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 in | Vietnam journal of mathematics Vol. 48; no. 3; pp. 589 - 613 |
---|---|
Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Singapore
Springer Singapore
01.09.2020
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 2305-221X 2305-2228 |
DOI | 10.1007/s10013-020-00383-6 |
Cover
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. |
Author_xml | – sequence: 1 givenname: N. orcidid: 0000-0001-6558-1289 surname: Dinh fullname: Dinh, N. email: ndinh@hcmiu.edu.vn organization: International University, Vietnam National University - HCMC – sequence: 2 givenname: D. H. surname: Long fullname: Long, D. H. organization: VNUHCM - University of Science, Tien Giang University – sequence: 3 givenname: J.-C. surname: Yao fullname: Yao, J.-C. organization: Center for General Education, China Medical University |
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Cites_doi | 10.1007/s00245-019-09596-9 10.1137/100791841 10.1016/j.na.2010.11.036 10.1007/s11750-014-0319-y 10.1007/s10957-018-1437-8 10.1007/s11590-015-0987-z 10.1007/978-3-642-57014-8_4 10.1007/s10107-013-0668-6 10.1007/978-3-642-04900-2 10.1137/060676982 10.1007/s10957-017-1136-x 10.1007/s11228-012-0219-y 10.1137/16M1067925 10.1007/s10957-017-1067-6 10.1080/02331934.2016.1152272 10.1007/s11117-010-0078-4 10.1080/02331934.2011.619263 10.1137/130939596 10.1007/s40306-019-00349-y 10.1080/02331930801951348 10.1007/s11228-019-00515-2 |
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Keywords | 90C31 39B62 Stable robust strong duality for robust linear infinite problems Robust Farkas-type results for sub-affine systems Linear infinite programming problems 49J52 90C25 Robust linear infinite problems 46N10 Robust Farkas-type results for infinite linear systems |
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References_xml | – reference: BolintinéanuSVector variational principles; ε-efficiency and scalar stationarityJ. Convex Anal.20018718518290561058.90072 – reference: ChoHKimK-KLeeKComputing lower bounds on basket option prices by discretizing semi-infinite linear programmingOptim. Lett.20161016291644355694810.1007/s11590-015-0987-z – reference: GobernaMAJeyakumarVLiGVicente-PérezJRobust solutions of multi-objective linear semi-infinite programs under constraint data uncertaintySIAM J. Optim.20142414021419324937610.1137/130939596 – reference: ChuongTDJeyakumarVAn exact formula for radius of robust feasibility of uncertain linear programsJ. Optim. Theory Appl.2017173203226362664410.1007/s10957-017-1067-6 – 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 – reference: DinhNNghiaTTAValletGA closedness condition and its applications to DC programs with convex constraintsOptimization201059541560267651610.1080/02331930801951348 – reference: GobernaMAJeyakumarVLiGLópezMARobust linear semi-infinite programming duality under uncertaintyMath. Program. Ser. B2013139185203307010010.1007/s10107-013-0668-6 – reference: DaumSWernerRA novel feasible discretization method for linear semi-infinite programming applied to basket option pricingOptimization20116013791398286382810.1080/02331934.2011.619263 – reference: FangDLiCYaoJ-CStable Lagrange dualities for robust conical programmingJ. Nonlinear and Convex Anal.2015162141215834226431332.90207 – 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 – reference: Bolintinéanu, S.: Vector variational principles towards asymptotically well behaved vector convex functions. In: Nguyen, V. H., Strodiot, J.-J., Tossings, P (eds.) Optimization. Lecture Notes in Economics and Mathematical Systems, vol. 481, pp. 55–68. Springer, Berlin (2000) – reference: DinhNMoTHValletGVolleMA unified approach to robust Farkas-type results with applications to robust optimization problemsSIAM J. Optim.20172710751101366300610.1137/16M1067925 – reference: GobernaMALópezMALinear Semi-Infinite Optimization1998ChichesterWiley0909.90257 – reference: DinhNGobernaMAVolleMPrimal-dual optimization conditions for the robust sum of functions with applicationsAppl. Math. Optim.201980643664402659410.1007/s00245-019-09596-9 – reference: LiGYJeyakumarVLeeGMRobust conjugate duality for convex optimization under uncertainty with application to data classificationNonlinear