Stochastic gradient ascent with averaging learns Lagrangian multipliers for MILP at the minimax rate Θ(s/√N) and faster Θ(s/N) for warm-start, closing the gap between upper and lower bounds.
European Journal of Operational Research , volume=
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MEMOIR adds branch-local and global memory with a reflection step to tree search for LLM solver synthesis, reaching 96.7% solution validity and 7.3-point score gains over baselines on seven CO problems with lower run-to-run variance.
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