Derives O(s^{1.5}/√N) generalization bound, Ω(s/√N) minimax lower bound, and shows SGA with averaging attains Θ(s/√N) optimal rate for data-driven Lagrangian relaxation in MILPs, plus faster Θ(s/N) rate for warm-start learning.
Advances in Neural Information Processing Systems , volume=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming
Derives O(s^{1.5}/√N) generalization bound, Ω(s/√N) minimax lower bound, and shows SGA with averaging attains Θ(s/√N) optimal rate for data-driven Lagrangian relaxation in MILPs, plus faster Θ(s/N) rate for warm-start learning.