Francisco Facchinei
Electrical and Electronic Engineering · 2003–2016
A researcher in the Science corpus: 8 papers; trusts 17 researchers and is trusted by 90 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 40 citations into it; #9,604 of 16,539 by trust here, #8,176 by citations
- Computer Networks and Communications — 38 citations into it; #6,708 of 9,886 by trust here, #3,523 by citations
- Computational Mechanics — 23 citations into it; #4,645 of 10,001 by trust here, #5,157 by citations
- Numerical Analysis — 22 citations into it; #61 of 3,586 by trust here, #45 by citations
- Computational Theory and Mathematics — 19 citations into it; #252 of 12,182 by trust here, #271 by citations
- Optimization & Training — 17 citations into it; #2,387 of 2,601 by trust here, #2,378 by citations
- Mathematical Optimization — 12 citations into it; #46 of 422 by trust here, #45 by citations
- Economics and Econometrics — 10 citations into it; #14,851 of 23,481 by trust here, #10,872 by citations
Whom Francisco Facchinei trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jong-Shi Pang
- Dimitri P. Bertsekas
- Yurii E. Nesterov
- M. Powell
- Meisam Razaviyayn
- Zhi-Quan Tom Luo
- Robert Tibshirani
- Francis R. Bach
- D. P. Palomar
- Javier Contreras
Who trusts Francisco Facchinei overall
- Dang Van Hieu
- Masao Fukushima
- Yura Malitsky
- Patrick L. Combettes
- Aviv Gibali
- Jong-Shi Pang
- Angelia Nedić
- Gesualdo Scutari
- D. P. Palomar
- M. Seethharama Gowda
Papers from Francisco Facchinei's perspective
Ordered by how strongly the researchers Francisco Facchinei trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Nonlinear Programming
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Partial Solutions Manual Parallel and Distributed Computation : Numerical Methods
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Regression Shrinkage and Selection via the Lasso
- Distributed multiuser power control for digital subscriber lines (IEEE J. Sel. Areas Commun.)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Finite-Dimensional Variational Inequalities and Complementarity Problems (2003) — 5,830 citations
- Generalized Nash equilibrium problems (2007) — 1,378 citations
- Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems (2013) — 319 citations
- Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory (2016) — 297 citations
- On generalized Nash games and variational inequalities (2007) — 368 citations
- Convex Optimization, Game Theory, and Variational Inequality Theory (2010) — 396 citations
- Exact penalty functions for generalized Nash problems (2006) — 61 citations
- Parallel Selective Algorithms for Nonconvex Big Data Optimization (2014) — 172 citations