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- About project
- Algorithm classification
- All Pairs Shortest Path (APSP)
- Assignment problem
- Auction algorithm
- BFS, C++, Boost Graph Library
- BFS, C++, MPI, Boost Graph Library
- BFS, GAP
- BFS, Java, WebGraph
- BFS, Ligra
- BFS, MPI, Graph500
- BFS, Python, NetworkX
- BFS, Python/C++, NetworKit
- BFS, RCC for CPU
- BFS, RCC for GPU
- BFS, VGL
- Back substitution
- Backward substitution
- Backward substitution, locality
- Backward substitution, scalability
- Bellman-Ford, C++, Boost Graph Library
- Bellman-Ford, Java, JGraphT
- Bellman-Ford, Ligra
- Bellman-Ford, MPI, Graph500
- Bellman-Ford, Nvidia nvGraph
- Bellman-Ford, OpenMP, Stinger
- Bellman-Ford, Python, NetworkX
- Bellman-Ford, locality
- Bellman-Ford, scalability
- Bellman-Ford algorithm
- BiCGStab, HYPRE
- BiCGStab, MIT
- BiCGStab, NVIDIA AmgX
- Biconjugate gradient stabilized method (BiCGStab)
- Binary search, .NET Framework 2.0
- Binary search, C++
- Binary search, Java
- Binary search, Python
- Binary search, locality
- Binary search, С
- Binary search: Finding the position of a target value within a sorted array
- Block Thomas algorithm
- Boruvka's, C++, MPI, Parallel Boost Graph Library
- Boruvka's, RCC for CPU
- Boruvka's, RCC for GPU
- Boruvka's, locality
- Boruvka's, scalability
- Boruvka's algorithm
- Breadth-first search (BFS)
- Cholesky decomposition
- Cholesky decomposition, SCALAPACK
- Cholesky decomposition, locality
- Cholesky decomposition, scalability
- Cholesky method
- Classical orthogonalization method
- Classical point-wise Householder (reflections) method for reducing a matrix to Hessenberg form
- Compact scheme for Gaussian elimination and its modifications: Tridiagonal matrix
- Complete cyclic reduction
- Complete cyclic reduction, locality
- Complete cyclic reduction, scalability
- Construction of the minimum spanning tree (MST)
- Cooley-Tukey, locality
- Cooley-Tukey, scalability
- Cooley–Tukey Fast Fourier Transform, radix-2 case
- Cubature rules
- DCSC algorithm for finding the strongly connected components
- DCSC for finding the strongly connected components, C++, MPI, Parallel Boost Graph Library
- DFS, C++, Boost Graph Library
- DFS, C++, MPI, Parallel Boost Graph Library
- DFS, Python, NetworkX
- Dense matrix-vector multiplication
- Dense matrix-vector multiplication, locality
- Dense matrix-vector multiplication, scalability
- Dense matrix multiplication
- Dense matrix multiplication, locality
- Dense matrix multiplication, scalability
- Dense matrix multiplication (serial version for real matrices)
- Depth-first search (DFS)
- Description of algorithm properties and structure
- Dijkstra's algorithm
- Dijkstra, C++, Boost Graph Library
- Dijkstra, C++, MPI: Parallel Boost Graph Library, 1
- Dijkstra, C++, MPI: Parallel Boost Graph Library, 2
- Dijkstra, Google
- Dijkstra, Python
- Dijkstra, Python/C++
- Dijkstra, VGL, pull
- Dijkstra, VGL, push
- Dijkstra, locality
- Disjoint set union
- Disjoint set union, Boost Graph Library
- Disjoint set union, Java, JGraphT
- Dot product
- Dot product, locality
- Dot product, scalability
- Eigenvalue decomposition (finding eigenvalues and eigenvectors)
- Elimination method, pointwise version
- Face recognition
- Face recognition, scalability
- Fast Fourier transform for powers-of-two
- Finding maximal flow in a transportation network
- Finding minimal-cost flow in a transportation network
- Floyd-Warshall, C++, Boost Graph Library
- Floyd-Warshall, Java, JGraphT
- Floyd-Warshall, Python, NetworkX
- Floyd-Warshall, scalability
- Floyd-Warshall algorithm
- Ford–Fulkerson, C++, Boost Graph Library
- Ford–Fulkerson, Java, JGraphT
- Ford–Fulkerson, Python, NetworkX
- Ford–Fulkerson algorithm
- Forward substitution
- GHS algorithm
- Gabow's edge connectivity algorithm
- Gaussian elimination, compact scheme for tridiagonal matrices, serial variant
- Gaussian elimination, compact scheme for tridiagonal matrices, serial version
- Gaussian elimination, compact scheme for tridiagonal matrices and its modifications
- Gaussian elimination (finding the LU decomposition)
- Gaussian elimination with column pivoting
- Gaussian elimination with complete pivoting
- Gaussian elimination with diagonal pivoting
- Gaussian elimination with row pivoting
- Givens (rotations) method for the QR decomposition of a (real) Hessenberg matrix
- Givens (rotations) method for the QR decomposition of a matrix
- Givens method
- Givens method, locality
- Glossary
- Graph connectivity
