Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems

Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex con...
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Autor*in:	Dinh, Nguyen [verfasserIn] Long, Dang Hai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Robust vector optimization Robust convex optimization Vector optimization Robust strong duality Vector Farkas lemma

Übergeordnetes Werk:	Enthalten in: Vietnam journal of mathematics - Singapore : Springer, 1999, 46(2018), 2 vom: 17. März, Seite 293-328
Übergeordnetes Werk:	volume:46 ; year:2018 ; number:2 ; day:17 ; month:03 ; pages:293-328

Links:	Volltext

DOI / URN:	10.1007/s10013-018-0283-1

Katalog-ID:	SPR008017158

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520			\|a Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex cone in Z, C ⊂ X is a nonempty subset, $\mathcal {U}$ is an uncertainty set, and F : X → Y ∪{ + ∞Y}, Gu : X → Z ∪{ + ∞Z} for all $u\in \mathcal {U}$. The Lagrangian robust dual problem and the weakly Lagrangian robust dual problem for (RVP) are proposed and then principles of robust strong duality for (RVP) associated to these two dual problems in a general setting (not necessarily convex) are established. The results are obtained based mainly on the robust vector Farkas-type results and the representations of epigraphs of conjugate mappings of vector-valued functions, which are the key tools of this paper. Next, we give characterizations of robust strong duality for (RVP) in the convex setting and also for vector optimization problems (i.e., when the uncertainty set is a singleton). To illustrate the ability of applications of the results just obtained, we show that when specifying our general results for (RVP) to robust scalar problems (i.e., when $Y = \mathbb {R}$), we get new principles of robust strong duality for general non-convex robust problems, and various characterizations of robust strong duality for convex problems which cover/extend known results in the literature. In addition, we consider a (scalar) robust infinite problem. It turns out that a further application of the general results obtained for scalar robust problems to this specific problem also leads to various forms of robust strong duality results for the problem in consideration, which are new or cover/extend the known ones published in the recent years.
650		4	\|a Robust vector optimization \|7 (dpeaa)DE-He213
650		4	\|a Robust convex optimization \|7 (dpeaa)DE-He213
650		4	\|a Vector optimization \|7 (dpeaa)DE-He213
650		4	\|a Robust strong duality \|7 (dpeaa)DE-He213
650		4	\|a Vector Farkas lemma \|7 (dpeaa)DE-He213
700	1		\|a Long, Dang Hai \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Vietnam journal of mathematics \|d Singapore : Springer, 1999 \|g 46(2018), 2 vom: 17. März, Seite 293-328 \|w (DE-627)300183968 \|w (DE-600)1481450-X \|x 2305-2228 \|7 nnns
773	1	8	\|g volume:46 \|g year:2018 \|g number:2 \|g day:17 \|g month:03 \|g pages:293-328
856	4	0	\|u https://dx.doi.org/10.1007/s10013-018-0283-1 \|z lizenzpflichtig \|3 Volltext
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912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2018
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 31.00 \|q ASE
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952			\|d 46 \|j 2018 \|e 2 \|b 17 \|c 03 \|h 293-328

