A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach
A Mokhtari, A Ozdaglar… - … Conference on Artificial …, 2020 - proceedings.mlr.press
In this paper we consider solving saddle point problems using two variants of Gradient
Descent-Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent …
Descent-Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent …
UAV-LEO integrated backbone: A ubiquitous data collection approach for B5G internet of remote things networks
With the advance of unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites,
the integration of space, air and ground networks has become a potential solution to the …
the integration of space, air and ground networks has become a potential solution to the …
Extragradient method: O (1/k) last-iterate convergence for monotone variational inequalities and connections with cocoercivity
Abstract Extragradient method (EG)(Korpelevich, 1976) is one of the most popular methods
for solving saddle point and variational inequalities problems (VIP). Despite its long history …
for solving saddle point and variational inequalities problems (VIP). Despite its long history …
Control performance monitoring and degradation recovery in automatic control systems: A review, some new results, and future perspectives
This paper addresses control performance monitoring (CPM) and degradation recovering in
automatic control systems. It begins with a re-visit of CPM techniques and a summary of the …
automatic control systems. It begins with a re-visit of CPM techniques and a summary of the …
Last-iterate convergence of optimistic gradient method for monotone variational inequalities
Abstract The Past Extragradient (PEG)[Popov, 1980] method, also known as the Optimistic
Gradient method, has known a recent gain in interest in the optimization community with the …
Gradient method, has known a recent gain in interest in the optimization community with the …
Convergence Rate of for Optimistic Gradient and Extragradient Methods in Smooth Convex-Concave Saddle Point Problems
We study the iteration complexity of the optimistic gradient descent-ascent (OGDA) method
and the extragradient (EG) method for finding a saddle point of a convex-concave …
and the extragradient (EG) method for finding a saddle point of a convex-concave …
MIMO PID tuning via iterated LMI restriction
We formulate multi‐input multi‐output proportional integral derivative controller design as an
optimization problem that involves nonconvex quadratic matrix inequalities. We propose a …
optimization problem that involves nonconvex quadratic matrix inequalities. We propose a …
Linear convergence of the primal-dual gradient method for convex-concave saddle point problems without strong convexity
We consider the convex-concave saddle point problem $\min_ {x}\max_ {y} f (x)+ y^\top A xg
(y) $ where $ f $ is smooth and convex and $ g $ is smooth and strongly convex. We prove …
(y) $ where $ f $ is smooth and convex and $ g $ is smooth and strongly convex. We prove …
A data-driven approach to robust control of multivariable systems by convex optimization
The frequency-domain data of a multivariable system in different operating points is used to
design a robust controller with respect to the measurement noise and multimodel …
design a robust controller with respect to the measurement noise and multimodel …
Tight analysis of extra-gradient and optimistic gradient methods for nonconvex minimax problems
Despite the established convergence theory of Optimistic Gradient Descent Ascent (OGDA)
and Extragradient (EG) methods for the convex-concave minimax problems, little is known …
and Extragradient (EG) methods for the convex-concave minimax problems, little is known …