Publications associées à A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization

Publications associées (43)

Slack-variable Approach for Variational Quantum Semidefinite Programming

Solving optimization problems is a key task for which quantum computers could possibly provide a speedup over the best known classical algorithms. Particular classes of optimization problems including semidefinite programming (SDP) and linear programming ( ...

AMER PHYSICAL SOC2025

Robust design optimization of gas-lubricated herringbone grooved journal bearings: Surrogate modeling and experimental validation

Cameron John Bush, Jürg Alexander Schiffmann, Soheyl Massoudi

This study experimentally validates robust designs for herringbone grooved journal bearings (HGJBs) in micro-turbocompressors, addressing manufacturing deviations that lead to rotor instabilities. Experiments on a Pelton-driven rotor system focused on maxi ...

2025

Multi-Period Optimal Power Flow: Convex Relaxations and Parallel Algorithms

Daniel Kuhn, Tianshu Yang

Ensuring reliable operation in power grids given the complexity of modern electricity networks with increasing demands, varying generation and flows present significant computational challenges. Addressing these challenges, we develop efficient methods for ...

Institute of Electrical and Electronics Engineers Inc.2024

Fast Interactive Search under a Scale-Free Comparison Oracle

Matthias Grossglauser, Lucas Maystre, Daniyar Chumbalov, Lars Henning Klein

A comparison-based search algorithm lets a user find a target item t in a database by answering queries of the form, "Which of items i and j is closer to t?" Instead of formulating an explicit query (such as one or several keywords), the user navigates tow ...

2024

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 Ltd2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Scalable constrained optimization

Maria-Luiza Vladarean

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

Tencers: Tension-Constrained Elastic Rods

Mark Pauly, Yingying Ren, Michele Vidulis, Liliane-Joy Yana Dandy

We study ensembles of elastic rods that are tensioned by a small set of inextensible cables. The cables induce forces that deform the initially straight, but flexible rods into 3D space curves at equilibrium. Rods can be open or closed, knotted, and arrang ...

2024

Differential Error Feedback for Communication-Efficient Decentralized Optimization

Ali H. Sayed

Communication-constrained algorithms for decentralized learning and optimization rely on the exchange of quantized signals coupled with local updates. In this context, differential quantization is an effective technique to mitigate the negative impact of q ...

IEEE Computer Society2024

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Philippe Schwaller, Jeff Guo

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

Amer Chemical Soc2024

Abstract homotopy theory in the language of infinity-categories

Samuel Pierre Lavenir

The aim of this thesis is to revisit a selection of classical topics in homotopy theory from the abstract point of view of higher categories. We take care to allow ourselves only model-independent arguments. We prove generalizations of the Hilton-Milnor th ...

EPFL2024

Hypergraph-Based Fast Distributed AC Power Flow Optimization

Colin Neil Jones, Yuning Jiang, Yingzhao Lian

This paper presents a novel distributed approach for solving AC power flow (PF) problems. The optimization problem is reformulated into a distributed form using a communication structure corresponding to a hypergraph, by which complex relationships between ...

IEEE2023

Perturbed Utility Stochastic Traffic Assignment

Rui Yao

This paper develops a fast algorithm for computing the equilibrium assignment with the perturbed utility route choice (PURC) model. Without compromise, this allows the significant advantages of the PURC model to be used in large-scale applications. We form ...

Informs2024

The effect of smooth parametrizations on nonconvex optimization landscapes

Nicolas Boumal

We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...

Springer Heidelberg2024

The statistical complexity of early-stopped mirror descent

Tomas Vaskevicius

Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the statistical guarantees on the excess risk achieved by early-stopped unconstrain ...

Oxford University Press2023

Constrained Form-Finding of Tension-Compression Structures using Automatic Differentiation

Stefana Parascho

This paper proposes a computational approach to form-find pin-jointed bar structures subjected to combinations of tension and compression forces. The generated equilibrium states can meet structural and geometrical constraints via gradient-based optimizati ...

ELSEVIER SCI LTD2023

New Equivalences between Interpolation and SVMs: Kernels and Structured Features

Andrew Duncan Mc Rae

The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick. Recent work has demonstrated that in certain sufficien ...

SIAM PUBLICATIONS2024

Understanding generalization and robustness in modern deep learning

Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Le Nguyen Hoang, Sadegh Farhadkhani, John Stephan

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

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

Colin Neil Jones, Yuning Jiang, Wenjie Xu

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