Indexfelder

author_variant	n d nd d h l dh dhl
matchkey_str	article:23052228:2018----::opeehrceiainorbssrndaiyorbsvco
hierarchy_sort_str	2018
bklnumber	31.00
publishDate	2018
allfields	10.1007/s10013-018-0283-1 doi (DE-627)SPR008017158 (SPR)s10013-018-0283-1-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Dinh, Nguyen verfasserin aut Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex cone in Z, C ⊂ X is a nonempty subset, $\mathcal {U}$ is an uncertainty set, and F : X → Y ∪{ + ∞Y}, Gu : X → Z ∪{ + ∞Z} for all $u\in \mathcal {U}$. The Lagrangian robust dual problem and the weakly Lagrangian robust dual problem for (RVP) are proposed and then principles of robust strong duality for (RVP) associated to these two dual problems in a general setting (not necessarily convex) are established. The results are obtained based mainly on the robust vector Farkas-type results and the representations of epigraphs of conjugate mappings of vector-valued functions, which are the key tools of this paper. Next, we give characterizations of robust strong duality for (RVP) in the convex setting and also for vector optimization problems (i.e., when the uncertainty set is a singleton). To illustrate the ability of applications of the results just obtained, we show that when specifying our general results for (RVP) to robust scalar problems (i.e., when $Y = \mathbb {R}$), we get new principles of robust strong duality for general non-convex robust problems, and various characterizations of robust strong duality for convex problems which cover/extend known results in the literature. In addition, we consider a (scalar) robust infinite problem. It turns out that a further application of the general results obtained for scalar robust problems to this specific problem also leads to various forms of robust strong duality results for the problem in consideration, which are new or cover/extend the known ones published in the recent years. Robust vector optimization (dpeaa)DE-He213 Robust convex optimization (dpeaa)DE-He213 Vector optimization (dpeaa)DE-He213 Robust strong duality (dpeaa)DE-He213 Vector Farkas lemma (dpeaa)DE-He213 Long, Dang Hai verfasserin aut Enthalten in Vietnam journal of mathematics Singapore : Springer, 1999 46(2018), 2 vom: 17. März, Seite 293-328 (DE-627)300183968 (DE-600)1481450-X 2305-2228 nnns volume:46 year:2018 number:2 day:17 month:03 pages:293-328 https://dx.doi.org/10.1007/s10013-018-0283-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 46 2018 2 17 03 293-328
spelling	10.1007/s10013-018-0283-1 doi (DE-627)SPR008017158 (SPR)s10013-018-0283-1-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Dinh, Nguyen verfasserin aut Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex cone in Z, C ⊂ X is a nonempty subset, $\mathcal {U}$ is an uncertainty set, and F : X → Y ∪{ + ∞Y}, Gu : X → Z ∪{ + ∞Z} for all $u\in \mathcal {U}$. The Lagrangian robust dual problem and the weakly Lagrangian robust dual problem for (RVP) are proposed and then principles of robust strong duality for (RVP) associated to these two dual problems in a general setting (not necessarily convex) are established. The results are obtained based mainly on the robust vector Farkas-type results and the representations of epigraphs of conjugate mappings of vector-valued functions, which are the key tools of this paper. Next, we give characterizations of robust strong duality for (RVP) in the convex setting and also for vector optimization problems (i.e., when the uncertainty set is a singleton). To illustrate the ability of applications of the results just obtained, we show that when specifying our general results for (RVP) to robust scalar problems (i.e., when $Y = \mathbb {R}$), we get new principles of robust strong duality for general non-convex robust problems, and various characterizations of robust strong duality for convex problems which cover/extend known results in the literature. In addition, we consider a (scalar) robust infinite problem. It turns out that a further application of the general results obtained for scalar robust problems to this specific problem also leads to various forms of robust strong duality results for the problem in consideration, which are new or cover/extend the known ones published in the recent years. Robust vector optimization (dpeaa)DE-He213 Robust convex optimization (dpeaa)DE-He213 Vector optimization (dpeaa)DE-He213 Robust strong duality (dpeaa)DE-He213 Vector Farkas lemma (dpeaa)DE-He213 Long, Dang Hai verfasserin aut Enthalten in Vietnam journal of mathematics Singapore : Springer, 1999 46(2018), 2 vom: 17. März, Seite 293-328 (DE-627)300183968 (DE-600)1481450-X 2305-2228 nnns volume:46 year:2018 number:2 day:17 month:03 pages:293-328 https://dx.doi.org/10.1007/s10013-018-0283-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 46 2018 2 17 03 293-328
