Tightness of LP via Max-product Belief Propagation
| dc.creator | Sanghavi, Sujay | |
| dc.creator | Shah, Devavrat | |
| dc.date | 2005-08-23 | |
| dc.date | 2008-04-12 | |
| dc.date.accessioned | 2026-07-07T09:32:03Z | |
| dc.date.available | 2026-07-07T09:32:03Z | |
| dc.description | We investigate the question of tightness of linear programming (LP) relaxation for finding a maximum weight independent set (MWIS) in sparse random weighted graphs. We show that an edge-based LP relaxation is asymptotically tight for Erdos-Renyi graph $G(n,c/n)$ for $c \leq 2e$ and random regular graph $G(n,r)$ for $r\leq 4$ when node weights are i.i.d. with exponential distribution of mean 1. We establish these results, through a precise relation between the tightness of LP relaxation and convergence of the max-product belief propagation algorithm. We believe that this novel method of understanding structural properties of combinatorial problems through properties of iterative procedure such as the max-product should be of interest in its own right. | |
| dc.identifier | https://arxiv.org/abs/cs/0508097 | |
| dc.identifier | http://arxiv.org/abs/cs/0508097 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/158676 | |
| dc.subject | Data Structures and Algorithms | |
| dc.subject | Discrete Mathematics | |
| dc.title | Tightness of LP via Max-product Belief Propagation | |
| dc.type | text |