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Showing below up to 321 results in range #1 to #321.
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- Backward substitution, scalability (2 categories)
- Prim's, Java, JGraphT (2 categories)
- Bellman-Ford, locality (2 categories)
- QR decomposition of dense nonsingular matrices (2 categories)
- Binary search, C++ (2 categories)
- Serial-parallel algorithm for the LU decomposition of a tridiagonal matrix (2 categories)
- Boruvka's, RCC for CPU (2 categories)
- Single Source Shortest Path (SSSP) (2 categories)
- Cholesky decomposition, locality (2 categories)
- Tarjan's biconnected components, C++, Boost Graph Library (2 categories)
- Complete cyclic reduction, scalability (2 categories)
- Tarjan's strongly connected components, Python/C++, NetworKit (2 categories)
- DFS, C++, Boost Graph Library (2 categories)
- The serial-parallel summation method, locality (2 categories)
- Dense matrix multiplication, scalability (2 categories)
- Triangular decompositions (2 categories)
- Dijkstra, Python (2 categories)
- Uniform norm of a vector, locality (2 categories)
- Dot product (2 categories)
- VF2 algorithm (2 categories)
- Finding minimal-cost flow in a transportation network (2 categories)
- Ford–Fulkerson, Python, NetworkX (2 categories)
- Gaussian elimination (finding the LU decomposition) (2 categories)
- Givens method, locality (2 categories)
- Hopcroft–Karp, Java, JGraphT (2 categories)
- Householder (reflections) method for reducing of a matrix to Hessenberg form (2 categories)
- Householder (reflections) reduction of a matrix to bidiagonal form, locality (2 categories)
- K-means clustering, Apache Mahout (2 categories)
- K-means clustering, Octave (2 categories)
- K-means clustering, Stata (2 categories)
- Kaczmarz's, MATLAB1 (2 categories)
- Kruskal's algorithm (2 categories)
- Lanczos, C++, MPI, 3 (2 categories)
- Longest shortest path, Java, WebGraph (2 categories)
- Methods for solving tridiagonal SLAEs (2 categories)
- Newton's method for systems of nonlinear equations, parallel2 (2 categories)
- One step of the dqds, LAPACK (2 categories)
- All Pairs Shortest Path (APSP) (2 categories)
- Pairwise summation of numbers, scalability (2 categories)
- BFS, MPI, Graph500 (2 categories)
- Poisson equation, solving with DFT, PFFT (2 categories)
- Bellman-Ford, C++, Boost Graph Library (2 categories)
- Prim's algorithm (2 categories)
- Bellman-Ford, scalability (2 categories)
- Reducing matrices to compact forms (2 categories)
- Binary search, Java (2 categories)
- Serial-parallel method for solving tridiagonal matrices based on the LU decomposition and backward substitutions (2 categories)
- Boruvka's, RCC for GPU (2 categories)
- Singular value decomposition (finding singular values and singular vectors) (2 categories)
- Cholesky decomposition, scalability (2 categories)
- Tarjan's biconnected components, Java, JGraphT (2 categories)
- Construction of the minimum spanning tree (MST) (2 categories)
- Tarjan's strongly connected components algorithm (2 categories)
- DFS, C++, MPI, Parallel Boost Graph Library (2 categories)
- The serial-parallel summation method, scalability (2 categories)
- Dense matrix multiplication (serial version for real matrices) (2 categories)
- Two-qubit transform of a state vector (2 categories)
- Dijkstra, Python/C++ (2 categories)
- Uniform norm of a vector: Real version, serial-parallel variant (2 categories)
- Dot product, locality (2 categories)
- Vertex connectivity of a graph (2 categories)
- Floyd-Warshall, C++, Boost Graph Library (2 categories)
- Ford–Fulkerson algorithm (2 categories)
- Gaussian elimination with column pivoting (2 categories)
- Hopcroft–Karp algorithm (2 categories)
- Householder (reflections) method for the QR decomposition, SCALAPACK (2 categories)
- Hungarian, Java, JGraphT (2 categories)
- K-means clustering, Ayasdi (2 categories)
- K-means clustering, OpenCV (2 categories)
- K-means clustering, Torch (2 categories)
