Richtárik proves linear convergence for parallel multi-agent local search, with rate α = μ·q(H)/κ
peter_richtarik · x · 2026-10-22
In an ongoing thread, optimization researcher Peter Richtárik states a theorem for multi-agent parallel search: agents explore privately (H steps each), a coordinator verifies proposals on the global loss, then merges non-interfering proposals via graph coloring. The expected gap decays linearly at rate α = μ·q(H)/κ (task geometry × local-search capture / number of colors), down to a floor set by correction errors; exact corrections remove the floor, and the shared solution never gets worse.
Related event: First Convergence Theory for AI Agent Collaboration via Block Optimization(6 posts)→
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