- HITS, VGL
- HPCG, locality
- HPCG, scalability
- Help
- Hessenberg QR algorithm as implemented in SCALAPACK
- High Performance Conjugate Gradient (HPCG) benchmark
- Hopcroft–Karp, Java, JGraphT
- Hopcroft–Karp algorithm
- Horners, locality
- Horners method
- Householder (reflections) method for reducing a complex Hermitian matrix to symmetric tridiagonal form
- Householder (reflections) method for reducing a symmetric matrix to tridiagonal form
- Householder (reflections) method for reducing a symmetric matrix to tridiagonal form, SCALAPACK
- Householder (reflections) method for reducing a symmetric matrix to tridiagonal form, locality
- Householder (reflections) method for reducing of a matrix to Hessenberg form
- Householder (reflections) method for the QR decomposition, SCALAPACK
- Householder (reflections) method for the QR decomposition, locality
- Householder (reflections) method for the QR decomposition of a (real) Hessenberg matrix
- Householder (reflections) method for the QR decomposition of a matrix
- Householder (reflections) method for the QR decomposition of a square matrix, real point-wise version
- Householder (reflections) reduction of a matrix to bidiagonal form
- Householder (reflections) reduction of a matrix to bidiagonal form, SCALAPACK
- Householder (reflections) reduction of a matrix to bidiagonal form, locality
- Hungarian, Java, JGraphT
- Hungarian algorithm
- Jacobi (rotations) method for finding singular values
- Johnson's, C++, Boost Graph Library
- Johnson's algorithm
- K-means clustering
- K-means clustering, Accord.NET
- K-means clustering, Apache Mahout
- K-means clustering, Ayasdi
- K-means clustering, CrimeStat
- K-means clustering, ELKI
- K-means clustering, Julia
- K-means clustering, MATLAB
- K-means clustering, MLPACK
- K-means clustering, Mathematica
- K-means clustering, Octave
- K-means clustering, OpenCV
- K-means clustering, R
- K-means clustering, RapidMiner
- K-means clustering, SAP HANA
- K-means clustering, SAS
- K-means clustering, SciPy
- K-means clustering, Spark
- K-means clustering, Stata
- K-means clustering, Torch
- K-means clustering, Weka
- K-means clustering, scalability1
- K-means clustering, scalability2
- K-means clustering, scalability3
- K-means clustering, scalability4
- K-means clustering, scikit-learn
- Kaczmarz's, MATLAB1
- Kaczmarz's, MATLAB2
- Kaczmarz's, MATLAB3
- Kaczmarz's algorithm
- Kruskal's, C++, Boost Graph Library
- Kruskal's, C++, MPI, Parallel Boost Graph Library
- Kruskal's, Java, JGraphT
- Kruskal's, Python, NetworkX
- Kruskal's algorithm
- LU decomposition using Gaussian elimination with pivoting
- LU decomposition using Gaussian elimination without pivoting
- LU decomposition via Gaussian elimination
- LU decomposition via Gaussian elimination, locality
- LU decomposition via Gaussian elimination, scalability
- Lanczos, C++, MPI
- Lanczos, C++, MPI, 2
- Lanczos, C++, MPI, 3
- Lanczos, C, MPI
- Lanczos, MPI, OpenMP
- Lanczos algorithm in exact algorithm (without reorthogonalization)
- Linpack, HPL
- Linpack, locality
- Linpack benchmark
- Longest shortest path
- Longest shortest path, Java, WebGraph
- Longest shortest path, Python/C++, NetworKit
- Matrix decomposition problem
- Meet-in-the-middle attack
- Meet-in-the-middle attack, implementation1
- Meet-in-the-middle attack, implementation2
- Meet-in-the-middle attack, implementation3
- Meet-in-the-middle attack, scalability
- Methods for solving tridiagonal SLAEs
- Newton's method for systems of nonlinear equations
- Newton's method for systems of nonlinear equations, ALIAS C++
- Newton's method for systems of nonlinear equations, Numerical Mathematics - NewtonLib
- Newton's method for systems of nonlinear equations, Numerical Recipes
- Newton's method for systems of nonlinear equations, PETSc
- Newton's method for systems of nonlinear equations, Sundials
- Newton's method for systems of nonlinear equations, parallel1
- Newton's method for systems of nonlinear equations, parallel2
- Newton's method for systems of nonlinear equations, parallel3
- Newton's method for systems of nonlinear equations, scalability1
- Newton's method for systems of nonlinear equations, scalability2
- Newton's method for systems of nonlinear equations, scalability3
- Newton's method for systems of nonlinear equations, scalability4
- Numerical quadrature (cubature) rules on an interval (for a multidimensional cube)
- Numerical quadrature (cubature) rules on an interval (for a multidimensional cube), scalability
- One step of the dqds, LAPACK