allfields_unstemmed	10.1007/s10013-018-0283-1 doi (DE-627)SPR008017158 (SPR)s10013-018-0283-1-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Dinh, Nguyen verfasserin aut Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex cone in Z, C ⊂ X is a nonempty subset, $\mathcal {U}$ is an uncertainty set, and F : X → Y ∪{ + ∞Y}, Gu : X → Z ∪{ + ∞Z} for all $u\in \mathcal {U}$. The Lagrangian robust dual problem and the weakly Lagrangian robust dual problem for (RVP) are proposed and then principles of robust strong duality for (RVP) associated to these two dual problems in a general setting (not necessarily convex) are established. The results are obtained based mainly on the robust vector Farkas-type results and the representations of epigraphs of conjugate mappings of vector-valued functions, which are the key tools of this paper. Next, we give characterizations of robust strong duality for (RVP) in the convex setting and also for vector optimization problems (i.e., when the uncertainty set is a singleton). To illustrate the ability of applications of the results just obtained, we show that when specifying our general results for (RVP) to robust scalar problems (i.e., when $Y = \mathbb {R}$), we get new principles of robust strong duality for general non-convex robust problems, and various characterizations of robust strong duality for convex problems which cover/extend known results in the literature. In addition, we consider a (scalar) robust infinite problem. It turns out that a further application of the general results obtained for scalar robust problems to this specific problem also leads to various forms of robust strong duality results for the problem in consideration, which are new or cover/extend the known ones published in the recent years. Robust vector optimization (dpeaa)DE-He213 Robust convex optimization (dpeaa)DE-He213 Vector optimization (dpeaa)DE-He213 Robust strong duality (dpeaa)DE-He213 Vector Farkas lemma (dpeaa)DE-He213 Long, Dang Hai verfasserin aut Enthalten in Vietnam journal of mathematics Singapore : Springer, 1999 46(2018), 2 vom: 17. März, Seite 293-328 (DE-627)300183968 (DE-600)1481450-X 2305-2228 nnns volume:46 year:2018 number:2 day:17 month:03 pages:293-328 https://dx.doi.org/10.1007/s10013-018-0283-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 46 2018 2 17 03 293-328
allfieldsGer	10.1007/s10013-018-0283-1 doi (DE-627)SPR008017158 (SPR)s10013-018-0283-1-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Dinh, Nguyen verfasserin aut Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex cone in Z, C ⊂ X is a nonempty subset, $\mathcal {U}$ is an uncertainty set, and F : X → Y ∪{ + ∞Y}, Gu : X → Z ∪{ + ∞Z} for all $u\in \mathcal {U}$. The Lagrangian robust dual problem and the weakly Lagrangian robust dual problem for (RVP) are proposed and then principles of robust strong duality for (RVP) associated to these two dual problems in a general setting (not necessarily convex) are established. The results are obtained based mainly on the robust vector Farkas-type results and the representations of epigraphs of conjugate mappings of vector-valued functions, which are the key tools of this paper. Next, we give characterizations of robust strong duality for (RVP) in the convex setting and also for vector optimization problems (i.e., when the uncertainty set is a singleton). To illustrate the ability of applications of the results just obtained, we show that when specifying our general results for (RVP) to robust scalar problems (i.e., when $Y = \mathbb {R}$), we get new principles of robust strong duality for general non-convex robust problems, and various characterizations of robust strong duality for convex problems which cover/extend known results in the literature. In addition, we consider a (scalar) robust infinite problem. It turns out that a further application of the general results obtained for scalar robust problems to this specific problem also leads to various forms of robust strong duality results for the problem in consideration, which are new or cover/extend the known ones published in the recent years. Robust vector optimization (dpeaa)DE-He213 Robust convex optimization (dpeaa)DE-He213 Vector optimization (dpeaa)DE-He213 Robust strong duality (dpeaa)DE-He213 Vector Farkas lemma (dpeaa)DE-He213 Long, Dang Hai verfasserin aut Enthalten in Vietnam journal of mathematics Singapore : Springer, 1999 46(2018), 2 vom: 17. März, Seite 293-328 (DE-627)300183968 (DE-600)1481450-X 2305-2228 nnns volume:46 year:2018 number:2 day:17 month:03 pages:293-328 https://dx.doi.org/10.1007/s10013-018-0283-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 46 2018 2 17 03 293-328
