Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-27T16:55:52.020Z Has data issue: false hasContentIssue false

20 - Submodular Partitioning for Welfare Maximization

Published online by Cambridge University Press:  07 May 2024

Rahul Vaze
Affiliation:
Tata Institute of Fundamental Research, Mumbai, India
Get access

Summary

Introduction

In this chapter, we consider a combinatorial resource allocation problem, called the submodular partition or welfare problem, where the objective is to divide or partition a given set of resources among multiple agents (with a possibly different valuation for each subset of resources), such that the sum of the agents’ valuation (for resources assigned to them) is maximized.

When the agents’ valuations of the subsets of resources is arbitrary, this problem is not only NP-hard, but also APX hard, i.e., it is hard to find even a good approximate solution. Thus, a natural, submodularity assumption is made on the agent valuations, that essentially captures the diminishing returns property, i.e., the incremental increase in any agents’ valuation decreases as more and more resources are assigned to it. Important examples of this problem include combinatorial auctions, e.g., spectrum allocation among various cellular telephone service providers, advertisement-display slot assignments on web platforms, public utility allocations, etc.

Under the submodularity assumption, the partitioning problem becomes approximable. Early research in this direction considered an offline setting, but surprisingly the same ideas are applicable in the online setting as well, but with a slightly weaker guarantee.

In this chapter, for the online submodular partitioning problem, we present a simple greedy algorithm, and derive its competitive ratio, as a function of the curvature of the submodular valuation functions and a new metric called the discriminant. Curvature measures the ‘distance’ as to how far the valuation function is from being linear, while the discriminant counts the amount of improvement made by the greedy algorithm in each iteration. We also discuss some important applications of the submodular partition problem.

Submodular Partition Problem

We begin with a formal definition of a submodular function.

Submodularity captures the diminishing returns property exhibited by or expected to hold for natural utility metrics, i.e., the rate of increase of utility function decreases with an increase in the size of the allocated set.

Alternate and equivalent definitions of submodularity are as follows.

Important examples of submodular functions include set union, entropy, mutual information [347], number of edges crossing a graph cut [348], etc. Showing these quantities are submodular can be easy or hard depending on the choice of the three definitions one chooses.

Type
Chapter
Information
Online Algorithms , pp. 421 - 432
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×