Semidefinite approximations of the Matrix Logarithm

Hamza Fawzi, James Saunderson, Pablo A. Parrilo

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

Abstract

The matrix logarithm, when applied to Hermitian positive definite matrices, is concave with respect to the positive semidefinite order. This operator concavity property leads to numerous concavity and convexity results for other matrix functions, many of which are of importance in quantum information theory. In this paper we show how to approximate the matrix logarithm with functions that preserve operator concavity and can be described using the feasible regions of semidefinite optimization problems of fairly small size. Such approximations allow us to use off-the-shelf semidefinite optimization solvers for convex optimization problems involving the matrix logarithm and related functions, such as the quantum relative entropy. The basic ingredients of our approach apply, beyond the matrix logarithm, to functions that are operator concave and operator monotone. As such, we introduce strategies for constructing semidefinite approximations that we expect will be useful, more generally, for studying the approximation power of functions with small semidefinite representations.

Original languageEnglish
Pages (from-to)259-296
Number of pages38
JournalFoundations of Computational Mathematics
Volume19
Issue number2
DOIs
Publication statusPublished - 1 Apr 2019

Keywords

  • Convex optimization
  • Matrix concavity
  • Quantum relative entropy

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