Learned online policies for the ADMM relaxation parameter improve iteration count and runtime on benchmark quadratic programs while maintaining convergence guarantees for time-varying parameters under mild assumptions.
Learning to warm-start fixed-point optimization algorithms
2 Pith papers cite this work. Polarity classification is still indexing.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A surrogate for parametric nonconvex optimization is constructed as the minimum of convex-monotonic function compositions and solved via parallel convex optimization, with a proof-of-concept on path tracking.
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Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
Learned online policies for the ADMM relaxation parameter improve iteration count and runtime on benchmark quadratic programs while maintaining convergence guarantees for time-varying parameters under mild assumptions.
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Parametric Nonconvex Optimization via Convex Surrogates
A surrogate for parametric nonconvex optimization is constructed as the minimum of convex-monotonic function compositions and solved via parallel convex optimization, with a proof-of-concept on path tracking.