- One step of the dqds algorithm
- Open Encyclopedia of Parallel Algorithmic Features
- Orthogonalization method
- Orthogonalization method with reorthogonalization
- PageRank, VGL
- Pairwise summation
- Pairwise summation of numbers
- Pairwise summation of numbers, locality
- Pairwise summation of numbers, scalability
- Parallel prefix scan algorithm using pairwise summation
- Poisson equation, solving with DFT
- Poisson equation, solving with DFT, AccFFT
- Poisson equation, solving with DFT, FFTE
- Poisson equation, solving with DFT, FFTW
- Poisson equation, solving with DFT, MKL FFT
- Poisson equation, solving with DFT, P3DFFT
- Poisson equation, solving with DFT, PFFT
- Poisson equation, solving with DFT, cuFFT
- Poisson equation, solving with DFT, locality
- Poisson equation, solving with DFT, scalability
- Preflow-Push, C++, Boost Graph Library
- Preflow-Push, Python, NetworkX
- Preflow-Push algorithm
- Prim's, C++, Boost Graph Library
- Prim's, Java, JGraphT
- Prim's algorithm
- Purdom's, Boost Graph Library
- Purdom's algorithm
- QR algorithm
- QR algorithm as implemented in SCALAPACK
- QR algorithm for complex Hermitian matrices as implemented in SCALAPACK
- QR decomposition methods for dense Hessenberg matrices
- QR decomposition of dense nonsingular matrices
- Reducing matrices to compact forms
- Repeated Thomas, locality
- Repeated Thomas algorithm, pointwise version
- Repeated two-sided Thomas, locality
- Repeated two-sided Thomas algorithm, pointwise version
- SDDP, scalability
- Search for isomorphic subgraphs
- Serial-parallel algorithm for the LU decomposition of a tridiagonal matrix
- Serial-parallel method for solving tridiagonal matrices based on the LU decomposition and backward substitutions
- Serial Jacobi (rotations) method for symmetric matrices
- Serial Jacobi (rotations) method with thresholds for symmetric matrices
- Shiloach-Vishkin algorithm for finding the connected components
- Single-qubit transform of a state vector
- Single-qubit transform of a state vector, locality
- Single-qubit transform of a state vector, scalability
- Single Source Shortest Path (SSSP)
- Singular value decomposition (finding singular values and singular vectors)
- Stochastic dual dynamic programming (SDDP)
- Stone doubling algorithm
- Stone doubling algorithm for solving bidiagonal SLAEs
- Stone doubling algorithm for the LU decomposition of a tridiagonal matrix
- Symmetric QR algorithm as implemented in SCALAPACK
- Tarjan's algorithm for finding the bridges of a graph
- Tarjan's biconnected components, C++, Boost Graph Library
- Tarjan's biconnected components, Java, JGraphT
- Tarjan's biconnected components, Python, NetworkX
- Tarjan's biconnected components algorithm
- Tarjan's strongly connected components, C++, Boost Graph Library
- Tarjan's strongly connected components, Java, JGraphT
- Tarjan's strongly connected components, Java, WebGraph
- Tarjan's strongly connected components, Python, NetworkX
- Tarjan's strongly connected components, Python/C++, NetworKit
- Tarjan's strongly connected components algorithm
- Tarjan-Vishkin biconnected components, scalability
- Tarjan-Vishkin biconnected components algorithm
- The Jacobi (rotations) method for solving the symmetric eigenvalue problem
- The classical Jacobi (rotations) method with pivoting for symmetric matrices
- The dqds algorithm for calculating singular values of bidiagonal matrices
- The serial-parallel summation method
- The serial-parallel summation method, locality
- The serial-parallel summation method, scalability
- Thomas, locality
- Thomas algorithm
- Thomas algorithm, locality
- Thomas algorithm, pointwise version
- Transitive closure of a directed graph
- Triangular decomposition of a Gram matrix
- Triangular decompositions
- Two-qubit transform of a state vector
- Two-sided Thomas, locality
- Two-sided Thomas algorithm, block variant
- Two-sided Thomas algorithm, pointwise version
- Ullman's, C++, Chemical Descriptors Library
- Ullman's, C++, VF Library
- Ullman's algorithm
- Uniform norm of a vector, locality
- Uniform norm of a vector: Real version, serial-parallel variant
- Unitary-triangular factorizations
- Unitary reductions to Hessenberg form
- Unitary reductions to tridiagonal form
- VF2, C++, Boost Graph Library
- VF2, C++, VF Library
- VF2, Python, NetworkX
- VF2 algorithm
- Vertex connectivity of a graph
- Δ-stepping, C++, MPI, Parallel Boost Graph Library
- Δ-stepping, Gap
- Δ-stepping algorithm
- Участник:VolkovNikita94
- Шаблон:Buttonlinkimp