- Kaczmarz's, MATLAB2 (2 categories)
- LU decomposition using Gaussian elimination with pivoting (2 categories)
- Lanczos, C, MPI (2 categories)
- Longest shortest path, Python/C++, NetworKit (2 categories)
- Newton's method for systems of nonlinear equations (2 categories)
- Newton's method for systems of nonlinear equations, parallel3 (2 categories)
- One step of the dqds algorithm (2 categories)
- Assignment problem (2 categories)
- Parallel prefix scan algorithm using pairwise summation (2 categories)
- BFS, Python, NetworkX (2 categories)
- Poisson equation, solving with DFT, cuFFT (2 categories)
- Poisson equation, solving with DFT, locality (2 categories)
- Bellman-Ford, Java, JGraphT (2 categories)
- Purdom's, Boost Graph Library (2 categories)
- Bellman-Ford algorithm (2 categories)
- Repeated Thomas, locality (2 categories)
- Binary search, Python (2 categories)
- Serial Jacobi (rotations) method for symmetric matrices (2 categories)
- Boruvka's, locality (2 categories)
- Stochastic dual dynamic programming (SDDP) (2 categories)
- Cholesky method (2 categories)
- Tarjan's biconnected components, Python, NetworkX (2 categories)
- Cooley-Tukey, locality (2 categories)
- Tarjan-Vishkin biconnected components, scalability (2 categories)
- DFS, Python, NetworkX (2 categories)
- Thomas, locality (2 categories)
- Depth-first search (DFS) (2 categories)
- Two-sided Thomas, locality (2 categories)
- Dijkstra, VGL, pull (2 categories)
- Unitary-triangular factorizations (2 categories)
- Dot product, scalability (2 categories)
- Δ-stepping, C++, MPI, Parallel Boost Graph Library (2 categories)
- Floyd-Warshall, Java, JGraphT (2 categories)
- Forward substitution (2 categories)
- Gaussian elimination with complete pivoting (2 categories)
- Graph connectivity (2 categories)
- Horners, locality (2 categories)
- Householder (reflections) method for the QR decomposition, locality (2 categories)
- Hungarian algorithm (2 categories)
- K-means clustering, CrimeStat (2 categories)
- K-means clustering, R (2 categories)
- K-means clustering, Weka (2 categories)
- Kaczmarz's, MATLAB3 (2 categories)
- LU decomposition using Gaussian elimination without pivoting (2 categories)
- Lanczos, MPI, OpenMP (2 categories)
- Matrix decomposition problem (2 categories)
- Newton's method for systems of nonlinear equations, ALIAS C++ (2 categories)
- Newton's method for systems of nonlinear equations, scalability1 (2 categories)
- Orthogonalization method (2 categories)
- Auction algorithm (2 categories)
- Poisson equation, solving with DFT (2 categories)
- BFS, Python/C++, NetworKit (2 categories)
- Poisson equation, solving with DFT, scalability (2 categories)
- Bellman-Ford, Ligra (2 categories)
- Purdom's algorithm (2 categories)
- BiCGStab, HYPRE (2 categories)
- Repeated Thomas algorithm, pointwise version (2 categories)
- Binary search, locality (2 categories)
- Serial Jacobi (rotations) method with thresholds for symmetric matrices (2 categories)
- Boruvka's, scalability (2 categories)
- Stone doubling algorithm (2 categories)
- Classical orthogonalization method (2 categories)
- Tarjan's biconnected components algorithm (2 categories)
- Cooley-Tukey, scalability (2 categories)
- Tarjan-Vishkin biconnected components algorithm (2 categories)
- Dense matrix-vector multiplication (2 categories)
- Thomas algorithm (2 categories)
- Dijkstra's algorithm (2 categories)
- Two-sided Thomas algorithm, block variant (2 categories)
- Dijkstra, VGL, push (2 categories)
- Unitary reductions to Hessenberg form (2 categories)
- Eigenvalue decomposition (finding eigenvalues and eigenvectors) (2 categories)
- Δ-stepping, Gap (2 categories)
- Floyd-Warshall, Python, NetworkX (2 categories)
- GHS algorithm (2 categories)
- Gaussian elimination with diagonal pivoting (2 categories)
- HITS, VGL (2 categories)
- Horners method (2 categories)
- Jacobi (rotations) method for finding singular values (2 categories)
- K-means clustering, ELKI (2 categories)
