Minimizing Regret with Multiple Reserves

Abstract

We study the problem of computing and learning non-anonymous reserve prices to maximize revenue. We first define the {\sc Maximizing Multiple Reserves (MMR)} problem in single-parameter matroid environments, where the input is $m$ valuation profiles v^1,...,v^m, indexed by the same n bidders, and the goal is to compute the vector r of (non-anonymous) reserve prices that maximizes the total revenue obtained on these profiles by the VCG mechanism with reserves r. We prove that the problem is APX-hard, even in the special case of single-item environments, and give a polynomial-time 1/2-approximation algorithm for it in arbitrary matroid environments.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 34 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.