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Evaluating CDCL Restart Schemes

https://doi.org/10.29007/89dw
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-24318-4_29openalex-snapshot
Evaluating CDCL Variable Scoring Schemes
inferredcites →https://doi.org/10.1007/978-3-319-26287-1_14openalex-snapshot
Understanding VSIDS Branching Heuristics in Conflict-Driven Clause-Learning SAT Solvers
inferredcites →https://doi.org/10.1007/978-3-540-72788-0_28openalex-snapshot
A Lightweight Component Caching Scheme for Satisfiability Solvers
inferredcites →https://doi.org/10.1007/978-3-540-79719-7_4openalex-snapshot
Adaptive Restart Strategies for Conflict Driven SAT Solvers
inferredcites →https://doi.org/10.1007/978-3-642-33558-7_11openalex-snapshot
Refining Restarts Strategies for SAT and UNSAT
inferredcites →https://doi.org/10.1007/978-3-642-81952-0_16openalex-snapshot
A Machine Program for Theorem Proving
inferredcites →https://doi.org/10.1007/bf01531077openalex-snapshot
Solving propositional satisfiability problems
inferredcites →https://doi.org/10.1016/0020-0190(93)90029-9openalex-snapshot
Optimal speedup of Las Vegas algorithms
inferredcites →https://doi.org/10.1109/12.769433openalex-snapshot
GRASP: a search algorithm for propositional satisfiability
inferredcites →https://doi.org/10.1109/dac.2001.935565openalex-snapshot
Chaff: engineering an efficient SAT solver
inferredcites →https://doi.org/10.13182/nse70-a19705openalex-snapshot
The Art of Computer Programming
inferredcites →https://doi.org/10.3233/978-1-58603-929-5-131openalex-snapshot
Conflict-Driven Clause Learning SAT Solvers
inferredcites →https://doi.org/10.3233/sat190082openalex-snapshot
Reusing the Assignment Trail in CDCL Solvers
inferredcites →https://doi.org/10.5555/777092.777196openalex-snapshot
Dynamic restart policies

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-17462-0_3openalex-snapshot
Encoding Redundancy for Satisfaction-Driven Clause Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-19955-5_22openalex-snapshot
On the Complexity of Restarting
inferred← citeshttps://doi.org/10.1007/978-3-030-20652-9_10openalex-snapshot
Optimizing a Verified SAT Solver
inferred← citeshttps://doi.org/10.1007/978-3-030-32520-6_7openalex-snapshot
ML Supported Predictions for SAT Solvers Performance
inferred← citeshttps://doi.org/10.1007/978-3-030-51825-7_12openalex-snapshot
On CDCL-Based Proof Systems with the Ordered Decision Strategy
inferred← citeshttps://doi.org/10.1007/978-3-030-51825-7_9openalex-snapshot
Distributed Cube and Conquer with Paracooba
inferred← citeshttps://doi.org/10.1007/978-3-030-58475-7_25openalex-snapshot
Using Resolution Proofs to Analyse CDCL Solvers
inferred← citeshttps://doi.org/10.1007/978-3-030-80223-3_22openalex-snapshot
On Dedicated CDCL Strategies for PB Solvers
inferred← citeshttps://doi.org/10.1007/978-3-031-44505-7_40openalex-snapshot
Unleashing the Potential of Restart by Detecting the Search Stagnation
inferred← citeshttps://doi.org/10.1007/978-3-031-75623-8_15openalex-snapshot
WANCE: Learnt Clause Evaluation Method for SAT Solver Using Graph Structure
inferred← citeshttps://doi.org/10.1007/978-3-319-98334-9_30openalex-snapshot
Learning-Sensitive Backdoors with Restarts
inferred← citeshttps://doi.org/10.1007/s44196-025-00798-4openalex-snapshot
Instance Assignment Coverage Feature for Operation Control of SAT Solver
inferred← citeshttps://doi.org/10.1017/s1471068420000332openalex-snapshot
Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More
inferred← citeshttps://doi.org/10.1109/ssci.2017.8280953openalex-snapshot
Improving performance of CDCL SAT solvers by automated design of variable selection heuristics
inferred← citeshttps://doi.org/10.1109/vlsid64188.2025.00041openalex-snapshot
BMC Engine Sequencing with Graph Neural Network Embeddings of Hardware Circuits
inferred← citeshttps://doi.org/10.1145/3610579openalex-snapshot
Proceedings of the 21st ACM-IEEE International Conference on Formal Methods and Models for System Design
inferred← citeshttps://doi.org/10.1145/3675168openalex-snapshot
MAB-BMC: A Formal Verification Enhancer by Harnessing Multiple BMC Engines Together
inferred← citeshttps://doi.org/10.1145/3706628.3708869openalex-snapshot
SAT-Accel: A Modern SAT Solver on a FPGA
inferred← citeshttps://doi.org/10.1561/9781680838992openalex-snapshot
Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT
inferred← citeshttps://doi.org/10.1609/aaai.v33i01.33012395openalex-snapshot
An Improved Generic Bet-and-Run Strategy with Performance Prediction for Stochastic Local Search
inferred← citeshttps://doi.org/10.1609/aaai.v37i13.26978openalex-snapshot
Understand Restart of SAT Solver Using Search Similarity Index (Student Abstract)
inferred← citeshttps://doi.org/10.1613/jair.1.13666openalex-snapshot
Better Decision Heuristics in CDCL through Local Search and Target Phases
inferred← citeshttps://doi.org/10.24963/ijcai.2018/181openalex-snapshot
Seeking Practical CDCL Insights from Theoretical SAT Benchmarks
inferred← citeshttps://doi.org/10.29007/fj74openalex-snapshot
Analyzing Multiple Conflicts in SAT: An Experimental Evaluation
inferred← citeshttps://doi.org/10.3233/faia200990openalex-snapshot
Chapter 7. Proof Complexity and SAT Solving
inferred← citeshttps://doi.org/10.3390/math10132264openalex-snapshot
A Verified Implementation of the DPLL Algorithm in Dafny
inferred← citeshttps://doi.org/10.48550/arxiv.1706.08611openalex-snapshot
Relating Complexity-theoretic Parameters with SAT Solver Performance
inferred← citeshttps://doi.org/10.48550/arxiv.1806.03600openalex-snapshot
ML + FV = $\heartsuit$? A Survey on the Application of Machine Learning to Formal Verification
inferred← citeshttps://doi.org/10.48550/arxiv.1806.08984openalex-snapshot
An Improved Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search
inferred← citeshttps://doi.org/10.48550/arxiv.2102.03002openalex-snapshot
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
inferred← citeshttps://doi.org/10.70675/932a7c81z8d8dz4199z9e7bz02240dcf3998openalex-snapshot
Reasoning and inference for (maximum) satisfiability : new insights
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