Dynamic Pricing and Demand Learning on a Large Network of Products: A PAC-Bayesian Approach

By B. Keskin et al.

Published on Dec. 18, 2021

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Table of Contents

1. Introduction
1.1. Related Work
1.2. Overview of Main Contributions
2. Model and Preliminaries
2.1. L0 Sparsity
2.2. Off-Diagonal Sparsity
2.3. Spectral Sparsity

Summary

This document discusses dynamic pricing strategies for a seller offering a large network of products over a time horizon. It introduces various sparsity frameworks to optimize pricing policies based on demand learning. The paper presents different approaches to achieve optimal revenue performance in dynamic pricing scenarios with multiple products. It also explores how the size and structure of the product network affect learning performance and revenue optimization.