arXiv Machine Learning September 24, 2026 By Jiameng Lyu

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

Read the original on arXiv Machine Learning →

arXiv:2609. 28263v1 Announce Type: new Abstract: The growth of large language model (LLM) inference and search services increases the scale of online linear programming problems, motivating computationally efficient algorithms.

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llms

arXiv Machine Learning

Jun 15

Optimal Hidden-Target Learning for Online Inventory Optimization on General Convex Sets

arXiv:2606. 14679v1 Announce Type: new Abstract: Online inventory optimization (OIO) is online convex optimization with physical memory: inventory carryover makes the feasible action set depend on the past.

By Anthony Pineci, Yunzong Xu

safety

arXiv Machine Learning

Aug 17

Resource-Adaptive Primal-Dual Learning for One-Warehouse Multi-Store Systems with Censored Demand

arXiv:2608. 14096v1 Announce Type: new Abstract: The one-warehouse multi-store (OWMS) system is a fundamental inventory network in which a nonreplenishable warehouse allocates shared stock across multiple stores over time.

By Jiameng Lyu

benchmarks

arXiv Machine Learning

Aug 4

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection

arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.

By Patrick Helm, Jan-Niklas Doerr, Joren Gijsbrechts, Stefan Minner

rag reinforcement-learning benchmarks

arXiv Machine Learning

Jul 21

Regret Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems: A General Optimization Perspective

arXiv:2407. 04900v2 Announce Type: replace Abstract: Numerous existing studies have examined the performance of Sample Average Approximation (SAA) in the fundamental newsvendor problem.

By Jiameng Lyu, Shilin Yuan, Bingkun Zhou, Yuan Zhou

benchmarks

arXiv Machine Learning

Aug 6

Non-Stationary Inventory Control with Lead Times

arXiv:2602. 05799v2 Announce Type: replace-cross Abstract: We study non-stationary single-item, periodic-review inventory control problems in which the demand distribution is unknown and may change over time.

By Nele H. Amiri, Sean R. Sinclair, Maximiliano Udenio

benchmarks

arXiv Machine Learning

Aug 24

Adaptive Inference for Resource-Constrained Dynamic Pricing

arXiv:2606. 03736v2 Announce Type: replace-cross Abstract: We study resource-constrained dynamic pricing when the seller seeks revenue and valid inference about demand at a price fixed before the selling season.

By Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi

One email a morning, machine-written

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Optimal Hidden-Target Learning for Online Inventory Optimization on General Convex Sets

Resource-Adaptive Primal-Dual Learning for One-Warehouse Multi-Store Systems with Censored Demand

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection

Regret Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems: A General Optimization Perspective

Non-Stationary Inventory Control with Lead Times

Adaptive Inference for Resource-Constrained Dynamic Pricing