Learned inexact alternating minimization bounds optimality gap for convex problems
The paper learns an inexact alternating minimization scheme in which neural networks approximate each subproblem solution. For convex problems with quadratic coupling and a quadratic growth condition, it derives a worst-case optimality-gap bound exposing the per-iteration error networks are trained…
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- 2026-11-10 07:04 · DEV Community — Machine Learning
Learned inexact alternating minimization bounds optimality gap for convex problems