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SLEPc: Standards & Science

SLEPc is a software library for the parallel computation of eigenvalues and eigenvectors of large, sparse matrices. It can be seen as a module of PETSc that provides solvers for different types of eigenproblems, including linear (standard and generalized) and nonlinear (quadratic, polynomial and general), as well as the SVD. Recent versions also include…

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SLEPc topic overview

The analysis highlights Standards and Science as prominent areas in the source structure around SLEPc.

Related topics
16
Source areas
2
Connected nodes
18
Extracted relationships
13
Concept neighborhoods
13
Bridge connections
18

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Components · 8 topics
Overview · 8 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Available in
C (main language), C++, FORTRAN, Python
License
BSD 2-clause license
Operating system
Linux, Unix, Mac OS X, Windows
Repository
gitlab.com/slepc/slepc
Stable release
3.25 / 31 March 2026; 4 months ago (2026-03-31)
Type
Scientific simulation software

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Components

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How SLEPc connects Entity context

The extracted context around SLEPc shows recurring relationship patterns in the source. For example, SLEPc → C (main language), C++, FORTRAN, Python Another extracted example is SLEPc → BSD 2-clause license. Use these groups to spot repeated connection types before inspecting the individual relationships.

SLEPc

Top relations

Available in · 1
SLEPc → C (main language), C++, FORTRAN, Python
License · 1
SLEPc → BSD 2-clause license
Operating system · 1
SLEPc → Linux, Unix, Mac OS X, Windows
Repository · 1
SLEPc → gitlab.com/slepc/slepc
Stable release · 1
SLEPc → 3.25 / 31 March 2026; 4 months ago (2026-03-31)
Type · 1
SLEPc → Scientific simulation software
Website · 1
SLEPc → slepc.upv.es
is a · 1
SLEPc → software library for the parallel computation of eigenvalues and eigenvectors of large
related to External links · 1
SLEPc → The Official SLEPc

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

solvers petsc solver based polynomial well eigenvalues provides linear generalized matrix standard also software quadratic svd eps eigenvalue problems methods

SLEPc relationships Subject–Predicate–Object triples

TTTA extracted 13 structured relationships around SLEPc. Examples in this analysis include SLEPc → Available in → C (main language), C++, FORTRAN, Python and SLEPc → License → BSD 2-clause license. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
SLEPcAvailable inC (main language), C++, FORTRAN, Python1.00infobox
SLEPcLicenseBSD 2-clause license1.00infobox
SLEPcOperating systemLinux, Unix, Mac OS X, Windows1.00infobox
SLEPcRepositorygitlab.com/slepc/slepc1.00infobox
SLEPcStable release3.25 / 31 March 2026; 4 months ago (2026-03-31)1.00infobox
SLEPcTypeScientific simulation software1.00infobox
SLEPcWebsiteslepc.upv.es1.00infobox
SLEPcis asoftware library for the parallel computation of eigenvalues and eigenvectors of large0.90text
Krylov-Schurinstance ofComponentsEPS provides iterative algorithms for linear eigenvalue problems.Krylov methods0.80text
Arnoldiinstance ofComponentsEPS provides iterative algorithms for linear eigenvalue problems.Krylov methods0.80text
Lanczos.Davidson methods such as Generalized Davidsoninstance ofComponentsEPS provides iterative algorithms for linear eigenvalue problems.Krylov methods0.80text
Jacobi-Davidson.Conjugate gradient methods such as LOBPCG.A contour integral solverinstance ofComponentsEPS provides iterative algorithms for linear eigenvalue problems.Krylov methods0.80text

Related concept clusters Concept neighborhoods

The concept neighborhoods around SLEPc bring nearby vocabulary together. In this analysis, examples include Computation, Scientific and Software. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • petsc
    • Well
    • Slepc
    • General
    • Eigenproblems
    • Eigensolvers
    • Including
    • Nonlinear
    • Preconditioners
    • Quadratic
    • Scientific
    • Standard
    • Support
  • SLEPc
    • Computation
    • Scientific
    • Software
    • Petsc
    • Also
    • External
    • Using
    • Eigenvalue
    • Eigenvalues
    • Eps
    • Linear
    • Problems
  • slepc
    • Computation
    • Scientific
    • Software
    • Petsc
    • Also
    • External
    • Using
    • Eigenvalue
    • Eigenvalues
    • Eps
    • Linear
    • Problems
  • generalized singular value decomposition
    • Svd
    • Well
    • General
    • Including
    • Nonlinear
    • Quadratic
    • Standard
    • Jacobi-davidson
    • Linear
    • Methods
    • Provides
    • Solvers
  • general
    • Including
    • Nonlinear
    • Quadratic
    • Standard
    • Svd
    • Generalized
    • Linear
    • Provides
    • Well
    • Petsc
    • Polynomial
    • Solvers
  • software library
    • Slepc
    • Also
    • Computation
    • External
    • Scientific
    • Eigenvalue
    • Eigenvalues
    • Eps
    • Linear
    • Problems
    • Provides
  • quadratic
    • Standard
    • Svd
    • Eigenvalue
    • Pep
    • Well
    • Solvers
  • matrix functions
    • Rely
    • Support
    • Eps
    • Based
    • Solvers

Connections between topic areas Semantic bridges

For SLEPc, one of the stronger structural bridges in this analysis connects SLEPc with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
SLEPc — Overview · splits 10 ⟂ 9
SLEPc — Components · splits 10 ⟂ 9

Map overview Semantic statistics

SLEPc

Nodes19
Edges18
Triples13
Avg. degree1.89
Density0.105263
Components1

Source & methodology

TTTA analyzes the structure around SLEPc to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Standards & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — SLEPc · EN edition · Analysis: TopicsToTalkAbout

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