- K-means clustering, RapidMiner (2 categories)
- K-means clustering, scalability1 (2 categories)
- Kaczmarz's algorithm (2 categories)
- LU decomposition via Gaussian elimination (2 categories)
- Lanczos algorithm in exact algorithm (without reorthogonalization) (2 categories)
- Meet-in-the-middle attack (2 categories)
- Newton's method for systems of nonlinear equations, Numerical Mathematics - NewtonLib (2 categories)
- Newton's method for systems of nonlinear equations, scalability2 (2 categories)
- Orthogonalization method with reorthogonalization (2 categories)
- BFS, C++, Boost Graph Library (2 categories)
- Poisson equation, solving with DFT, AccFFT (2 categories)
- BFS, RCC for CPU (2 categories)
- Preflow-Push, C++, Boost Graph Library (2 categories)
- Bellman-Ford, MPI, Graph500 (2 categories)
- QR algorithm (2 categories)
- BiCGStab, MIT (2 categories)
- Repeated two-sided Thomas, locality (2 categories)
- Binary search, С (2 categories)
- Shiloach-Vishkin algorithm for finding the connected components (2 categories)
- Boruvka's algorithm (2 categories)
- Stone doubling algorithm for solving bidiagonal SLAEs (2 categories)
- Classical point-wise Householder (reflections) method for reducing a matrix to Hessenberg form (2 categories)
- Tarjan's strongly connected components, C++, Boost Graph Library (2 categories)
- Cooley–Tukey Fast Fourier Transform, radix-2 case (2 categories)
- The Jacobi (rotations) method for solving the symmetric eigenvalue problem (2 categories)
- Dense matrix-vector multiplication, locality (2 categories)
- Thomas algorithm, locality (2 categories)
- Dijkstra, C++, Boost Graph Library (2 categories)
- Two-sided Thomas algorithm, pointwise version (2 categories)
- Dijkstra, locality (2 categories)
- Unitary reductions to tridiagonal form (2 categories)
- Face recognition (2 categories)
- Δ-stepping algorithm (2 categories)
- Floyd-Warshall, scalability (2 categories)
- Gabow's edge connectivity algorithm (2 categories)
- Gaussian elimination with row pivoting (2 categories)
- HPCG, locality (2 categories)
- Householder (reflections) method for reducing a complex Hermitian matrix to symmetric tridiagonal form (2 categories)
- Householder (reflections) method for the QR decomposition of a matrix (2 categories)
- Johnson's, C++, Boost Graph Library (2 categories)
- K-means clustering, Julia (2 categories)
- K-means clustering, SAP HANA (2 categories)
- K-means clustering, scalability2 (2 categories)
- Kruskal's, C++, Boost Graph Library (2 categories)
- LU decomposition via Gaussian elimination, locality (2 categories)
- Linpack, HPL (2 categories)
- Meet-in-the-middle attack, implementation1 (2 categories)
- Newton's method for systems of nonlinear equations, Numerical Recipes (2 categories)
- Newton's method for systems of nonlinear equations, scalability3 (2 categories)
- PageRank, VGL (2 categories)
- BFS, C++, MPI, Boost Graph Library (2 categories)
- Poisson equation, solving with DFT, FFTE (2 categories)
- BFS, RCC for GPU (2 categories)
- Preflow-Push, Python, NetworkX (2 categories)
- Bellman-Ford, Nvidia nvGraph (2 categories)
- QR algorithm as implemented in SCALAPACK (2 categories)
- BiCGStab, NVIDIA AmgX (2 categories)
- Repeated two-sided Thomas algorithm, pointwise version (2 categories)
- Binary search: Finding the position of a target value within a sorted array (2 categories)
- Single-qubit transform of a state vector (2 categories)
- Breadth-first search (BFS) (2 categories)
- Stone doubling algorithm for the LU decomposition of a tridiagonal matrix (2 categories)
- Compact scheme for Gaussian elimination and its modifications: Tridiagonal matrix (2 categories)
- Tarjan's strongly connected components, Java, JGraphT (2 categories)
- Cubature rules (2 categories)
- The classical Jacobi (rotations) method with pivoting for symmetric matrices (2 categories)
- Dense matrix-vector multiplication, scalability (2 categories)
- Thomas algorithm, pointwise version (2 categories)