Anal.20117423272341278176110.1016/j.na.2010.11.036 – reference: Dinh, N., Long, D.H.: Sectional convexity of epigraphs of conjugate mappings with applications to robust vector duality. Acta Math. Vietnam. https://doi.org/10.1007/s40306-019-00349-y (2019) – reference: BoţRIJeyakumarVLiGYRobust duality in parametric convex optimizationSet-valued Var. Anal.201321177189304824210.1007/s11228-012-0219-y – reference: Dinh, N., Goberna, M.A., Volle, M.: Duality for the robust sum of functions. Set-valued Var. Anal. https://doi.org/10.1007/s11228-019-00515-2 (2019) – reference: LiGNgKFOn extension of Fenchel duality and its applicationSIAM J. Optim.20081914891509246618110.1137/060676982 – reference: Chen, J., Li, J., Li, X., Lv, Y., Yao, J.-C.: Radius of robust feasibility of system of convex inequalities with uncertain data. J. Optim. Theory Appl. (to appear) – reference: DinhNGobernaMALópezMAVolleMA unifying approach to robust convex infinite optimization dualityJ. Optim. Theory Appl.2017174650685368890110.1007/s10957-017-1136-x – reference: JeyakumarVLiGYRobust Farkas’ lemma for uncertain linear systems with applicationsPositivity201115331342280382210.1007/s11117-010-0078-4 – reference: DinhNJeyakumarVFarkas’ lemma: three decades of generalizations for mathematical optimizationTOP201422122319080810.1007/s11750-014-0319-y – volume-title: Linear Semi-Infinite Optimization year: 1998 ident: 383_CR19 – volume: 80 start-page: 643 year: 2019 ident: 383_CR14 publication-title: Appl. Math. Optim. doi: 10.1007/s00245-019-09596-9 – volume: 20 start-page: 3384 year: 2010 ident: 383_CR22 publication-title: SIAM J. Optim. doi: 10.1137/100791841 – volume: 74 start-page: 2327 year: 2011 ident: 383_CR24 publication-title: Nonlinear Anal. doi: 10.1016/j.na.2010.11.036 – volume: 22 start-page: 1 year: 2014 ident: 383_CR10 publication-title: TOP doi: 10.1007/s11750-014-0319-y – volume: 181 start-page: 411 year: 2019 ident: 383_CR7 publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-018-1437-8 – volume: 10 start-page: 1629 year: 2016 ident: 383_CR8 publication-title: Optim. Lett. doi: 10.1007/s11590-015-0987-z – ident: 383_CR1 doi: 10.1007/978-3-642-57014-8_4 – volume: 139 start-page: 185 year: 2013 ident: 383_CR20 publication-title: Math. Program. Ser. B doi: 10.1007/s10107-013-0668-6 – volume-title: Conjugate Duality in Convex Optimization. Lecture Notes in Economics and Mathematical Systems, vol. 637 year: 2010 ident: 383_CR3 doi: 10.1007/978-3-642-04900-2 – volume: 19 start-page: 1489 year: 2008 ident: 383_CR25 publication-title: SIAM J. Optim. doi: 10.1137/060676982 – volume: 174 start-page: 650 year: 2017 ident: 383_CR11 publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-017-1136-x – volume: 21 start-page: 177 year: 2013 ident: 383_CR4 publication-title: Set-valued Var. Anal. doi: 10.1007/s11228-012-0219-y – volume: 16 start-page: 2141 year: 2015 ident: 383_CR18 publication-title: J. Nonlinear and Convex Anal. – volume: 27 start-page: 1075 year: 2017 ident: 383_CR16 publication-title: SIAM J. Optim. doi: 10.1137/16M1067925 – volume: 173 start-page: 203 year: 2017 ident: 383_CR6 publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-017-1067-6 – volume: 66 start-page: 939 year: 2017 ident: 383_CR12 publication-title: Optimization doi: 10.1080/02331934.2016.1152272 – volume: 15 start-page: 331 year: 2011 ident: 383_CR23 publication-title: Positivity doi: 10.1007/s11117-010-0078-4 – volume: 60 start-page: 1379 year: 2011 ident: 383_CR9 publication-title: Optimization doi: 10.1080/02331934.2011.619263 – volume: 24 start-page: 1402 year: 2014 ident: 383_CR21 publication-title: SIAM J. Optim. doi: 10.1137/130939596 – ident: 383_CR5 – ident: 383_CR13 doi: 10.1007/s40306-019-00349-y – volume: 59 start-page: 541 year: 2010 ident: 383_CR17 publication-title: Optimization doi: 10.1080/02331930801951348 – volume: 8 start-page: 71 year: 2001 ident: 383_CR2 publication-title: J. Convex Anal. – ident: 383_CR15 doi: 10.1007/s11228-019-00515-2 |
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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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Title | Duality for Robust Linear Infinite Programming Problems Revisited |
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