François Glineur
Computational Mechanics · 2010–2015
A researcher in the Science corpus: 4 papers; trusts 16 researchers and is trusted by 55 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 23 citations into it; #740 of 10,001 by trust here, #1,272 by citations
- Artificial Intelligence — 17 citations into it; #4,383 of 35,345 by trust here, #8,706 by citations
- Optimization & Training — 14 citations into it; #1,885 of 2,601 by trust here, #1,766 by citations
- Numerical Analysis — 13 citations into it; #280 of 3,586 by trust here, #258 by citations
- Mathematical Optimization — 11 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
- Management Science and Operations Research — 7 citations into it
- Mathematical Physics — 7 citations into it
Whom François Glineur trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- Guanghui Lan
- Alexandre d'Aspremont
- E.J. Candes
- Zhi-Quan Tom Luo
- Po-Wei Wang
- Yehuda Koren
- Paul Tseng
- Chih-Jen Lin
- D. Leventhal
Who trusts François Glineur overall
- Nicolas Loizou
- Sebastian U. Stich
- Neil Burch
- Guannan Qu
- Dusan Jakovetic
- Peter Richtárik
- Damek Davis
- Mark W. Schmidt
- Reese Pathak
- Qiuwei Li
Papers from François Glineur's perspective
Ordered by how strongly the researchers François Glineur trusts endorse each paper. Same corpus, a different order for every researcher.
- Stochastic Approximation approach to Stochastic Programming
- Gradient methods for minimizing composite functions
- Error bounds and convergence analysis of feasible descent methods: a general approach
- Smooth minimization of non-smooth functions
- Problem Complexity and Method Efficiency in Optimization
- An asynchronous parallel stochastic coordinate descent algorithm
- Iteration complexity of feasible descent methods for convex optimization
- Adaptive Restart for Accelerated Gradient Schemes
- Introductory Lectures on Convex Optimization - A Basic Course
- ImageNet classification with deep convolutional neural networks
Their papers
- First-order methods of smooth convex optimization with inexact oracle (2013) — 588 citations
- Linear convergence of first order methods for non-strongly convex optimization (2015) — 409 citations
- Linear convergence of first order methods under weak nondegeneracy assumptions for convex programming (2015) — 9 citations
- Low-Rank Matrix Approximation with Weights or Missing Data Is NP-Hard (2010) — 201 citations