Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Pedro Pérez-Aros"'
Publikováno v:
Journal of Optimization Theory and Applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::926e5f99d939a8eaacadc08f52d56868
https://doi.org/10.1007/s10957-023-02240-1
https://doi.org/10.1007/s10957-023-02240-1
Publikováno v:
Applied Mathematics & Optimization. 87
In this paper, we study integral functionals defined on spaces of functions with values on general (non-separable) Banach spaces. We introduce a new class of integrands and multifunctions for which we obtain measurable selection results. Then, we pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c3c0f1b5518bfed27d573aa7a941f13
https://doi.org/10.1007/s00245-022-09942-4
https://doi.org/10.1007/s00245-022-09942-4
Publikováno v:
Applied mathematics and optimization. 86(3)
In this paper, we consider a (control) optimization problem, which involves a stochastic dynamic. The model proposes selecting the best control function that keeps bounded a stochastic process over an interval of time with a high probability level. H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::870d09309ad092489ec145c8b8dc1c1c
https://pubmed.ncbi.nlm.nih.gov/36254121
https://pubmed.ncbi.nlm.nih.gov/36254121
Autor:
Pedro Pérez-Aros, Wim van Ackooij
Publikováno v:
Open Journal of Mathematical Optimization. 2:1-29
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06ff679e356a9b02bdb233fd162910cc
https://doi.org/10.5802/ojmo.9
https://doi.org/10.5802/ojmo.9
Publikováno v:
Set-Valued and Variational Analysis. 30:487-519
In many practical applications models exhibiting chance constraints play a role. Since, in practice one is also typically interesting in numerically solving the underlying optimization problems, an interest naturally arises in understanding analytica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::80620b045fb02476525f1b73fcf4a712
https://doi.org/10.1007/s11228-021-00598-w
https://doi.org/10.1007/s11228-021-00598-w
Autor:
Boris Mordukhovich, Pedro Pérez-Aros
Publikováno v:
SIAM Journal on Optimization. 31:3212-3246
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee5e75e3e7d43450e97a0ce29612ff34
https://doi.org/10.1137/21m1392541
https://doi.org/10.1137/21m1392541
Autor:
Emilio Vilches, Pedro Pérez-Aros
Publikováno v:
SIAM Journal on Optimization. 31:1635-1657
In this paper, we investigate the Moreau envelope of the supremum of a family of convex, proper, and lower semicontinuous functions. Under mild assumptions, we prove that the Moreau envelope of a s...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b850b614abcfb405ad9c6ba617ba5999
https://doi.org/10.1137/20m1373517
https://doi.org/10.1137/20m1373517
Publikováno v:
Mathematical Programming. 190:561-583
We show, in Hilbert space setting, that any two convex proper lower semicontinuous functions bounded from below, for which the norm of their minimal subgradients coincide, they coincide up to a constant. Moreover, under classic boundary conditions, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6724367b82d2202c26a2652f0e702910
https://doi.org/10.1007/s10107-020-01550-w
https://doi.org/10.1007/s10107-020-01550-w
Publikováno v:
Mathematical Programming. 189:527-553
This paper develops new extremal principles of variational analysis that are motivated by applications to constrained problems of stochastic programming and semi-infinite programming without smoothness and/or convexity assumptions. These extremal pri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eda91f9269eed82684c03d058a73b967
https://doi.org/10.1007/s10107-020-01548-4
https://doi.org/10.1007/s10107-020-01548-4
Autor:
Wim van Ackooij, Pedro Pérez-Aros
Publikováno v:
Journal of Optimization Theory and Applications. 185:239-269
Probability functions appearing in chance constraints are an ingredient of many practical applications. Understanding differentiability, and providing explicit formulae for gradients, allow us to build nonlinear programming methods for solving these
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b72cce2e0b61c73b72d930a9798f0c25
https://doi.org/10.1007/s10957-020-01634-9
https://doi.org/10.1007/s10957-020-01634-9