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.
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Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming
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.