allfieldsSound	10.1007/s10013-018-0283-1 doi (DE-627)SPR008017158 (SPR)s10013-018-0283-1-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Dinh, Nguyen verfasserin aut Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex cone in Z, C ⊂ X is a nonempty subset, $\mathcal {U}$ is an uncertainty set, and F : X → Y ∪{ + ∞Y}, Gu : X → Z ∪{ + ∞Z} for all $u\in \mathcal {U}$. The Lagrangian robust dual problem and the weakly Lagrangian robust dual problem for (RVP) are proposed and then principles of robust strong duality for (RVP) associated to these two dual problems in a general setting (not necessarily convex) are established. The results are obtained based mainly on the robust vector Farkas-type results and the representations of epigraphs of conjugate mappings of vector-valued functions, which are the key tools of this paper. Next, we give characterizations of robust strong duality for (RVP) in the convex setting and also for vector optimization problems (i.e., when the uncertainty set is a singleton). To illustrate the ability of applications of the results just obtained, we show that when specifying our general results for (RVP) to robust scalar problems (i.e., when $Y = \mathbb {R}$), we get new principles of robust strong duality for general non-convex robust problems, and various characterizations of robust strong duality for convex problems which cover/extend known results in the literature. In addition, we consider a (scalar) robust infinite problem. It turns out that a further application of the general results obtained for scalar robust problems to this specific problem also leads to various forms of robust strong duality results for the problem in consideration, which are new or cover/extend the known ones published in the recent years. Robust vector optimization (dpeaa)DE-He213 Robust convex optimization (dpeaa)DE-He213 Vector optimization (dpeaa)DE-He213 Robust strong duality (dpeaa)DE-He213 Vector Farkas lemma (dpeaa)DE-He213 Long, Dang Hai verfasserin aut Enthalten in Vietnam journal of mathematics Singapore : Springer, 1999 46(2018), 2 vom: 17. März, Seite 293-328 (DE-627)300183968 (DE-600)1481450-X 2305-2228 nnns volume:46 year:2018 number:2 day:17 month:03 pages:293-328 https://dx.doi.org/10.1007/s10013-018-0283-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 46 2018 2 17 03 293-328
language	English
source	Enthalten in Vietnam journal of mathematics 46(2018), 2 vom: 17. März, Seite 293-328 volume:46 year:2018 number:2 day:17 month:03 pages:293-328
sourceStr	Enthalten in Vietnam journal of mathematics 46(2018), 2 vom: 17. März, Seite 293-328 volume:46 year:2018 number:2 day:17 month:03 pages:293-328
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Robust vector optimization Robust convex optimization Vector optimization Robust strong duality Vector Farkas lemma
dewey-raw	510
isfreeaccess_bool	false
container_title	Vietnam journal of mathematics
authorswithroles_txt_mv	Dinh, Nguyen @@aut@@ Long, Dang Hai @@aut@@
publishDateDaySort_date	2018-03-17T00:00:00Z
hierarchy_top_id	300183968
dewey-sort	3510
id	SPR008017158
language_de	englisch
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author	Dinh, Nguyen
spellingShingle	Dinh, Nguyen ddc 510 bkl 31.00 misc Robust vector optimization misc Robust convex optimization misc Vector optimization misc Robust strong duality misc Vector Farkas lemma Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems
authorStr	Dinh, Nguyen
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topic_title	510 ASE 31.00 bkl Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems Robust vector optimization (dpeaa)DE-He213 Robust convex optimization (dpeaa)DE-He213 Vector optimization (dpeaa)DE-He213 Robust strong duality (dpeaa)DE-He213 Vector Farkas lemma (dpeaa)DE-He213
topic	ddc 510 bkl 31.00 misc Robust vector optimization misc Robust convex optimization misc Vector optimization misc Robust strong duality misc Vector Farkas lemma
topic_unstemmed	ddc 510 bkl 31.00 misc Robust vector optimization misc Robust convex optimization misc Vector optimization misc Robust strong duality misc Vector Farkas lemma
topic_browse	ddc 510 bkl 31.00 misc Robust vector optimization misc Robust convex optimization misc Vector optimization misc Robust strong duality misc Vector Farkas lemma
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title	Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems
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title_full	Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems
author_sort	Dinh, Nguyen
journal	Vietnam journal of mathematics
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title_sort	complete characterizations of robust strong duality for robust vector optimization problems
title_auth	Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems
