2012/03/16 by Vijay Bharadwaj, Bharadwaj, Vijay, Peiji Chen +14
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Auction Theory and Applications #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Optimization and Search Problems #cs.DS
paper · pdf · doi:10.48550/arxiv.1203.3619
arxiv created 2012/03/16 · openalex publication_date 2012/03/16 · arxiv updated 2012/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Motivated by the problem of optimizing allocation in guaranteed display advertising, we develop an efficient, lightweight method of generating a compact \em allocation plan that can be used to guide ad server decisions. The plan itself uses just O(1) state per guaranteed contract, is robust to noise, and allows us to serve (provably) nearly optimally. The optimization method we develop is scalable, with a small in-memory footprint, and working in linear time per iteration. It is also "stop-anytime", meaning that time-critical applications can stop early and still get a good serving solution. Thus, it is particularly useful for optimizing the large problems arising in the context of display advertising. We demonstrate the effectiveness of our algorithm using actual Yahoo! data.