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Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion

ICML 2024 · Optimization › Nonconvex and smooth optimization · approach: Extragradient for variational inequalities · 0 citations (OpenAlex)

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Extends the Extra Anchored Gradient (EAG) and Fast Extra Gradient (FEG) algorithms to constrained comonotone min-max optimization and comonotone inclusion, proving O(1/T) last-iterate tangent-residual rates and point convergence in several regimes.

Problem

Comonotone (nonconvex-nonconcave, structured) min-max optimization and composite comonotone inclusion with constraints: existing methods were sub-optimal, unconstrained-only, or lacked point convergence.

Approach

Introduce an auxiliary variable c_k in A(z_k) so that F(z_k)+c_k is an element of (F+A)(z_k), letting the EAG/FEG update be analyzed as if it were unconstrained using a potential function; proves O(1/T) tangent residual rate for rho >= -1/(20L) (composite-EAG) and rho > -1/(2L) (composite-FEG), with point convergence via a merging path to optimized Halpern iteration.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Extend EAG to constrained comonotone inclusion with ρ ≥ -1/(20L)EAG · Comonotone inclusiontangent residual convergence rate: O(1/T)Helped
Extend FEG to constrained comonotone inclusion with ρ > -1/(2L)FEG · Comonotone inclusiontangent residual convergence rate: O(1/T)Helped
Extend EAG to the constrained, negatively comonotone settingEAG · Comonotone inclusion, ρ ≥ -1/(20L)point convergenceHelped
Extend FEG to constrained comonotone inclusionFEG · Comonotone inclusion, ρ = 0point convergenceHelped

Limitations

Experimental setup

Metrics: tangent residual natural residual

Baselines: EG+ CEG+ Double-loop Halpern APG ARG AOG fOGDA

Builds on: Extra Anchored Gradient (EAG) Fast Extragradient (FEG) Optimized Halpern iteration

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