| Title |
A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality
|
|---|---|
| Published in |
Journal of Automated Reasoning, March 2018
|
| DOI | 10.1007/s10817-018-9455-7 |
| Pubmed ID | |
| Authors |
Jasmin Christian Blanchette, Mathias Fleury, Peter Lammich, Christoph Weidenbach |
| Abstract |
We developed a formal framework for conflict-driven clause learning (CDCL) using the Isabelle/HOL proof assistant. Through a chain of refinements, an abstract CDCL calculus is connected first to a more concrete calculus, then to a SAT solver expressed in a functional programming language, and finally to a SAT solver in an imperative language, with total correctness guarantees. The framework offers a convenient way to prove metatheorems and experiment with variants, including the Davis-Putnam-Logemann-Loveland (DPLL) calculus. The imperative program relies on the two-watched-literal data structure and other optimizations found in modern solvers. We used Isabelle's Refinement Framework to automate the most tedious refinement steps. The most noteworthy aspects of our work are the inclusion of rules for forget, restart, and incremental solving and the application of stepwise refinement. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 7% |
| Unknown | 13 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 2 | 14% |
| Professor | 2 | 14% |
| Student > Postgraduate | 2 | 14% |
| Student > Ph. D. Student | 2 | 14% |
| Student > Bachelor | 1 | 7% |
| Other | 2 | 14% |
| Unknown | 3 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 50% |
| Engineering | 2 | 14% |
| Agricultural and Biological Sciences | 1 | 7% |
| Business, Management and Accounting | 1 | 7% |
| Unknown | 3 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 August 2018.
All research outputs
#18,606,163
of 23,047,237 outputs
Outputs from Journal of Automated Reasoning
#108
of 136 outputs
Outputs of similar age
#258,587
of 332,695 outputs
Outputs of similar age from Journal of Automated Reasoning
#4
of 4 outputs
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