- Set-Completely-Positive Representations and Cuts for the Max-Cut Polytope and the Unit Modulus Lifting Florian Jarre (jarrehhu.de) Felix Lieder (Felix.liederhhu.de) Ya-Feng Liu (yafliulsec.cc.ac.cn) Cheng Lu (lucheng1983163.com) Abstract: This paper considers a generalization of the max-cut-polytope'' $\conv\{\ xx^T\mid x\in\real^n, \ \ |x_k| = 1 \ \hbox{for} \ 1\le k\le n\}$ in the space of real symmetric $n\times n$-matrices with all-ones-diagonal to a complex unit modulus lifting'' $\conv\{xx\HH\mid x\in\complex^n, \ \ |x_k| = 1 \ \hbox{for} \ 1\le k\le n\}$ in the space of complex Hermitian $n\times n$-matrices with all-ones-diagonal. The unit modulus lifting arises in applications such as digital communication and shares similar symmetry properties as the max-cut-polytope. Set-completely positive representations of both sets are derived and the relation of the complex unit modulus lifting to its semidefinite relaxation is investigated in dimensions 3 and 4. It is shown that the unit modulus lifting coincides with its semidefinite relaxation in dimension 3 but not in dimension 4. In dimension 4 a family of deep valid cuts for the unit modulus lifting is derived that strengthen the semidefinite relaxation, and also an extension to sharper convex quadratic cuts is derived which yields an optimal approximation to the boundary of the unit modulus lifting. Keywords: Max-cut problem, complex variables, semidefinite relaxation, unit modulus lifting. Category 1: Convex and Nonsmooth Optimization (Convex Optimization ) Category 2: Linear, Cone and Semidefinite Programming (Other ) Citation: Journal of Global Optimization, (2019) https://doi.org/10.1007/s10898-019-00813-x Download: Entry Submitted: 11/06/2017Entry Accepted: 11/06/2017Entry Last Modified: 07/29/2019Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.