
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Cites_doi	10.1006/jmaa.1996.0371 10.1007/s10479-016-2363-5 10.1007/BF00940466 10.1134/S0005117909060149 10.1080/02331930108844524 10.1007/s11117-017-0549-y 10.1007/s11117-018-0630-1 10.1017/S0962492901000071 10.1016/j.orl.2003.12.007 10.1090/S0025-5718-2010-02449-4 10.1016/j.apm.2011.12.053 10.1080/02331934.2012.690760 10.1137/060676982 10.1515/9781400831050 10.1007/s10898-004-5904-4 10.1007/s10107-003-0425-3 10.1137/1038003 10.1007/s11590-016-1067-8 10.1007/BF02594782 10.1007/s10589-018-0045-8 10.1007/s10107-002-0339-5 10.1080/10556789208805510 10.1016/S0377-2217(03)00206-6 10.1287/moor.26.3.543.10582 10.1016/j.ejor.2005.05.007 10.1007/s11117-012-0186-4 10.1007/s10107-003-0380-z 10.1186/1029-242X-2014-501 10.1007/s10107-013-0668-6 10.1007/3-540-31246-3 10.1007/s11590-019-01404-1 10.1137/S1052623402417699 10.1155/2010/363012 10.1006/jmaa.1996.0080 10.1007/s10957-018-1445-8 10.1016/j.jmaa.2013.07.075
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References	GoldfarbDIyengarGRobust convex quadratically constrained programsMath. Program.200397495515200812010.1007/s10107-003-0425-31106.90365 SchmietaSAlizadehFExtension of primal-dual interior point algorithms to symmetric conesMath. Program. Ser. A200396409438199345710.1007/s10107-003-0380-z1023.90083 GovilMGMehraAϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Optimality for multi-objective programming on a Banach spaceEur. J. Oper. Res.200415710611210.1016/S0377-2217(03)00206-61106.90065 Ben-TalAGhaouiLENemirovskiARobust Optimzation2009PrincetonPrinceton University Press10.1515/97814008310501221.90001 LiuJCϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Pareto optimality for nondifferentiable multi-objective programming via penalty functionJ. Math. Anal. Appl.1996198248261137353910.1006/jmaa.1996.00800848.90107 GutiérrezCJiménezBNovoVMultiplier rules and saddle-point theorems for Helbig’s approximate solutions in convex Pareto problemsJ. Glob. Optim.200532367383217762410.1007/s10898-004-5904-41149.90398 AlzalgBAriyawansaKALogarithmic barrier decomposition-based interior point methods for stochastic symmetric programmingJ. Math. Anal. Appl.2014409973995310321310.1016/j.jmaa.2013.07.0751306.90103 LeeJHLeeGMϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Duality theorems for convex semidefinite optimization problems with conic constraintsJ. Inequal. Appl.20102010259285510.1155/2010/3630121184.49036 Ben-TalAGhaouiLENemirovskiARobustness, in Handbook on Semidefinite Programming2000New YorkKluwer0957.90525 MordukhovichBSVariational Analysis and Generalized Differentiation, I: Basic Theory, II. Applications2006BerlinSpringer10.1007/3-540-31246-3 LiuJCϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Duality theorem of nondifferentiable nonconvex multi-objective programmingJ. Optim. Theory Appl.199169153167110459210.1007/BF00940466 LeeJHLeeGMOn ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-solutions for convex optimization problems with uncertainty dataPositivity201216509526297431210.1007/s11117-012-0186-41334.90126 AlizadehFGoldfarbDSecond-order cone programmingMath. Program. Ser. B200395351197138110.1007/s10107-002-0339-51153.90522 LeeJHLeeGMOn ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-solutions for robust semi-infinite optimization problemsPositivity201923651669397758910.1007/s11117-018-0630-11421.90150 AlzalgBA primal-dual interior-point method based on various selections of displacement step for symmetric