Browsing by Author "Guigues, Vincent Gérard Yannick"
Now showing items 1-15 of 15
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Change detection via affine and quadratic detectors
2018The goal of the paper is to develop a specific application of the convex optimization based hypothesis testing techniques developed in A. Juditsky, A. Nemirovski, “Hypothesis testing via affine detectors,” Electronic Journal ... -
Constant Depth Decision Rules for Multistage Stochastic Convex Programs
2020-05In this paper, we introduce a new class of decision rules called Constant Depth Decision Rules (CDDRs) for Multistage Stochastic Convex Programs (MSCP) depending on a parame- ter Mi called the depth of the decision rules. ... -
Convergence analysis of sampling-based decomposition methods for risk-averse multistage stochastic convex programs
2016We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the ... -
Dual Dynamic Programing with cut selection: convergence proof and numerical experiments
2017-04-01We 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 ... -
Exploiting the structure of autoregressive processes in chance-constrained multistage stochastic linear programs
2012-11We consider an interstage dependent stochastic process whose components follow an autoregressive model with time varying order. At a given time, we give some recursive formulae linking future values of the process with ... -
Joint dynamic probabilistic constraints with projected linear decision rules
2016We consider multistage stochastic linear optimization problems combining joint dynamic probabilistic constraints with hard constraints. We develop a method for projecting decision rules onto hard constraints of wait-and-see ... -
Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures
2016We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic ... -
Non-asymptotic confidence bounds for the optimal value of a stochastic program
2016We discuss a general approach to building non-asymptotic confidence bounds for stochastic optimization problems. Our principal contribution is the observation that a Sample Average Approximation of a problem supplies upper ... -
Nonparametric multivariate breakpoint detection for the means, variances, and covariances of a discrete time stochastic process
2012We introduce a nonparametric breakpoint detection method for the means and covariances of a multivariate discrete time stochastic process. Breakpoints are defined as left or right endpoints of maximal intervals of local ... -
Risk-averse feasible policies for large-scale multistage stochastic linear programs
2013-04We consider risk-averse formulations of stochastic linear programs having a structure that is common in real-life applications. Specifically, the optimization problem corresponds to controlling over a certain horizon a ... -
Robust management and pricing of liquefied natural gas contracts with cancelation options
2014Liquefied Natural Gas contracts offer cancelation options that make their pricing difficult, especially if many gas storages need to be taken into account. We develop a valuation mechanism from the buyer's perspective, a ... -
Robust production management
2016The problem of production management can often be cast in the form of a linear program with uncertain parameters and risk constraints. Typically, such problems are treated in the framework of multi-stage Stochastic ... -
SDDP for some interstage dependent risk-averse problems and application to hydro-thermal planning
2014-01We consider interstage dependent stochastic linear programs where both the random right-hand side and the model of the underlying stochastic process have a special structure. Namely, for equality constraints (resp. inequality ...
