Parallel simple solvers with near-optimal optimization depth
Parallel simple solvers with near-optimal optimization depth. Current research status is unassessed. The dated source poses this question; inclusion does not certify that it remains open today.
Editorial MSC2020 mapping of Parallel simple solvers with near-optimal optimization depth. https://siagoptimization.github.io/assets/views/ViewsAndNews-33-1.pdf#page=4 Not an author-supplied code.
Problem statement
For the same unit-ball nonsmooth convex problem, can polynomially many simple subgradient-style workers attain about parallel rounds by sharing quadratic-order memory?
Lean 4
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Background
The objective is convex and 1-Lipschitz in Euclidean norm. Error is objective suboptimality. The article asks whether parallel workers can use high shared memory without costly cutting-plane-style iterations.
Progress & source assessment
Current research status is unassessed. The dated source poses this question; inclusion does not certify that it remains open today.
- [1]Jelena Diakonikolas. SIAM Optimization — Open Problems in Optimization (2025).; Parallel simple solvers with near-optimal optimization depth linkIndependently written short question summaries and bibliographic pointers. No blanket redistribution license asserted for the original text.
Recorded progress
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Discussion
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