LAPACK
E440650
LAPACK is a widely used software library that provides highly optimized routines for numerical linear algebra operations such as solving systems of equations, eigenvalue problems, and singular value decompositions.
All labels observed (1)
| Label | Occurrences |
|---|---|
| LAPACK canonical | 5 |
How this entity was disambiguated
This entity first appeared as the object of triple T4443243 — 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: LAPACK Context triple: [LinearAlgebra, exportsConstant, LAPACK]
-
A.
LINPACK
LINPACK is a widely used benchmark and software library for performing numerical linear algebra computations, particularly solving systems of linear equations.
-
B.
arpack
arpack is a numerical software library for efficiently computing a few eigenvalues and eigenvectors of large sparse matrices, commonly used in scientific computing and machine learning.
-
C.
LinearAlgebra
LinearAlgebra is Julia’s standard library module providing core functionality for vectors, matrices, and advanced linear algebra operations.
-
D.
cuBLAS
cuBLAS is NVIDIA’s GPU-accelerated implementation of the BLAS linear algebra library, providing high-performance matrix and vector operations for CUDA applications.
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E.
Numerical Recipes
Numerical Recipes is a widely used series of books that provides practical algorithms and explanations for numerical methods in scientific computing.
- 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: LAPACK Target entity description: LAPACK is a widely used software library that provides highly optimized routines for numerical linear algebra operations such as solving systems of equations, eigenvalue problems, and singular value decompositions.
-
A.
LINPACK
LINPACK is a widely used benchmark and software library for performing numerical linear algebra computations, particularly solving systems of linear equations.
-
B.
arpack
arpack is a numerical software library for efficiently computing a few eigenvalues and eigenvectors of large sparse matrices, commonly used in scientific computing and machine learning.
-
C.
LinearAlgebra
LinearAlgebra is Julia’s standard library module providing core functionality for vectors, matrices, and advanced linear algebra operations.
-
D.
cuBLAS
cuBLAS is NVIDIA’s GPU-accelerated implementation of the BLAS linear algebra library, providing high-performance matrix and vector operations for CUDA applications.
-
E.
Numerical Recipes
Numerical Recipes is a widely used series of books that provides practical algorithms and explanations for numerical methods in scientific computing.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
numerical linear algebra library
ⓘ
software library ⓘ |
| acronymFor | Linear Algebra PACKage NERFINISHED ⓘ |
| dependsOn | BLAS NERFINISHED ⓘ |
| designedFor | high-performance computing ⓘ |
| domain | numerical linear algebra ⓘ |
| fullName | Linear Algebra PACKage NERFINISHED ⓘ |
| hasComponent |
auxiliary routines
ⓘ
computational routines ⓘ driver routines ⓘ |
| hasInterface |
C
NERFINISHED
ⓘ
C++ ⓘ MATLAB NERFINISHED ⓘ Python NERFINISHED ⓘ R NERFINISHED ⓘ |
| influenced |
MAGMA
NERFINISHED
ⓘ
PLASMA NERFINISHED ⓘ ScaLAPACK NERFINISHED ⓘ |
| license | BSD-style license ⓘ |
| optimizedFor | cache-based architectures ⓘ |
| predecessor |
EISPACK
NERFINISHED
ⓘ
LINPACK NERFINISHED ⓘ |
| provides |
Cholesky factorization
ⓘ
LU factorization ⓘ QR factorization ⓘ Schur decomposition NERFINISHED ⓘ matrix factorization routines ⓘ routines for eigenvalue problems ⓘ routines for singular value decomposition ⓘ routines for solving linear least squares problems ⓘ routines for solving systems of linear equations ⓘ |
| supportsDataType |
double-precision complex
ⓘ
double-precision real ⓘ single-precision complex ⓘ single-precision real ⓘ |
| supportsOperation |
balancing of matrices
ⓘ
computing condition numbers ⓘ computing error bounds ⓘ computing matrix norms ⓘ equilibration of matrices ⓘ generalized eigenvalue problems ⓘ generalized least squares problems ⓘ matrix inversion ⓘ |
| targetPlatform |
multicore processors
ⓘ
shared-memory systems ⓘ |
| usedIn |
data analysis
ⓘ
engineering applications ⓘ machine learning implementations ⓘ scientific computing ⓘ |
| uses | BLAS NERFINISHED ⓘ |
| writtenIn | Fortran NERFINISHED ⓘ |
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: LAPACK Description of subject: LAPACK is a widely used software library that provides highly optimized routines for numerical linear algebra operations such as solving systems of equations, eigenvalue problems, and singular value decompositions.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.