arpack
E426675
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.
All labels observed (6)
| Label | Occurrences |
|---|---|
| ARPACK | 1 |
| ARPACK-NG | 1 |
| ARnoldi PACKage | 1 |
| MATLAB eigs function | 1 |
| Octave eigs function | 1 |
| arpack canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4277338 — 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: arpack Context triple: [PCA (scikit-learn), svd_solverOption, arpack]
-
A.
LINPACK
LINPACK is a widely used benchmark and software library for performing numerical linear algebra computations, particularly solving systems of linear equations.
-
B.
Richardson iteration
Richardson iteration is an early iterative method for solving linear systems and other operator equations, based on repeated relaxation steps to progressively improve an approximate solution.
-
C.
LinearAlgebra
LinearAlgebra is Julia’s standard library module providing core functionality for vectors, matrices, and advanced linear algebra operations.
-
D.
Jacobi method
The Jacobi method is an iterative numerical algorithm used to solve systems of linear equations by repeatedly updating each variable using values from the previous iteration.
-
E.
Gauss–Seidel method
The Gauss–Seidel method is an iterative numerical technique used to solve systems of linear equations, particularly in large, sparse problems arising in scientific and engineering computations.
- 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: arpack Target entity description: 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.
-
A.
LINPACK
LINPACK is a widely used benchmark and software library for performing numerical linear algebra computations, particularly solving systems of linear equations.
-
B.
Richardson iteration
Richardson iteration is an early iterative method for solving linear systems and other operator equations, based on repeated relaxation steps to progressively improve an approximate solution.
-
C.
LinearAlgebra
LinearAlgebra is Julia’s standard library module providing core functionality for vectors, matrices, and advanced linear algebra operations.
-
D.
Jacobi method
The Jacobi method is an iterative numerical algorithm used to solve systems of linear equations by repeatedly updating each variable using values from the previous iteration.
-
E.
Gauss–Seidel method
The Gauss–Seidel method is an iterative numerical technique used to solve systems of linear equations, particularly in large, sparse problems arising in scientific and engineering computations.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
eigenvalue solver library
ⓘ
numerical software library ⓘ |
| algorithmType |
Arnoldi method
ⓘ
implicitly restarted Arnoldi method ⓘ |
| designedFor | large-scale eigenvalue computations ⓘ |
| distributionFormat | source code ⓘ |
| feature |
ability to use user-supplied matrix-vector products
ⓘ
iterative Krylov subspace methods ⓘ memory efficiency for large problems ⓘ |
| fullName |
ARnoldi PACKage
ⓘ
linked to:
arpack
|
| hasSuccessor |
ARPACK-NG
ⓘ
linked to:
arpack
|
| implementationLanguage | Fortran 77 ⓘ |
| influenced |
Julia Arpack.jl package
ⓘ
MATLAB eigs function ⓘ
linked to:
arpack
Octave eigs function ⓘ
linked to:
arpack
R RSpectra package ⓘ SciPy ARPACK wrappers ⓘ |
| isOpenSource | true ⓘ |
| license | BSD-style license ⓘ |
| optimizedFor | sparse matrices ⓘ |
| primaryFunction |
computing a few eigenvalues of large sparse matrices
ⓘ
computing corresponding eigenvectors of large sparse matrices ⓘ |
| providesInterface | reverse communication interface ⓘ |
| supportsOperation |
computing eigenvalues near a target shift
ⓘ
computing largest magnitude eigenvalues ⓘ computing smallest magnitude eigenvalues ⓘ shift-and-invert spectral transformation ⓘ |
| supportsProblemType |
complex-valued problems
ⓘ
double precision problems ⓘ generalized eigenvalue problems ⓘ large sparse eigenvalue problems ⓘ nonsymmetric eigenvalue problems ⓘ real-valued problems ⓘ single precision problems ⓘ standard eigenvalue problems ⓘ symmetric eigenvalue problems ⓘ |
| typicalUseCase |
PCA computations
ⓘ
computational chemistry ⓘ computational physics ⓘ data mining ⓘ graph analysis ⓘ machine learning ⓘ scientific computing ⓘ spectral clustering ⓘ structural engineering ⓘ |
| usedBy |
GNU Octave
ⓘ
Julia packages ⓘ MATLAB ⓘ R packages ⓘ SciPy ⓘ many finite element codes ⓘ |
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: arpack Description of subject: 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.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject linked to:
PCA
linked to: arpack