optimizationComput. Optim. Appl.201972363390391973010.1007/s10589-018-0045-81414.90322 HamelAAn ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Lagrange multiplier rule for a mathematical programming problem on Banach spacesOptimization200149137149181616110.1080/023319301088445240966.90081 BertsimasDPachamanovaDSimMRobust linear optimization under general normsOper. Res. Lett.200432510516207745110.1016/j.orl.2003.12.0071054.90046 SchmietaSHAlizadehFAssociative and Jordan algebras, and polynomial time interior point algorithms for symmetric conesMath. Oper. Res.2001263543564184988410.1287/moor.26.3.543.105821073.90572 AlzalgBBadarnehKAbabnehAInfeasible interior-point algorithm for stochastic second-order cone optimizationJ. Optim. Theory Appl.2019181324346392140910.1007/s10957-018-1445-81414.90261 JeyakumarVLiGYStrong duality in robust semi-definite linear programming under data uncertaintyOptimization201463713733319600410.1080/02331934.2012.6907601291.90164 JeyakumarVLeeGMDinhNCharacterization of solution sets of convex vector minimization problemsEur. J. Oper. Res.200617413801395225431610.1016/j.ejor.2005.05.0071103.90090 VandenbergheLBoydSSemidefinite programmingSIAM Rev.1996384995137904110.1137/10380030845.65023 AriyawansaKZhuYA class of polynomial volumetric barrier decomposition algorithms for stochastic semidefinite programmingMath. Comput.20198016391661278547210.1090/S0025-5718-2010-02449-41244.90172 JeyakumarVLeeGMDinhNNew sequential Lagrange multiplier conditions characterizing optimality without constraint qualification for convex programsSIAM J. Optim.200314534547204815610.1137/S10526234024176991046.90059 YokoyamaKEpsilon approximate solutions for multi-objective programming problemsJ. Math. Anal. Appl.1996203142149141248510.1006/jmaa.1996.03710858.90114 AlzalgBA logarithmic barrier interior-point method based on majorant functions for second-order cone programmingOptim. Lett.202014729746407544610.1007/s11590-019-01404-11442.90177 LeeJHLeeGMOn approximate solutions for robust convex semidefinite optimization problemsPositivity201822419438381712410.1007/s11117-017-0549-y1394.90459 ArutyunovAPolyakBTMordukhovichBSVariational analysis and generalized differentiation I. Basic theory, II. ApplicationsAutom. Remote Control2009701086108710.1134/S0005117909060149 NesterovYuENemirovskiiASConic formulation of a convex programming problem and dualityOptim. Methods Softw.199219511510.1080/10556789208805510 LiCNgKFPongTKConstraint qualifications for convex inequality systems with applications in constrainted optimizationSIAM. J. Optim.200819163187240302610.1137/0606769821170.90009 LeeJHLeeGMOn ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-solutions for robust fractional optimization problemsJ. Inequal. Appl.20142014501336054710.1186/1029-242X-2014-5011333.90094 LeeJHLeeGMOn optimality conditions and duality theorems for robust semi-infinite multi-objective optimization problemsAnn. Oper. Res.2018269419438384848810.1007/s10479-016-2363-51446.90143 ToddMJSemidefinite optimizationActa Numer.200110515560200969810.1017/S09624929010000711105.65334 LeeJHJiaoLGOn quasi ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-solution for robust convex optimization problemsOptim. Lett.20171116091622372223810.1007/s11590-016-1067-81410.90155 AlzalgBCombinatorial and Algorithmic Mathematics: From Foundation to Optimization20221Seattle, WAKindle Direct Publishing StrodiotJJNguyenVHHeukemesNϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Optimal solutions in nondifferentiable convex programming