Smoothed complexity of local Max-Cut and binary Max-CSP

11/23/2019
by   Xi Chen, et al.
0

We show that the smoothed complexity of the FLIP algorithm for local Max-Cut is at most ϕ n^O(√(log n)), where n is the number of nodes in the graph and ϕ is a parameter that measures the magnitude of perturbations applied on its edge weights. This improves the previously best upper bound of ϕ n^O(log n) by Etscheid and Röglin. Our result is based on an analysis of long sequences of flips, which shows that it is very unlikely for every flip in a long sequence to incur a positive but small improvement in the cut weight. We also extend the same upper bound on the smoothed complexity of FLIP to all binary Maximum Constraint Satisfaction Problems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/25/2023

Smoothed Complexity of SWAP in Local Graph Partitioning

We give the first quasipolynomial upper bound ϕ n^polylog(n) for the smo...
research
05/01/2020

Cutting Bamboo Down to Size

This paper studies the problem of programming a robotic panda gardener t...
research
03/15/2018

Approximating Max-Cut under Graph-MSO Constraints

We consider the max-cut and max-k-cut problems under graph-based constra...
research
09/22/2021

Bounds on approximating Max kXOR with quantum and classical local algorithms

We consider the power of local algorithms for approximately solving Max ...
research
04/24/2021

Improving the filtering of Branch-And-Bound MDD solver (extended)

This paper presents and evaluates two pruning techniques to reinforce th...
research
09/30/2022

TinyTurbo: Efficient Turbo Decoders on Edge

In this paper, we introduce a neural-augmented decoder for Turbo codes c...
research
04/11/2022

Nonlocal Effect on a Generalized Ohta-Kawasaki Model

We propose a nonlocal Ohta-Kawasaki model to study the nonlocal effect o...

Please sign up or login with your details

Forgot password? Click here to reset