Reproduce “Online Resource Allocation with an Endogenous Markov State: Fewer LP Solves Earn More” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Zhaohua Chen
The paper compares frequent and infrequent linear-program re-solving for resource allocation when actions affect future system states. In a provider-routing model, cluster load changes rewards and resource use, while decisions also change later load. At T=32,000, one degenerate-case experiment reports mean regret of 15,285.072 for frequent re-solving and 1.704 for infrequent re-solving; worst-case guarantees depend on stated assumptions.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.