CDCL SAT solver
E822900
A CDCL SAT solver is an advanced algorithm for solving Boolean satisfiability problems that extends the classic DPLL approach with conflict-driven clause learning and non-chronological backtracking to greatly improve efficiency on large, complex instances.
All labels observed (7)
| Label | Occurrences |
|---|---|
| CDCL | 1 |
| CDCL (conflict-driven clause learning) | 1 |
| CDCL SAT solver canonical | 1 |
| CDCL SAT solvers | 1 |
| MapleSAT | 1 |
| MiniSAT | 1 |
| VSIDS heuristic | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T9809856 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CDCL SAT solver Context triple: [Davis–Putnam algorithm, relatedAlgorithm, CDCL SAT solver]
-
A.
Z3: An Efficient SMT Solver
Z3: An Efficient SMT Solver is a high-performance satisfiability modulo theories (SMT) solver widely used in program verification, formal methods, and automated reasoning.
-
B.
Z3 SMT solver
Z3 SMT solver is a high-performance Satisfiability Modulo Theories (SMT) solver developed at Microsoft Research, widely used in program verification, formal methods, and automated reasoning.
-
C.
Satisfiability Modulo Theories (SMT)
Satisfiability Modulo Theories (SMT) is a framework in computer science and mathematical logic for deciding the satisfiability of logical formulas with respect to background theories such as arithmetic, bit-vectors, arrays, and data types, widely used in verification, synthesis, and automated reasoning.
-
D.
Davis–Putnam algorithm
The Davis–Putnam algorithm is a pioneering procedure in automated theorem proving and propositional logic satisfiability that laid foundational groundwork for modern SAT solvers.
-
E.
TNTSAT
TNTSAT is a French free-to-air satellite television platform that broadcasts the national digital terrestrial TV channels via satellite.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CDCL SAT solver Target entity description: A CDCL SAT solver is an advanced algorithm for solving Boolean satisfiability problems that extends the classic DPLL approach with conflict-driven clause learning and non-chronological backtracking to greatly improve efficiency on large, complex instances.
-
A.
Z3: An Efficient SMT Solver
Z3: An Efficient SMT Solver is a high-performance satisfiability modulo theories (SMT) solver widely used in program verification, formal methods, and automated reasoning.
-
B.
Z3 SMT solver
Z3 SMT solver is a high-performance Satisfiability Modulo Theories (SMT) solver developed at Microsoft Research, widely used in program verification, formal methods, and automated reasoning.
-
C.
Satisfiability Modulo Theories (SMT)
Satisfiability Modulo Theories (SMT) is a framework in computer science and mathematical logic for deciding the satisfiability of logical formulas with respect to background theories such as arithmetic, bit-vectors, arrays, and data types, widely used in verification, synthesis, and automated reasoning.
-
D.
Davis–Putnam algorithm
The Davis–Putnam algorithm is a pioneering procedure in automated theorem proving and propositional logic satisfiability that laid foundational groundwork for modern SAT solvers.
-
E.
TNTSAT
TNTSAT is a French free-to-air satellite television platform that broadcasts the national digital terrestrial TV channels via satellite.
- F. None of above. chosen
Statements (57)
| Predicate | Object |
|---|---|
| instanceOf |
SAT solver
ⓘ
algorithm ⓘ |
| abbreviation |
CDCL
ⓘ
linked to:
CDCL SAT solver
|
| appliedIn |
AI reasoning
ⓘ
constraint solving ⓘ formal methods ⓘ hardware verification ⓘ model checking ⓘ planning ⓘ software verification ⓘ |
| basedOn |
Davis–Putnam–Logemann–Loveland procedure
ⓘ
linked to:
DPLL algorithm
|
| designedFor |
industrial SAT instances
ⓘ
large SAT instances ⓘ |
| exampleImplementation |
CryptoMiniSat
ⓘ
Glucose ⓘ Lingeling ⓘ MapleSAT ⓘ
linked to:
CDCL SAT solver
MiniSAT ⓘ
linked to:
CDCL SAT solver
|
| extends | DPLL algorithm ⓘ |
| fullName | Conflict-Driven Clause Learning SAT solver ⓘ |
| hasFeature |
backjumping
ⓘ
clause activity heuristics ⓘ clause database management ⓘ conflict-driven clause learning ⓘ decision levels ⓘ first UIP learning ⓘ implication graph analysis ⓘ learned clauses ⓘ non-chronological backtracking ⓘ restarts ⓘ unit propagation ⓘ variable activity heuristics ⓘ watched literals ⓘ |
| hasProperty |
backtrackable
ⓘ
clause-learning-based ⓘ complete decision procedure for SAT ⓘ conflict-driven ⓘ incomplete for UNSAT core minimization ⓘ sound ⓘ terminating ⓘ |
| improvesOn | DPLL algorithm ⓘ |
| influenced | modern SMT solving techniques ⓘ |
| introducedInField | propositional satisfiability ⓘ |
| performs |
Boolean constraint propagation
ⓘ
backtracking search ⓘ systematic search ⓘ |
| relatedTo | SMT solver ⓘ |
| solves | Boolean satisfiability problem ⓘ |
| typicalImplementationLanguage |
C
ⓘ
C++ ⓘ |
| uses |
VSIDS heuristic
ⓘ
linked to:
CDCL SAT solver
clause learning scheme ⓘ conflict analysis ⓘ decision heuristic ⓘ implication graph ⓘ phase saving ⓘ restart policy ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: CDCL SAT solver Description of subject: A CDCL SAT solver is an advanced algorithm for solving Boolean satisfiability problems that extends the classic DPLL approach with conflict-driven clause learning and non-chronological backtracking to greatly improve efficiency on large, complex instances.
Referenced by (7)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: CDCL SAT solver
linked to: CDCL SAT solver
linked to: CDCL SAT solver
linked to: CDCL SAT solver
linked to: CDCL SAT solver
linked to: CDCL SAT solver