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Concept

Algorithme du simplexe

Publications associées (26)

Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls

Daniel Kuhn, Grani Adiwena Hanasusanto

Adaptive robust optimization problems are usually solved approximately by restricting the adaptive decisions to simple parametric decision rules. However, the corresponding approximation error can be substantial. In this paper we show that two-stage robust ...

2018

Convergence of the Exponentiated Gradient Method with Armijo Line Search

Volkan Cevher, Yen-Huan Li

Consider the problem of minimizing a convex differentiable function on the probability simplex, spectrahedron, or set of quantum density matrices. We prove that the expo-nentiated gradient method with Armijo line search always converges to the optimum, if ...

2019

Gravity-Insensitive Flexure Pivot Oscillators

Simon Nessim Henein, Ilan Vardi, Etienne Frédéric Gabriel Thalmann, Mohammad Hussein Kahrobaiyan, Lennart Rubbert

Classical mechanical watch plain bearing pivots have frictional losses limiting the quality factor of the hairspring-balance wheel oscillator. Replacement by flexure pivots leads to a drastic reduction in friction and an order of magnitude increase in qual ...

2018

Development and validation of an intelligent algorithm for synchronizing a low-environmental-impact electricity supply with a building’s electricity consumption

Didier Albert Simon Vuarnoz

Standard algorithm of building’s energy strategy often use electricity and its tariff as the sole criterion of choice. This paper introduced an algorithmic regulation using global warming potential (GWP) of energy flux, to select which installation will sa ...

IOP Publishing2018

Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations

Daniel Kuhn, Peyman Mohajerin Esfahani

We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability distribut ...

2018

On Coordinated Primal-Dual Interior-Point Methods for Multi-Agent Optimization

Colin Neil Jones, Altug Bitlislioglu

This paper presents a coordinated primal-dual interior point (PDIP) method for solving structured convex linear and quadratic programs (LP-QP) in a distributed man- ner. The considered class of problems represents a multi-agent setting, where the aggregate ...

2017