Resource-Adaptive Stochastic Gradient Descent for Online Linear Programming without Re-solving

arXiv:2609.28263 · cs.LG, math.OC · Submitted 2026-09-23 · Read on arXiv

cs.LG, math.OC

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Regret — 61× in this paper · explained in 3 episodes
• Degeneracy — 25× in this paper · explained in 3 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Calibration — 9× in this paper · explained in 7 episodes
• Resource-Adaptive Stochastic Gradient Descent — 7× in this paper
• Adaptation — 6× in this paper · explained in 1 episode
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes

Sources

• Beyond Non-Degeneracy: Revisiting Certainty Equivalent Heuristic for Online Linear Programming
• Online Linear Programming with Replenishment
• Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
• Online Resource Allocation with Continuous Random Consumption: Regret under Degeneracy
• Tight Lower Bounds for the Multi-Secretary Problem via Bellman Certificates

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