# Approximating Tensor Norms via Sphere Covering: Bridging the Gap Between Primal and Dual

## TL;DR

This paper introduces deterministic, data-independent algorithms for approximating tensor spectral and nuclear norms using sphere covering techniques, improving bounds and bridging the gap between primal and dual norms.

## Contribution

It presents novel sphere covering constructions that enable the best approximation bounds for both tensor norms, enhancing efficiency and applicability.

## Key findings

- Improved approximation bounds for tensor nuclear norm.
- Deterministic, data-independent algorithms for tensor norms.
- Sphere covering methods applicable to other decision problems.

## Abstract

The matrix spectral and nuclear norms appear in enormous applications. The generalizations of these norms to higher-order tensors is becoming increasingly important but unfortunately they are NP-hard to compute or even approximate. Although the two norms are dual to each other, the best known approximation bound achieved by polynomial-time algorithms for the tensor nuclear norm is worse than that for the tensor spectral norm. In this paper, we bridge this gap by proposing deterministic algorithms with the best bound for both tensor norms. Our methods not only improve the approximation bound for the nuclear norm, but are also data independent and easily implementable comparing to existing approximation methods for the tensor spectral norm. The main idea is to construct a selection of unit vectors that can approximately represent the unit sphere, in other words, a collection of spherical caps to cover the sphere. For this purpose, we explicitly construct several collections of spherical caps for sphere covering with adjustable parameters for different levels of approximations and cardinalities. These readily available constructions are of independent interest as they provide a powerful tool for various decision making problems on spheres and related problems. We believe the ideas of constructions and the applications to approximate tensor norms can be useful to tackle optimization problems over other sets such as the binary hypercube.