# Dependent Randomized Rounding for Matroid Polytopes and Applications

## TL;DR

This paper introduces new randomized rounding techniques for matroid polytopes, provides concentration bounds for these methods, and applies them to develop approximation algorithms for submodular maximization and packing problems.

## Contribution

It presents a new randomized swap rounding technique, derives Chernoff-type concentration bounds, and applies these to improve approximation algorithms for submodular and packing problems.

## Key findings

- Chernoff-type bounds for linear functions of the rounding variables
- A lower-tail exponential bound for monotone submodular functions
- Approximation algorithms with (1-1/e-epsilon) guarantees for submodular maximization

## Abstract

Motivated by several applications, we consider the problem of randomly rounding a fractional solution in a matroid (base) polytope to an integral one. We consider the pipage rounding technique and also present a new technique, randomized swap rounding. Our main technical results are concentration bounds for functions of random variables arising from these rounding techniques. We prove Chernoff-type concentration bounds for linear functions of random variables arising from both techniques, and also a lower-tail exponential bound for monotone submodular functions of variables arising from randomized swap rounding. The following are examples of our applications: (1) We give a (1-1/e-epsilon)-approximation algorithm for the problem of maximizing a monotone submodular function subject to 1 matroid and k linear constraints, for any constant k and epsilon>0. (2) We present a result on minimax packing problems that involve a matroid base constraint. We give an O(log m / log log m)-approximation for the general problem Min {lambda: x \in {0,1}^N, x \in B(M), Ax <= lambda b}, where m is the number of packing constraints. (3) We generalize the continuous greedy algorithm to problems involving multiple submodular functions, and use it to find a (1-1/e-epsilon)-approximate pareto set for the problem of maximizing a constant number of monotone submodular functions subject to a matroid constraint. An example is the Submodular Welfare Problem where we are looking for an approximate pareto set with respect to individual players' utilities.