Peter Richtarik outlines multi-agent block-coordinate optimization with verified merges
peter_richtarik · x · 2026-10-22
Optimization researcher Peter Richtarik describes a multi-agent parallel optimization scheme: each agent owns one block of a shared solution and searches a corrected local objective that accounts for its impact on all other blocks. Each epoch runs four steps — parallel private search (H steps per agent), verification of every proposal on the global loss, coloring of the dependency graph (κ colors) so blocks sharing terms can't interfere, and merging the highest-gain non-interfering group. The guaranteed inequality F(x^{k+1}) ≤ F(x^k) − (Σ gb)/κ means a verified merge never worsens the shared solution.
Related event: First Convergence Theory for AI Agent Collaboration via Block Optimization(6 posts)→
More from Research
- OpenTac open-sources full-stack 3D tactile sensing ecosystem for any robot surface — anand_bhattad · 2026-10-22
- Deep dive: running RL on heterogeneous hardware — Blackwells for training, TPUs for inference — A_K_Nain · 2026-10-22
- Long Now Labs picks first awardees, including a thermodynamic clock experiment for LLMs — begusgasper · 2026-10-22
- Princeton opens AI Postdoc fellowship in Natural and Artificial Minds, deadline Feb 28 — LakeBrenden · 2026-10-22
- ESMFold2 API adds max accuracy mode after single-sequence precision complaints — proteinrosh · 2026-10-22
- OpenTac team to demo tactile sensors at CoRL with small-scale sensor giveaway — anand_bhattad · 2026-10-22