Towards Ultra Rapid Restarts

02/18/2014
by   Shai Haim, et al.
0

We observe a trend regarding restart strategies used in SAT solvers. A few years ago, most state-of-the-art solvers restarted on average after a few thousands of backtracks. Currently, restarting after a dozen backtracks results in much better performance. The main reason for this trend is that heuristics and data structures have become more restart-friendly. We expect further continuation of this trend, so future SAT solvers will restart even more rapidly. Additionally, we present experimental results to support our observations.

READ FULL TEXT
research
08/05/2019

Learned Clause Minimization in Parallel SAT Solvers

Learned clauses minimization (LCM) let to performance improvements of mo...
research
06/08/2015

On SAT Models Enumeration in Itemset Mining

Frequent itemset mining is an essential part of data analysis and data m...
research
05/11/2020

Designing New Phase Selection Heuristics

CDCL-based SAT solvers have transformed the field of automated reasoning...
research
10/10/2018

Incremental SAT Library Integration Using Abstract Stobjs

We describe an effort to soundly use off-the-shelf incremental SAT solve...
research
10/27/2021

An Experimental Study of Permanently Stored Learned Clauses

Modern CDCL SAT solvers learn clauses rapidly, and an important heuristi...
research
09/02/2021

On Dedicated CDCL Strategies for PB Solvers

Current implementations of pseudo-Boolean (PB) solvers working on native...
research
09/12/2013

Cache Performance Study of Portfolio-Based Parallel CDCL SAT Solvers

Parallel SAT solvers are becoming mainstream. Their performance has made...

Please sign up or login with your details

Forgot password? Click here to reset