On the integrality gap of the maximum-cut semidefinite programming relaxation in fixed dimension

We describe a factor-revealing convex optimization problem for the integrality gap of the maximum-cut semidefinite programming relaxation: for each n ≥ 2 we present a convex optimization problem whose optimal value is the largest possible ratio between the value of an optimal rank-n solution to the relaxation and the value of an optimal cut. This problem is then used to compute lower bounds for the integrality gap.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/01/2017

The Mixing method: coordinate descent for low-rank semidefinite programming

In this paper, we propose a coordinate descent approach to low-rank stru...
research
02/11/2020

Maximizing Products of Linear Forms, and The Permanent of Positive Semidefinite Matrices

We study the convex relaxation of a polynomial optimization problem, max...
research
12/16/2020

Clustering with Iterated Linear Optimization

We introduce a novel method for clustering using a semidefinite programm...
research
10/16/2020

Strengthened SDP Verification of Neural Network Robustness via Non-Convex Cuts

There have been major advances on the design of neural networks, but sti...
research
05/08/2023

Distributed Detection over Blockchain-aided Internet of Things in the Presence of Attacks

Distributed detection over a blockchain-aided Internet of Things (BIoT) ...
research
05/05/2020

Primes in arithmetic progressions and semidefinite programming

Assuming the generalized Riemann hypothesis, we give asymptotic bounds o...
research
10/21/2020

Riemannian Langevin Algorithm for Solving Semidefinite Programs

We propose a Langevin diffusion-based algorithm for non-convex optimizat...

Please sign up or login with your details

Forgot password? Click here to reset