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.
Finite-dimensional variational inequality and nonlinear complementarity problems: A survey of theory, algorithms and applications,
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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.