Fixed Parameter Complexity and Approximability of Norm Maximization

TL;DR

This paper analyzes the fixed parameter complexity and approximability of maximizing p-norms over polytopes, revealing fixed parameter tractability for p=1 and W[1]-hardness for other p values, with implications for approximation algorithms.

Contribution

It establishes the fixed parameter tractability for p=1 and W[1]-hardness for p≠1 in norm maximization, and explores the limits of approximation algorithms in this context.

Findings

p=1 case is fixed parameter tractable

p≠1 case is W[1]-hard

Approximation algorithms have limitations depending on accuracy

Abstract

The problem of maximizing the $p$-th power of a $p$-norm over a halfspace-presented polytope in $\R^d$ is a convex maximization problem which plays a fundamental role in computational convexity. It has been shown in 1986 that this problem is $\NP$-hard for all values $p \in \mathbb{N}$, if the dimension $d$ of the ambient space is part of the input. In this paper, we use the theory of parametrized complexity to analyze how heavily the hardness of norm maximization relies on the parameter $d$. More precisely, we show that for $p=1$ the problem is fixed parameter tractable but that for all $p \in \mathbb{N} \setminus \{1\}$ norm maximization is W[1]-hard. Concerning approximation algorithms for norm maximization, we show that for fixed accuracy, there is a straightforward approximation algorithm for norm maximization in FPT running time, but there is no FPT approximation algorithm, the running time of which depends polynomially on the accuracy. As with the $\NP$-hardness of norm maximization, the W[1]-hardness immediately carries over to various radius computation tasks in Computational Convexity.