Friday, 15:15 - 15:40 h, Room: MA 313


Guoyong Gu
Customized proximal point algorithms: A unified approach

Coauthors: Bingsheng He, Xiaoming Yuan


This talk takes a unified look at the customized applications of proximal point algorithms (PPA) to two classes of problems, namely, the linearly constrained convex problem with a generic or separable objective function and a saddle-point problem. We model these two classes of problems as mixed variational inequalities, and show how PPA with customized proximal parameters can yield favorable algorithms, which are able to exploit the structure of the models. Our customized PPA revisit turns out to be a unified approach in designing a number of efficient algorithms, which are competitive with, or even more efficient than some benchmark methods in the existing literature such as the augmented Lagrangian method, the alternating direction method and a class of primal-dual methods, etc. From the PPA perspective, the global convergence and the O(1/t) convergence rate are established in a uniform way.


Talk 1 of the invited session Fri.3.MA 313
"Contraction methods for separable convex optimization in the frame of VIs" [...]
Cluster 3
"Complementarity & variational inequalities" [...]


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