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pro vyhledávání: '"Guigues A"'
Autor:
Guigues, Vincent, Kleywegt, Anton J., Amorim, Giovanni, Krauss, Andre, Nascimento, Victor Hugo
This is the User Manual of LASPATED library. This library is available on GitHub (at https://github.com/vguigues/LASPATED)) and provides a set of tools to analyze spatiotemporal data. A video tutorial for this library is available on Youtube. It is m
Externí odkaz:
http://arxiv.org/abs/2407.13889
This paper develops two parameter-free methods for solving convex and strongly convex hybrid composite optimization problems, namely, a composite subgradient type method and a proximal bundle type method. Both functional and stationary complexity bou
Externí odkaz:
http://arxiv.org/abs/2407.10073
Autor:
Guigues, Vincent, Kleywegt, Anton, Amorim, Giovanni, Krauss, André Mazal, Nascimento, Victor Hugo
We describe methods, tools, and a software library called LASPATED, available on GitHub (at https://github.com/vguigues/) to fit models using spatio-temporal data and space-time discretization. A video tutorial for this library is available on YouTub
Externí odkaz:
http://arxiv.org/abs/2401.04156
This paper considers optimization problems where the objective is the sum of a function given by an expectation and a closed convex composite function, and proposes stochastic composite proximal bundle (SCPB) methods for solving it. Complexity guaran
Externí odkaz:
http://arxiv.org/abs/2207.09024
We introduce two new optimization models for the dispatch of ambulances. The first model, called the ambulance selection problem, is used when an emergency call arrives to decide whether an ambulance should be dispatched for that call, and if so, whi
Externí odkaz:
http://arxiv.org/abs/2203.16371
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:
http://arxiv.org/abs/2112.09757
Autor:
Guigues, Laurent
We consider a very simple dynamical system on weighted graphs which we call Iterative Graph Normalization (IGN) and a variant in which we apply a non-linear activation function to the weights after each normalization. We show that the indicator vecto
Externí odkaz:
http://arxiv.org/abs/2012.07764
Autor:
Guigues, Vincent
We provide three new proofs of the strong concavity of the dual function of some convex optimization problems. For problems with nonlinear constraints, we show that the the assumption of strong convexity of the objective cannot be weakened to convexi
Externí odkaz:
http://arxiv.org/abs/2006.16781
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:
http://arxiv.org/abs/2005.13387
In [13], an Inexact variant of Stochastic Dual Dynamic Programming (SDDP) called ISDDP was introduced which uses approximate (instead of exact with SDDP) primal dual solutions of the problems solved in the forward and backward passes of the method. T
Externí odkaz:
http://arxiv.org/abs/2004.02701