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This paper proposes a regularity-aware stochastic multi-gradient descent method (MoRe) that adaptively switches between conflict-avoidant and scalarization updates. The method achieves improved convergence rates from O~T^{-1/4} to O~T^{-1/2} in nonconvex settings while maintaining per-iterate conflict avoidance.
This paper proposes mirror descent-type algorithms for solving variational inequality problems with functional constraints, proving optimal convergence rates for problems with bounded monotone operators and Lipschitz convex constraints. A modification is introduced to improve efficiency for many constraints.