On the optimal rank-1 approximation of matrices in the Chebyshev norm

The problem of low rank approximation is ubiquitous in science. Traditionally this problem is solved in unitary invariant norms such as Frobenius or spectral norm due to existence of efficient methods for building approximations. However, recent results reveal the potential of low rank approximations in Chebyshev norm, which naturally arises in many applications. In this paper we tackle the problem of building optimal rank-1 approximations in the Chebyshev norm. We investigate the properties of alternating minimization algorithm for building the low rank approximations and demonstrate how to use it to construct optimal rank-1 approximation. As a result we propose an algorithm that is capable of building optimal rank-1 approximations in Chebyshev norm for moderate matrices.(c) 2023 Elsevier Inc. All rights reserved.

Automated summary

The problem of low rank approximation is common in science. Traditionally, it is solved using unitary invariant norms, but recent research has shown potential in the Chebyshev norm. This paper investigates the problem of building optimal rank-1 approximations in the Chebyshev norm and proposes an algorithm for its construction.