A Survey of Adwords Problem With Small Bids In a Primal-dual Setting: Greedy Algorithm, Ranking Algorithm and Primal-dual Training-based Algorithm

TL;DR

This survey reviews various algorithms for the Adwords problem under different input models and small-bid assumptions, focusing on primal-dual methods, duality proofs, and training-based approaches.

Contribution

It provides a comprehensive overview of primal-dual algorithms, including greedy, ranking, and training-based methods, for Adwords with small bids in adversarial and IID models.

Findings

Duality proves efficiency of Greedy and MSVV algorithms in adversarial models.

Primal-dual training-based algorithm effective for IID models.

Comparison of algorithms under different input assumptions.

Abstract

The Adwords problem has always been an interesting internet advertising problem. There are many ways to solve the Adwords problem with the adversarial order model, including the Greedy Algorithm, the Balance Algorithm, and the Scale-bid Algorithm, which is also known as MSVV. In this survey, I will review the Adwords problem with different input models under a primal-dual setting, and with the small-bid assumption. In the first section, I will focus on Adwords with adversarial order model, and use duality to prove the efficiency of the Greedy Algorithm and the MSVV algorithm. Next, I will look at a primal-dual training-based algorithm for the Adwords problem with the IID model.