abstract	Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex cone in Z, C ⊂ X is a nonempty subset, $\mathcal {U}$ is an uncertainty set, and F : X → Y ∪{ + ∞Y}, Gu : X → Z ∪{ + ∞Z} for all $u\in \mathcal {U}$. The Lagrangian robust dual problem and the weakly Lagrangian robust dual problem for (RVP) are proposed and then principles of robust strong duality for (RVP) associated to these two dual problems in a general setting (not necessarily convex) are established. The results are obtained based mainly on the robust vector Farkas-type results and the representations of epigraphs of conjugate mappings of vector-valued functions, which are the key tools of this paper. Next, we give characterizations of robust strong duality for (RVP) in the convex setting and also for vector optimization problems (i.e., when the uncertainty set is a singleton). To illustrate the ability of applications of the results just obtained, we show that when specifying our general results for (RVP) to robust scalar problems (i.e., when $Y = \mathbb {R}$), we get new principles of robust strong duality for general non-convex robust problems, and various characterizations of robust strong duality for convex problems which cover/extend known results in the literature. In addition, we consider a (scalar) robust infinite problem. It turns out that a further application of the general results obtained for scalar robust problems to this specific problem also leads to various forms of robust strong duality results for the problem in consideration, which are new or cover/extend the known ones published in the recent years.
abstractGer	Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex cone in Z, C ⊂ X is a nonempty subset, $\mathcal {U}$ is an uncertainty set, and F : X → Y ∪{ + ∞Y}, Gu : X → Z ∪{ + ∞Z} for all $u\in \mathcal {U}$. The Lagrangian robust dual problem and the weakly Lagrangian robust dual problem for (RVP) are proposed and then principles of robust strong duality for (RVP) associated to these two dual problems in a general setting (not necessarily convex) are established. The results are obtained based mainly on the robust vector Farkas-type results and the representations of epigraphs of conjugate mappings of vector-valued functions, which are the key tools of this paper. Next, we give characterizations of robust strong duality for (RVP) in the convex setting and also for vector optimization problems (i.e., when the uncertainty set is a singleton). To illustrate the ability of applications of the results just obtained, we show that when specifying our general results for (RVP) to robust scalar problems (i.e., when $Y = \mathbb {R}$), we get new principles of robust strong duality for general non-convex robust problems, and various characterizations of robust strong duality for convex problems which cover/extend known results in the literature. In addition, we consider a (scalar) robust infinite problem. It turns out that a further application of the general results obtained for scalar robust problems to this specific problem also leads to various forms of robust strong duality results for the problem in consideration, which are new or cover/extend the known ones published in the recent years.
abstract_unstemmed	Abstract Consider the robust vector optimization problem of the model(RVP)WMin{F(x):x∈C,Gu(x)∈−S∀u∈U},%$\text{(RVP)}\qquad \text{WMin}\{F(x):~ x\in C,~ G_{u}(x)\in -S~ \forall u\in \mathcal{U}\} , %$ where X, Y, and Z are locally convex Hausdorff topological vector spaces, S is a nonempty convex cone in Z, C ⊂ X is a nonempty subset, $\mathcal {U}$ is an uncertainty set, and F : X → Y ∪{ + ∞Y}, Gu : X → Z ∪{ + ∞Z} for all $u\in \mathcal {U}$. The Lagrangian robust dual problem and the weakly Lagrangian robust dual problem for (RVP) are proposed and then principles of robust strong duality for (RVP) associated to these two dual problems in a general setting (not necessarily convex) are established. The results are obtained based mainly on the robust vector Farkas-type results and the representations of epigraphs of conjugate mappings of vector-valued functions, which are the key tools of this paper. Next, we give characterizations of robust strong duality for (RVP) in the convex setting and also for vector optimization problems (i.e., when the uncertainty set is a singleton). To illustrate the ability of applications of the results just obtained, we show that when specifying our general results for (RVP) to robust scalar problems (i.e., when $Y = \mathbb {R}$), we get new principles of robust strong duality for general non-convex robust problems, and various characterizations of robust strong duality for convex problems which cover/extend known results in the literature. In addition, we consider a (scalar) robust infinite problem. It turns out that a further application of the general results obtained for scalar robust problems to this specific problem also leads to various forms of robust strong duality results for the problem in consideration, which are new or cover/extend the known ones published in the recent years.
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container_issue	2
title_short	Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems
url	https://dx.doi.org/10.1007/s10013-018-0283-1
remote_bool	true
author2	Long, Dang Hai
author2Str	Long, Dang Hai
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doi_str	10.1007/s10013-018-0283-1
up_date	2024-07-03T16:47:12.675Z
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Complete Characterizations of Robust Strong Duality for Robust Vector Optimization Problems

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