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Localizing Unsynchronized Sensors With Unknown Sources
Publications associées (34)
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Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...
EPFL2023On The Convergence Of Stochastic Primal-Dual Hybrid Gradient
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In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution with convexity and linear convergenc ...
SIAM PUBLICATIONS2022A first-order primal-dual method with adaptivity to local smoothness
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We consider the problem of finding a saddle point for the convex-concave objective , where is a convex function with locally Lipschitz gradient and is convex and possibly non-smooth. We propose an ...
2021Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond
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 ...
EPFL2021Mathematical Foundations of Robust and Distributionally Robust Optimization
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Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertainty set or are governed by any probability distribution from within an ambigu ...
2021Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach
This paper introduces a method for computing points satisfying the second-order necessary optimality conditions for nonconvex minimization problems subject to a closed and convex constraint set. The method comprises two independent steps corresponding to t ...
SPRINGER/PLENUM PUBLISHERS2020A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features
Ali H. Sayed, Sulaiman A S A E Alghunaim, Ming Yan
This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...
IEEE2020On the convergence of stochastic primal-dual hybrid gradient
Volkan Cevher, Ahmet Alacaoglu
In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution and linear convergence with standard ...
2019Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
Nisheeth Vishnoi, Laura Elisa Celis, Vijay Keswani, Lingxiao Huang
Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying cla ...
ASSOC COMPUTING MACHINERY2019Proximity Operators of Discrete Information Divergences
Mireille El Gheche, Giovanni Chierchia
While phi-divergences have been extensively studied in convex analysis, their use in optimization problems often remains challenging. In this regard, one of the main shortcomings of existing methods is that the minimization of phi-divergences is usually pe ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018