Publication
A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization
Publications associées (34)
A ride time-oriented scheduling algorithm for dial-a-ride problems
Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi
This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...
Pergamon-Elsevier Science Ltd2024Augmented Lagrangian Methods for Provable and Scalable Machine Learning
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 ...
EPFL2023Universal and adaptive methods for robust stochastic optimization
Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...
EPFL2023A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling
In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...
2023Adaptation 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 ...
EPFL2021A 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 , where is a convex function with locally Lipschitz gradient and is convex and possibly non-smooth. We propose an ...
2021An Optimal-Storage Approach to Semidefinite Programming Using Approximate Complementarity
This paper develops a new storage-optimal algorithm that provably solves almost all semidefinite programs (SDPs). This method is particularly effective for weakly constrained SDPs under appropriate regularity conditions. The key idea is to formulate an app ...
SIAM PUBLICATIONS2021Yapa: Accelerated Proximal Algorithm For Convex Composite Problems
Mireille El Gheche, Giovanni Chierchia
Proximal splitting methods are standard tools for nonsmooth optimization. While primal-dual methods have become very popular in the last decade for their flexibility, primal methods may still be preferred for two reasons: acceleration schemes are more effe ...
IEEE2021Computational Inverse Design of Surface-based Inflatables
Mark Pauly, Florin Isvoranu, Tian Chen
We present a computational inverse design method for a new class of surface-based inflatable structure. Our deployable structures are fabricated by fusing together two layers of inextensible sheet material along carefully selected curves. The fusing curves ...
ASSOC COMPUTING MACHINERY2021Second-order guarantees in centralized, federated and decentralized nonconvex optimization
Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the s ...
INT PRESS BOSTON, INC2020