🤖 AI Summary
This work addresses the computational challenge in traditional alternating direction method of multipliers (ADMM) for bilinear minimax (saddle-point) optimization problems, where evaluating complex proximal operators is often required. The authors propose a novel ADMM variant that decomposes the original problem into two alternating substeps: a generalized projection onto the constraint set \( S \) and a Euclidean projection onto the set \( C \). The key innovation lies in the exact reformulation—without approximation or linearization—of the ADMM proximal operator under the bilinear structure into a computable generalized projection. By integrating tools from convex analysis and projection techniques, the method establishes a provably convergent and computationally efficient framework, significantly simplifying the solution process for bilinear minimax problems.
📝 Abstract
We consider minimax (saddle-point) problems of the form max_{c \in C} min_{β\in S} g(c; β), where C and S are compact convex sets, and g is concave-convex. Applying the Alternating Direction Method of Multipliers (ADMM) requires evaluating a proximal operator that is, in general, as hard as the original problem. We show that when the outcome function g is bilinear, i.e. g(c; β) = c^T A β, the proximal operator reduces to a generalized projection onto the confidence region S. This reduction is exact -- it involves no approximation or linearization. The resulting ADMM algorithm alternates between (i) a generalized projection onto S and (ii) a Euclidean projection onto C. We describe the derivation, state the algorithm, and discuss convergence.