Variance-Based Extragradient Methods with Line Search for Stochastic Variational Inequalities
By Alfredo Noel Iusem, Alejandro Jofré, Roberto Imbuzeiro Oliveira, Philip Thompson. 2019
66 citations recorded by the source corpus.
In this paper, we propose dynamic sampled stochastic approximated (DS-SA) extragradient methods for stochastic variational inequalities (SVIs) that are robust with respect to an unknown Lipschitz c...
Subfields: Artificial Intelligence, Computational Mechanics, Management Science and Operations Research, Optimization & Training
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Simon Lacoste-Julien
- Gauthier Gidel
- Issam Hadj Laradji
- Peter Richtárik
- Sharan Vaswani
- Nicolas Loizou
- Mark W. Schmidt
- Aaron Mishkin
In Computational Mechanics
- Simon Lacoste-Julien
- Gauthier Gidel
- Sharan Vaswani
- Issam Hadj Laradji
- Peter Richtárik
- Mark W. Schmidt
- Aaron Mishkin
- Nicolas Loizou
In Management Science and Operations Research
- Simon Lacoste-Julien
- Issam Hadj Laradji
- Gauthier Gidel
- Sharan Vaswani
- Mark W. Schmidt
- Nicolas Loizou
- Jakub Konecný
- Peter Richtárik
In Optimization & Training
- Simon Lacoste-Julien
- Issam Hadj Laradji
- Gauthier Gidel
- Sharan Vaswani
- Nicolas Loizou
- Peter Richtárik
- Mark W. Schmidt
- Aaron Mishkin
Citations in the corpus
- Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates (2019, methodology): “The “Lipschitz” line-search has been used by previous work in the deterministic [32, 38] and the variance reduced settings [30].”