Mathematical Programming

, Volume 162, Issue 1–2, pp 495–521

Some upper and lower bounds on PSD-rank

Full Length Paper Series A

DOI: 10.1007/s10107-016-1052-0

Cite this article as:
Lee, T., Wei, Z. & de Wolf, R. Math. Program. (2017) 162: 495. doi:10.1007/s10107-016-1052-0
  • 142 Downloads

Abstract

Positive semidefinite rank (PSD-rank) is a relatively new complexity measure on matrices, with applications to combinatorial optimization and communication complexity. We first study several basic properties of PSD-rank, and then develop new techniques for showing lower bounds on the PSD-rank. All of these bounds are based on viewing a positive semidefinite factorization of a matrix M as a quantum communication protocol. These lower bounds depend on the entries of the matrix and not only on its support (the zero/nonzero pattern), overcoming a limitation of some previous techniques. We compare these new lower bounds with known bounds, and give examples where the new ones are better. As an application we determine the PSD-rank of (approximations of) some common matrices.

Keywords

Semidefinite programming Extended formulation PSD-rank Slack matrix 

Mathematics Subject Classification

15A23 68Q17 90C22 

Funding information

Funder NameGrant NumberFunding Note
Singapore National Research Foundation
  • NRF RF Award No. NRF-NRFF2013-13
ERC Consolidator Grant QPROGRESS
    EU STREP project QALGO
    • 600700

    Copyright information

    © Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2016

    Authors and Affiliations

    1. 1.School of Physics and Mathematical SciencesNanyang Technological University and Centre for Quantum TechnologiesSingaporeSingapore
    2. 2.QuSoftCWI and University of AmsterdamAmsterdamThe Netherlands

    Personalised recommendations