Duality for Robust Linear Infinite Programming Problems Revisited

In this paper, we consider the robust linear infinite programming problem (RLIP c ) defined by ( RLIP c ) inf 〈 c , x 〉 subject to x ∈ X , 〈 x ∗ , x 〉 ≤ r , ∀ ( x ∗ , r ) ∈ U t , ∀ t ∈ T , where X is a locally convex Hausdorff topological vector space, T is an arbitrary index set, c ∈ X ∗ , and U t...

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Published inVietnam journal of mathematics Vol. 48; no. 3; pp. 589 - 613
Main Authors Dinh, N., Long, D. H., Yao, J.-C.
Format Journal Article
LanguageEnglish
Published Singapore Springer Singapore 01.09.2020
Springer Nature B.V
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Online AccessGet full text
ISSN2305-221X
2305-2228
DOI10.1007/s10013-020-00383-6

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Abstract In this paper, we consider the robust linear infinite programming problem (RLIP c ) defined by ( RLIP c ) inf 〈 c , x 〉 subject to x ∈ X , 〈 x ∗ , x 〉 ≤ r , ∀ ( x ∗ , r ) ∈ U t , ∀ t ∈ T , where X is a locally convex Hausdorff topological vector space, T is an arbitrary index set, c ∈ X ∗ , and U t ⊂ X ∗ × ℝ , t ∈ T are uncertainty sets. We propose an approach to duality for the robust linear problems with convex constraints (RP c ) and establish corresponding robust strong duality and also, stable robust strong duality, i.e., robust strong duality holds “uniformly” with all c ∈ X ∗ . With the different choices/ways of setting/arranging data from (RLIP c ), one gets back to the model (RP c ) and the (stable) robust strong duality for (RP c ) applies. By such a way, nine versions of dual problems for (RLIP c ) are proposed. Necessary and sufficient conditions for stable robust strong duality of these pairs of primal-dual problems are given, for which some cover several known results in the literature while the others, due to the best knowledge of the authors, are new. Moreover, as by-products, we obtained from the robust strong duality for variants pairs of primal-dual problems, several robust Farkas-type results for linear infinite systems with uncertainty. Lastly, as extensions/applications, we extend/apply the results obtained to robust linear problems with sub-affine constraints, and to linear infinite problems (i.e., (RLIP c ) with the absence of uncertainty). It is worth noticing even for these cases, we are able to derive new results on (robust/stable robust) duality for the mentioned classes of problems and new robust Farkas-type results for sub-linear systems, and also for linear infinite systems in the absence of uncertainty.
AbstractList In this paper, we consider the robust linear infinite programming problem (RLIPc) defined by (RLIPc)inf〈c,x〉subject tox∈X,〈x∗,x〉≤r,∀(x∗,r)∈Ut,∀t∈T, where X is a locally convex Hausdorff topological vector space, T is an arbitrary index set, c ∈ X∗, and Ut⊂X∗×ℝ, t ∈ T are uncertainty sets. We propose an approach to duality for the robust linear problems with convex constraints (RPc) and establish corresponding robust strong duality and also, stable robust strong duality, i.e., robust strong duality holds “uniformly” with all c ∈ X∗. With the different choices/ways of setting/arranging data from (RLIPc), one gets back to the model (RPc) and the (stable) robust strong duality for (RPc) applies. By such a way, nine versions of dual problems for (RLIPc) are proposed. Necessary and sufficient conditions for stable robust strong duality of these pairs of primal-dual problems are given, for which some cover several known results in the literature while the others, due to the best knowledge of the authors, are new. Moreover, as by-products, we obtained from the robust strong duality for variants pairs of primal-dual problems, several robust Farkas-type results for linear infinite systems with uncertainty. Lastly, as extensions/applications, we extend/apply the results obtained to robust linear problems with sub-affine constraints, and to linear infinite problems (i.e., (RLIPc) with the absence of uncertainty). It is worth noticing even for these cases, we are able to derive new results on (robust/stable robust) duality for the mentioned classes of problems and new robust Farkas-type results for sub-linear systems, and also for linear infinite systems in the absence of uncertainty.
