Target Mirror Descent unifies and stabilizes algorithms for monotone variational inequalities via target point correction in the dual update, recovering proximal point, extragradient, and other methods as special cases while supporting geometric ensembles.
A Unified Analysis of Extra- gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.OC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Target Mirror Descent: A Unifying Framework for Solving Monotone Variational Inequalities
Target Mirror Descent unifies and stabilizes algorithms for monotone variational inequalities via target point correction in the dual update, recovering proximal point, extragradient, and other methods as special cases while supporting geometric ensembles.