and some related questionMath. Program.19832530732868966010.1007/BF025947820495.90067 Hiriart-UrrutyJBLemarechalCConvex Analysis and Minimization Algorithms1993BerlinSpringer0795.49001 GobernaMAJeyakumarVLiGLópezMARobust linear semi-infinite programming duality under uncertaintyMath. Program.20131391–2185203307010010.1007/s10107-013-0668-61282.90204 AlzalgBStochastic second-order cone programming: application modelsAppl. Math. Model.20123651225134293040710.1016/j.apm.2011.12.0531252.90055 L Vandenberghe (952_CR32) 1996; 38 B Alzalg (952_CR29) 2019; 181 JB Hiriart-Urruty (952_CR36) 1993 A Ben-Tal (952_CR1) 2000 B Alzalg (952_CR28) 2012; 36 V Jeyakumar (952_CR37) 2003; 14 C Gutiérrez (952_CR6) 2005; 32 D Goldfarb (952_CR4) 2003; 97 A Arutyunov (952_CR18) 2009; 70 MG Govil (952_CR5) 2004; 157 B Alzalg (952_CR24) 2019; 72 F Alizadeh (952_CR27) 2003; 95 YuE Nesterov (952_CR34) 1992; 1 A Hamel (952_CR7) 2001; 49 MJ Todd (952_CR31) 2001; 10 B Alzalg (952_CR25) 2014; 409 V Jeyakumar (952_CR8) 2014; 63 BS Mordukhovich (952_CR19) 2006 JH Lee (952_CR9) 2017; 11 SH Schmieta (952_CR23) 2001; 26 JH Lee (952_CR11) 2012; 16 JJ Strodiot (952_CR20) 1983; 25 JC Liu (952_CR16) 1991; 69 JC Liu (952_CR17) 1996; 198 K Ariyawansa (952_CR33) 2019; 80 B Alzalg (952_CR26) 2022 JH Lee (952_CR10) 2010; 2010 K Yokoyama (952_CR21) 1996; 203 A Ben-Tal (952_CR2) 2009 V Jeyakumar (952_CR35) 2006; 174 JH Lee (952_CR13) 2018; 269 B Alzalg (952_CR30) 2020; 14 MA Goberna (952_CR39) 2013; 139 D Bertsimas (952_CR3) 2004; 32 S Schmieta (952_CR22) 2003; 96 JH Lee (952_CR12) 2014; 2014 JH Lee (952_CR14) 2018; 22 JH Lee (952_CR15) 2019; 23 C Li (952_CR38) 2008; 19
References_xml	– reference: JeyakumarVLiGYStrong duality in robust semi-definite linear programming under data uncertaintyOptimization201463713733319600410.1080/02331934.2012.6907601291.90164 – reference: GobernaMAJeyakumarVLiGLópezMARobust linear semi-infinite programming duality under uncertaintyMath. Program.20131391–2185203307010010.1007/s10107-013-0668-61282.90204 – reference: AlzalgBStochastic second-order cone programming: application modelsAppl. Math. Model.20123651225134293040710.1016/j.apm.2011.12.0531252.90055 – reference: YokoyamaKEpsilon approximate solutions for multi-objective programming problemsJ. Math. Anal. Appl.1996203142149141248510.1006/jmaa.1996.03710858.90114 – reference: LeeJHLeeGMOn ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-solutions for robust semi-infinite optimization problemsPositivity201923651669397758910.1007/s11117-018-0630-11421.90150 – reference: LeeJHJiaoLGOn quasi ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-solution for robust convex optimization problemsOptim. Lett.20171116091622372223810.1007/s11590-016-1067-81410.90155 – reference: MordukhovichBSVariational Analysis and Generalized Differentiation, I: Basic Theory, II. Applications2006BerlinSpringer10.1007/3-540-31246-3 – reference: LiuJCϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Duality theorem of nondifferentiable nonconvex multi-objective programmingJ. Optim. Theory Appl.199169153167110459210.1007/BF00940466 – reference: BertsimasDPachamanovaDSimMRobust linear optimization under general normsOper. Res. Lett.200432510516207745110.1016/j.orl.2003.12.0071054.90046 – reference: GutiérrezCJiménezBNovoVMultiplier rules and saddle-point theorems for Helbig’s approximate solutions in convex Pareto problemsJ. Glob. Optim.200532367383217762410.1007/s10898-004-5904-41149.90398 – reference: LeeJHLeeGMϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Duality