Publications associées à Méthode de l'ellipsoïde

A Combination Technique for Optimal Control Problems Constrained by Random PDEs

We present a combination technique based on mixed differences of both spatial approximations and quadrature formulae for the stochastic variables to solve efficiently a class of optimal control problems (OCPs) constrained by random partial differential equ ...

2024

Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

Colin Neil Jones, Yuning Jiang, Wenjie Xu, Bratislav Svetozarevic

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal- ...

IEEE2023

Beyond worst-case analysis, with or without predictions

Andreas Maggiori

In this thesis we design online combinatorial optimization algorithms for beyond worst-case analysis settings. In the first part, we discuss the online matching problem and prove that, in the edge arrival model, no online algorithm can achieve a competiti ...

EPFL2023

Efficient and Accurate Handling of Periodic Flows in Time-Sensitive Networks

Jean-Yves Le Boudec, Seyed Mohammadhossein Tabatabaee

Total Flow Analysis (TFA) is a method for the worst-case analysis of time-sensitive networks. It uses service curve characterizations of the network nodes and arrival curves of flows at their sources; for tractability, the latter are often taken to be line ...

2023

Further Collapses in TFNP

Mika Tapani Göös, Gilbert Théodore Maystre, Alexandros Paul Hollender, Siddhartha Jain, Ran Tao

We show EOPL = PLS ∩ PPAD. Here the class EOPL consists of all total search problems that reduce to the End-of-Potential-Line problem, which was introduced in the works by Hubáček and Yogev (SICOMP 2020) and Fearnley et al. (JCSS 2020). In particular, our ...

Schloss Dagstuhl - Leibniz-Zentrum für Informatik2022

A Newton Frank-Wolfe method for constrained self-concordant minimization

Volkan Cevher, Quoc Tran Dinh

We develop a new Newton Frank-Wolfe algorithm to solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO). Unlike L-smooth convex functions, where the Lipschitz continuity of the objective gradient holds gl ...

SPRINGER2022

Vessel scheduling with pilotage and tugging considerations

Michel Bierlaire

This paper considers vessel scheduling with pilotage and tugging constraints in berthing operations with channel restrictions at seaports. To our knowledge, pilotage and tugging requirements have not been simultaneously considered in the literature. This w ...

PERGAMON-ELSEVIER SCIENCE LTD2021

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness not ...

2021

Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Ahmet Alacaoglu

Stochastic gradient descent (SGD) and randomized coordinate descent (RCD) are two of the workhorses for training modern automated decision systems. Intriguingly, convergence properties of these methods are not well-established as we move away from the spec ...

EPFL2021

Topology-Finding of Tensegrity Structures Considering Global Stability Condition

Yafeng Wang

This study proposes a general framework for topology-finding or topology optimization of tensegrity structures. The existing topology-finding formulation of tensegrity structures based on mixed-integer linear programming (MILP) was improved and transformed ...

2020

Design of a quality control network for an international food company

Philippe Wieser, Nicolas Zufferey, Simone Lydia Voldrich

In this article, the design of a quality control network is considered in an entire supply chain, in the context of an international food and beverage company, denoted as ABC. For each possible control point of the network, two decisions have to be made: ( ...

TAYLOR & FRANCIS INC2020

Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks

Jan Sickmann Hesthaven, Niccolo' Discacciati, Deep Ray

High-order numerical solvers for conservation laws suffer from Gibbs phenomenon close to discontinuities, leading to spurious oscillations and a detrimental effect on the solution accuracy. A possible strategy to reduce it comprises adding a suitable amoun ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2020

Rapid Analysis of Arbitrary-Shaped Conformal Beam-Scanning Arrays

Anja Skrivervik, Denys Nikolayev

Conformal antenna arrays are able to fit seamlessly on curved 3D-shaped surfaces, which are found ubiquitously on vehicles, aircraft, a human body, etc. In addition, conformal structures can overcome the scan loss limitations of conventional planar arrays. ...

IEEE2020

Data-Driven Distributed Reactive Power Sharing in Microgrids

Alireza Karimi, Seyed Sohail Madani

In this paper, reactive power sharing for Photovoltaic (PV) units in islanded microgrids has been formulated as a robust control design problem and is solved using convex optimization method. In addition to reactive power sharing, the disturbance rejection ...

IEEE2020

Design of structures that adapt to loads through large shape changes

Ian Smith, Gennaro Senatore, Arka Prabhata Reksowardojo

Adaptive structures can modify their geometry and internal forces through sensing and mechanical actuation in order to maintain optimal performance under changing actions. Previous work has shown that well-conceived adaptive design strategies achieve subst ...

2020

Accuracy Assessment of Nonlinear Seismic Displacement Demand Predicted by Simplified Methods for the Plateau Range of Design Response Spectra

Pierino Lestuzzi, Lorenzo Diana

The non-linear seismic displacement demand prediction for low-period structures, i.e. with an initial fundamental period situated in the plateau of design response spectra is studied. In Eurocode 8, the computation of seismic displacement demands is essent ...

2019

Seismic displacement demand prediction in non-linear domain: Optimization of the N2 method

Pierino Lestuzzi, Lorenzo Diana

In Europe, computation of displacement demand for seismic assessment of existing buildings is essentially based on a simplified formulation of the N2 method as prescribed by Eurocode 8 (EC8). However, a lack of accuracy of the N2 method in certain conditio ...

2019

Accelerated and adaptive modifier-adaptation schemes for the real-time optimization of uncertain systems

Dominique Bonvin, René Uwe Schneider, Predrag Milosavljevic

The steady-state performance of a parametrically or structurally uncertain system can be optimized using iterative real-time optimization methods such as modifier adaptation. Here, we extend a recently proposed second-order modifier-adaptation scheme in tw ...

ELSEVIER SCI LTD2019