text:   Quadratic Constraints",5,"['New paper alert: <LINK>\n\nWe provide a unified and automated method to analyze first-order methods for smooth &amp; strongly-monotone games. The convergence rate for any first-order method can be obtained via a mechanical procedure of deriving and solving an SDP. <LINK>', 'Using this framework, we are able to recover or even improve known convergence bounds for a variety of algorithms. For example, we can recover the rate bound of the gradient method, proximal point method, and optimistic gradient method.', 'We can also gain new insights and derive new results that were previously unknown. For example, we for the first time provide the global convergence result of negative momentum, which is difficult to obtain using existing approaches.', 'Finally, we are able to extend this framework to a stochastic setting with multiplicative noise. See more details in the paper.', 'Joint work with @XuchanB, @LaurentLessard and @RogerGrosse .']",20,09,924