Výsledky vyhledávání - "Vincent Guigues"

Duality and sensitivity analysis of multistage linear stochastic programs

Autor: Vincent Guigues, Alexander Shapiro, Yi Cheng

Publikováno v: European Journal of Operational Research. 308:752-767

We investigate the dual of a Multistage Stochastic Linear Program (MSLP) to study two questions for this class of problems. The first of these questions is the study of the optimal value of the problem as a function of the involved parameters. For th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aef662f58091999610dd6b9caadb5409
https://doi.org/10.1016/j.ejor.2022.11.051

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Algorithms and a Library for the Exact Computation of the Cumulative Distribution Function of the Euclidean Distance Between a Point and a Random Variable Uniformly Distributed in Disks, Balls, or Polygones and Application to Probabilistic Seismic Hazard Analysis

Autor: Vincent Guigues

Publikováno v: International Journal of Computational Geometry & Applications. 32:119-174

We consider a random variable expressed as the Euclidean distance between an arbitrary point and a random variable uniformly distributed in a closed and bounded set of a three-dimensional Euclidean space. Four cases are considered for this set: a uni

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c176f83f245022792e10eb94addfb1ba
https://doi.org/10.1142/s0218195922500091

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Stochastic Dynamic Cutting Plane for Multistage Stochastic Convex Programs

Autor: Renato D. C. Monteiro, Vincent Guigues

Publikováno v: Journal of Optimization Theory and Applications. 189:513-559

We introduce Stochastic Dynamic Cutting Plane (StoDCuP), an extension of the Stochastic Dual Dynamic Programming (SDDP) algorithm to solve multistage stochastic convex optimization problems. At each iteration, the algorithm builds lower bounding affi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::18e0b1e3ea51e47b0b8085e12f6ee5a0
https://doi.org/10.1007/s10957-021-01842-x

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Inexact Cuts in Stochastic Dual Dynamic Programming Applied to Multistage Stochastic Nondifferentiable Problems

Autor: Benar Fux Svaiter, Renato D. C. Monteiro, Vincent Guigues

Publikováno v: SIAM Journal on Optimization. 31:2084-2110

In [V. Guigues, SIAM J. Optim., 30 (2020), pp. 407--438], an inexact variant of stochastic dual dynamic programming (SDDP) called ISDDP was introduced which uses approximate (instead of exact with ...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8981fbae9c1a0c03b1f02ebbea4dde86
https://doi.org/10.1137/20m1330075

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Risk-Averse Stochastic Optimal Control: an efficiently computable statistical upper bound

Autor: Vincent Guigues, Alexander Shapiro, Yi Cheng

In this paper, we discuss an application of the SDDP type algorithm to nested risk-averse formulations of Stochastic Optimal Control (SOC) problems. We propose a construction of a statistical upper bound for the optimal value of risk-averse SOC probl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6901b6452b4a0c923756e0ca7ecfe9da
http://arxiv.org/abs/2112.09757

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Constant Depth Decision Rules for multistage optimization under uncertainty

Autor: Arkadi Nemirovski, Anatoli Juditsky, Vincent Guigues

Publikováno v: European Journal of Operational Research
European Journal of Operational Research, 2021, 295 (1), pp.223-232. ⟨10.1016/j.ejor.2021.02.042⟩

In this paper, we introduce a new class of decision rules, referred to as Constant Depth Decision Rules (CDDRs), for multistage optimization under linear constraints with uncertainty-affected right-hand sides. We consider two uncertainty classes: dis

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b56edc671966696da8f8dabc652d96e
https://hal.archives-ouvertes.fr/hal-03185514

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Dual Dynamic Programing with cut selection: Convergence proof and numerical experiments

Autor: Vincent Guigues

Publikováno v: European Journal of Operational Research. 258:47-57

We consider convex optimization problems formulated using dynamic programing equations. Such problems can be solved using the Dual Dynamic Programing algorithm combined with the Level 1 cut selection strategy or the Territory algorithm to select the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b091416393f610f1f9304862926cdb4f
https://doi.org/10.1016/j.ejor.2016.10.047

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Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures

Autor: Vincent Guigues

Publikováno v: Mathematical Programming. 163:169-212

We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable confidence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the S

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea0dad8161189a18cb8be6b1de142308
https://doi.org/10.1007/s10107-016-1060-0

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Inexact cuts in Deterministic and Stochastic Dual Dynamic Programming applied to linear optimization problems

Autor: Vincent Guigues

We introduce an extension of Dual Dynamic Programming (DDP) to solve linear dynamic programming equations. We call this extension IDDP-LP which applies to situations where some or all primal and dual subproblems to be solved along the iterations of t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9457057bba98c30664e247dad8d00c1c
http://arxiv.org/abs/1801.04243

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