In this paper, we consider the robust linear infinite programming problem (RLIP c ) defined by ( RLIP c ) inf 〈 c , x 〉 subject to x ∈ X , 〈 x ∗ , x 〉 ≤ r , ∀ ( x ∗ , r ) ∈ U t , ∀ t ∈ T , where X is a locally convex Hausdorff topological vector space, T is an arbitrary index set, c ∈ X ∗ , and U t ⊂ X ∗ × ℝ , t ∈ T are uncertainty sets. We propose an approach to duality for the robust linear problems with convex constraints (RP c ) and establish corresponding robust strong duality and also, stable robust strong duality, i.e., robust strong duality holds “uniformly” with all c ∈ X ∗ . With the different choices/ways of setting/arranging data from (RLIP c ), one gets back to the model (RP c ) and the (stable) robust strong duality for (RP c ) applies. By such a way, nine versions of dual problems for (RLIP c ) are proposed. Necessary and sufficient conditions for stable robust strong duality of these pairs of primal-dual problems are given, for which some cover several known results in the literature while the others, due to the best knowledge of the authors, are new. Moreover, as by-products, we obtained from the robust strong duality for variants pairs of primal-dual problems, several robust Farkas-type results for linear infinite systems with uncertainty. Lastly, as extensions/applications, we extend/apply the results obtained to robust linear problems with sub-affine constraints, and to linear infinite problems (i.e., (RLIP c ) with the absence of uncertainty). It is worth noticing even for these cases, we are able to derive new results on (robust/stable robust) duality for the mentioned classes of problems and new robust Farkas-type results for sub-linear systems, and also for linear infinite systems in the absence of uncertainty.
Author Dinh, N.
Long, D. H.
Yao, J.-C.
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10.1016/j.na.2010.11.036
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10.1137/130939596
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10.1007/s11228-019-00515-2
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Stable robust strong duality for robust linear infinite problems
Robust Farkas-type results for sub-affine systems
Linear infinite programming problems
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Robust linear infinite problems
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MA Goberna (383_CR19) 1998
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N Dinh (383_CR10) 2014; 22
V Jeyakumar (383_CR23) 2011; 15
N Dinh (383_CR12) 2017; 66
RI Boţ (383_CR4) 2013; 21
G Li (383_CR25) 2008; 19
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N Dinh (383_CR17) 2010; 59
RI Boţ (383_CR3) 2010
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– reference: BoţRIConjugate Duality in Convex Optimization. Lecture Notes in Economics and Mathematical Systems, vol. 6372010BerlinSpringer10.1007/978-3-642-04900-2
– reference: DinhNGobernaMALópezMAMoTHRobust optimization revisited via robust vector Farkas lemmasOptimization201766939963364963710.1080/02331934.2016.1152272
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– reference: GobernaMAJeyakumarVLiGLópezMARobust linear semi-infinite programming duality under uncertaintyMath. Program. Ser. B2013139185203307010010.1007/s10107-013-0668-6
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– reference: JeyakumarVLiGStrong duality in robust convex programming: complete characterizationsSIAM J. Optim.20102033843407276350910.1137/100791841
– reference: ChenJKöbisEYaoJ-COptimality conditions and duality for robust nonsmooth multiobjective optimization problems with constraintsJ. Optim. Theory Appl.2019181411436393847510.1007/s10957-018-1437-8
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Snippet In this paper, we consider the robust linear infinite programming problem (RLIP c ) defined by ( RLIP c ) inf 〈 c , x 〉 subject to x ∈ X , 〈 x ∗ , x 〉 ≤ r , ∀...
In this paper, we consider the robust linear infinite programming problem (RLIPc) defined by (RLIPc)inf〈c,x〉subject tox∈X,〈x∗,x〉≤r,∀(x∗,r)∈Ut,∀t∈T, where X is...
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springer
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StartPage 589
SubjectTerms Linear systems
Mathematics
Mathematics and Statistics
Original Article
Robustness
Uncertainty
Title Duality for Robust Linear Infinite Programming Problems Revisited
URI https://link.springer.com/article/10.1007/s10013-020-00383-6
https://www.proquest.com/docview/2432896572
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