theorems for convex semidefinite optimization problems with conic constraintsJ. Inequal. Appl.20102010259285510.1155/2010/3630121184.49036 – reference: VandenbergheLBoydSSemidefinite programmingSIAM Rev.1996384995137904110.1137/10380030845.65023 – reference: Hiriart-UrrutyJBLemarechalCConvex Analysis and Minimization Algorithms1993BerlinSpringer0795.49001 – reference: GovilMGMehraAϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Optimality for multi-objective programming on a Banach spaceEur. J. Oper. Res.200415710611210.1016/S0377-2217(03)00206-61106.90065 – reference: AlzalgBA logarithmic barrier interior-point method based on majorant functions for second-order cone programmingOptim. Lett.202014729746407544610.1007/s11590-019-01404-11442.90177 – reference: LeeJHLeeGMOn approximate solutions for robust convex semidefinite optimization problemsPositivity201822419438381712410.1007/s11117-017-0549-y1394.90459 – reference: AlizadehFGoldfarbDSecond-order cone programmingMath. Program. Ser. B200395351197138110.1007/s10107-002-0339-51153.90522 – reference: ToddMJSemidefinite optimizationActa Numer.200110515560200969810.1017/S09624929010000711105.65334 – reference: ArutyunovAPolyakBTMordukhovichBSVariational analysis and generalized differentiation I. Basic theory, II. ApplicationsAutom. Remote Control2009701086108710.1134/S0005117909060149 – reference: GoldfarbDIyengarGRobust convex quadratically constrained programsMath. Program.200397495515200812010.1007/s10107-003-0425-31106.90365 – reference: LeeJHLeeGMOn optimality conditions and duality theorems for robust semi-infinite multi-objective optimization problemsAnn. Oper. Res.2018269419438384848810.1007/s10479-016-2363-51446.90143 – reference: AriyawansaKZhuYA class of polynomial volumetric barrier decomposition algorithms for stochastic semidefinite programmingMath. Comput.20198016391661278547210.1090/S0025-5718-2010-02449-41244.90172 – reference: JeyakumarVLeeGMDinhNCharacterization of solution sets of convex vector minimization problemsEur. J. Oper. Res.200617413801395225431610.1016/j.ejor.2005.05.0071103.90090 – reference: LeeJHLeeGMOn ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-solutions for robust fractional optimization problemsJ. Inequal. Appl.20142014501336054710.1186/1029-242X-2014-5011333.90094 – reference: LiCNgKFPongTKConstraint qualifications for convex inequality systems with applications in constrainted optimizationSIAM. J. Optim.200819163187240302610.1137/0606769821170.90009 – reference: SchmietaSHAlizadehFAssociative and Jordan algebras, and polynomial time interior point algorithms for symmetric conesMath. Oper. Res.2001263543564184988410.1287/moor.26.3.543.105821073.90572 – reference: JeyakumarVLeeGMDinhNNew sequential Lagrange multiplier conditions characterizing optimality without constraint qualification for convex programsSIAM J. Optim.200314534547204815610.1137/S10526234024176991046.90059 – reference: LiuJCϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Pareto optimality for nondifferentiable multi-objective programming via penalty functionJ. Math. Anal. Appl.1996198248261137353910.1006/jmaa.1996.00800848.90107 – reference: SchmietaSAlizadehFExtension of primal-dual interior point algorithms to symmetric conesMath. Program. Ser. A200396409438199345710.1007/s10107-003-0380-z1023.90083 – reference: AlzalgBA primal-dual interior-point method based on various selections of displacement step for symmetric optimizationComput. Optim. Appl.201972363390391973010.1007/s10589-018-0045-81414.90322 – reference: NesterovYuENemirovskiiASConic formulation of a convex programming problem and dualityOptim. Methods Softw.199219511510.1080/10556789208805510 – reference: Ben-TalAGhaouiLENemirovskiARobustness, in Handbook on Semidefinite Programming2000New YorkKluwer0957.90525 – reference: AlzalgBBadarnehKAbabnehAInfeasible interior-point algorithm for stochastic second-order cone optimizationJ. Optim. Theory Appl.2019181324346392140910.1007/s10957-018-1445-81414.90261 – reference: HamelAAn ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Lagrange multiplier rule for a mathematical programming problem on Banach spacesOptimization200149137149181616110.1080/023319301088445240966.90081 – reference: LeeJHLeeGMOn ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-solutions for convex optimization problems with uncertainty dataPositivity201216509526297431210.1007/s11117-012-0186-41334.90126 – reference: Ben-TalAGhaouiLENemirovskiARobust Optimzation2009PrincetonPrinceton University Press10.1515/97814008310501221.90001 – reference: AlzalgBAriyawansaKALogarithmic barrier decomposition-based interior point methods for stochastic symmetric programmingJ. Math. Anal. Appl.2014409973995310321310.1016/j.jmaa.2013.07.0751306.90103 – reference: StrodiotJJNguyenVHHeukemesNϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Optimal solutions in nondifferentiable convex programming and some related questionMath. Program.19832530732868966010.1007/BF025947820495.90067 – reference: AlzalgBCombinatorial and Algorithmic Mathematics: From Foundation to Optimization20221Seattle, WAKindle Direct Publishing – volume: 203 start-page: 142 year: 1996 ident: 952_CR21 publication-title: J. Math. Anal. Appl. doi: 10.1006/jmaa.1996.0371 – volume: 269 start-page: 419 year: 2018 ident: 952_CR13 publication-title: Ann. Oper. Res. doi: 10.1007/s10479-016-2363-5 – volume: 69 start-page: 153 year: 1991 ident: 952_CR16 publication-title: J. Optim. Theory Appl. doi: 10.1007/BF00940466 – volume: 70 start-page: 1086 year: 2009 ident: 952_CR18 publication-title: Autom. Remote Control doi: 10.1134/S0005117909060149 – volume: 49 start-page: 137 year: 2001 ident: 952_CR7 publication-title: Optimization doi: 10.1080/02331930108844524 – volume: 22 start-page: 419 year: 2018 ident: 952_CR14 publication-title: Positivity doi: 10.1007/s11117-017-0549-y – volume: 23 start-page: 651 year: 2019 ident: 952_CR15 publication-title: Positivity doi: 10.1007/s11117-018-0630-1 – volume: 10 start-page: 515 year: 2001 ident: 952_CR31 publication-title: Acta Numer. doi: 10.1017/S0962492901000071 – volume: 32 start-page: 510 year: 2004 ident: 952_CR3 publication-title: Oper. Res. Lett. doi: 10.1016/j.orl.2003.12.007 – volume: 80 start-page: 1639 year: 2019 ident: 952_CR33 publication-title: Math. Comput. doi: 10.1090/S0025-5718-2010-02449-4 – volume: 36 start-page: 5122 year: 2012 ident: 952_CR28 publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2011.12.053 – volume: 63 start-page: 713 year: 2014 ident: 952_CR8 publication-title: Optimization doi: 10.1080/02331934.2012.690760 – volume: 19 start-page: 163 year: 2008 ident: 952_CR38 publication-title: SIAM. J. Optim. doi: 10.1137/060676982 – volume-title: Robust Optimzation year: 2009 ident: 952_CR2 doi: 10.1515/9781400831050 – volume: 32 start-page: 367 year: 2005 ident: 952_CR6 publication-title: J. Glob. Optim. doi: 10.1007/s10898-004-5904-4 – volume: 97 start-page: 495 year: 2003 ident: 952_CR4 publication-title: Math. Program. doi: 10.1007/s10107-003-0425-3 – volume: 38 start-page: 49 year: 1996 ident: 952_CR32 publication-title: SIAM Rev. doi: 10.1137/1038003 – volume-title: Combinatorial and Algorithmic Mathematics: From Foundation to Optimization year: 2022 ident: 952_CR26 – volume: 11 start-page: 1609 year: 2017 ident: 952_CR9 publication-title: Optim. Lett. doi: 10.1007/s11590-016-1067-8 – volume: 25 start-page: 307 year: 1983 ident: 952_CR20 publication-title: Math. Program. doi: 10.1007/BF02594782 – volume: 72 start-page: 363 year: 2019 ident: 952_CR24 publication-title: Comput. Optim. Appl. doi: 10.1007/s10589-018-0045-8 – volume-title: Convex Analysis and Minimization Algorithms year: 1993 ident: 952_CR36 – volume: 95 start-page: 3 year: 2003 ident: 952_CR27 publication-title: Math. Program. Ser. B doi: 10.1007/s10107-002-0339-5 – volume-title: Robustness, in Handbook on Semidefinite Programming year: 2000 ident: 952_CR1 – volume: 1 start-page: 95 year: 1992 ident: 952_CR34 publication-title: Optim. Methods Softw. doi: 10.1080/10556789208805510 – volume: 157 start-page: 106 year: 2004 ident: 952_CR5 publication-title: Eur. J. Oper. Res. doi: 10.1016/S0377-2217(03)00206-6 – volume: 26 start-page: 543 issue: 3 year: 2001 ident: 952_CR23 publication-title: Math. Oper. Res. doi: 10.1287/moor.26.3.543.10582 – volume: 174 start-page: 1380 year: 2006 ident: 952_CR35 publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2005.05.007 – volume: 16 start-page: 509 year: 2012 ident: 952_CR11 publication-title: Positivity doi: 10.1007/s11117-012-0186-4 – volume: 96 start-page: 409 year: 2003 ident: 952_CR22 publication-title: Math. Program. Ser. A doi: 10.1007/s10107-003-0380-z – volume: 2014 start-page: 501 year: 2014 ident: 952_CR12 publication-title: J. Inequal. Appl. doi: 10.1186/1029-242X-2014-501 – volume: 139 start-page: 185 issue: 1–2 year: 2013 ident: 952_CR39 publication-title: Math. Program. doi: 10.1007/s10107-013-0668-6 – volume-title: Variational Analysis and Generalized Differentiation, I: Basic Theory, II. Applications year: 2006 ident: 952_CR19 doi: 10.1007/3-540-31246-3 – volume: 14 start-page: 729 year: 2020 ident: 952_CR30 publication-title: Optim. Lett. doi: 10.1007/s11590-019-01404-1 – volume: 14 start-page: 534 year: 2003 ident: 952_CR37 publication-title: SIAM J. Optim. doi: 10.1137/S1052623402417699 – volume: 2010 year: 2010 ident: 952_CR10 publication-title: J. Inequal. Appl. doi: 10.1155/2010/363012 – volume: 198 start-page: 248 year: 1996 ident: 952_CR17 publication-title: J. Math. Anal. Appl. doi: 10.1006/jmaa.1996.0080 – volume: 181 start-page: 324 year: 2019 ident: 952_CR29 publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-018-1445-8 – volume: 409 start-page: 973 year: 2014 ident: 952_CR25 publication-title: J. Math. Anal. Appl. doi: 10.1016/j.jmaa.2013.07.075
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Snippet	We present approximate solutions for the robust semi-infinite multi-objective convex symmetric cone programming problem. By using the robust optimization...
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SubjectTerms	Algebra Calculus of Variations and Optimal Control; Optimization Econometrics Fourier Analysis Linear programming Mathematics Mathematics and Statistics Multiple objective analysis Operator Theory Optimization Potential Theory Robustness Theorems
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Title	On approximate solutions for robust semi-infinite multi-objective convex symmetric cone optimization
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