- Dijkstra, C++, MPI: Parallel Boost Graph Library, 1 (2 categories)
- Ullman's, C++, Chemical Descriptors Library (2 categories)
- Disjoint set union (2 categories)
- VF2, C++, Boost Graph Library (2 categories)
- Face recognition, scalability (2 categories)
- Floyd-Warshall algorithm (2 categories)
- Gaussian elimination, compact scheme for tridiagonal matrices, serial variant (2 categories)
- HPCG, scalability (2 categories)
- Householder (reflections) method for reducing a symmetric matrix to tridiagonal form (2 categories)
- Householder (reflections) method for the QR decomposition of a square matrix, real point-wise version (2 categories)
- Johnson's algorithm (2 categories)
- K-means clustering, MATLAB (2 categories)
- K-means clustering, SAS (2 categories)
- K-means clustering, scalability3 (2 categories)
- Kruskal's, C++, MPI, Parallel Boost Graph Library (2 categories)
- LU decomposition via Gaussian elimination, scalability (2 categories)
- Linpack, locality (2 categories)
- Meet-in-the-middle attack, implementation2 (2 categories)
- Newton's method for systems of nonlinear equations, PETSc (2 categories)
- Newton's method for systems of nonlinear equations, scalability4 (2 categories)
- Pairwise summation (2 categories)
- BFS, GAP (2 categories)
- Poisson equation, solving with DFT, FFTW (2 categories)
- BFS, VGL (2 categories)
- Preflow-Push algorithm (2 categories)
- Bellman-Ford, OpenMP, Stinger (2 categories)
- QR algorithm for complex Hermitian matrices as implemented in SCALAPACK (2 categories)
- Biconjugate gradient stabilized method (BiCGStab) (2 categories)
- SDDP, scalability (2 categories)
- Block Thomas algorithm (2 categories)
- Single-qubit transform of a state vector, locality (2 categories)
- Cholesky decomposition (2 categories)
- Symmetric QR algorithm as implemented in SCALAPACK (2 categories)
- Complete cyclic reduction (2 categories)
- Tarjan's strongly connected components, Java, WebGraph (2 categories)
- DCSC algorithm for finding the strongly connected components (2 categories)
- Dense matrix multiplication (2 categories)
- Transitive closure of a directed graph (2 categories)
- Dijkstra, C++, MPI: Parallel Boost Graph Library, 2 (2 categories)
- Ullman's, C++, VF Library (2 categories)
- Disjoint set union, Boost Graph Library (2 categories)
- VF2, C++, VF Library (2 categories)
- Fast Fourier transform for powers-of-two (2 categories)
- Ford–Fulkerson, C++, Boost Graph Library (2 categories)
- Gaussian elimination, compact scheme for tridiagonal matrices, serial version (2 categories)
- Givens (rotations) method for the QR decomposition of a matrix (2 categories)
- Hessenberg QR algorithm as implemented in SCALAPACK (2 categories)
- Householder (reflections) method for reducing a symmetric matrix to tridiagonal form, SCALAPACK (2 categories)
- Householder (reflections) reduction of a matrix to bidiagonal form (2 categories)
- K-means clustering (2 categories)
- K-means clustering, MLPACK (2 categories)
- K-means clustering, SciPy (2 categories)
- K-means clustering, scalability4 (2 categories)
- Kruskal's, Java, JGraphT (2 categories)
- Lanczos, C++, MPI (2 categories)
- Linpack benchmark (2 categories)
- Meet-in-the-middle attack, implementation3 (2 categories)
- Newton's method for systems of nonlinear equations, Sundials (2 categories)
- Numerical quadrature (cubature) rules on an interval (for a multidimensional cube) (2 categories)
- Pairwise summation of numbers (2 categories)
- BFS, Java, WebGraph (2 categories)
- Poisson equation, solving with DFT, MKL FFT (2 categories)
- Backward substitution (2 categories)
- Backward substitution, locality (2 categories)
- Prim's, C++, Boost Graph Library (2 categories)
- Bellman-Ford, Python, NetworkX (2 categories)
- QR decomposition methods for dense Hessenberg matrices (2 categories)
- Binary search, .NET Framework 2.0 (2 categories)
- Search for isomorphic subgraphs (2 categories)
- Boruvka's, C++, MPI, Parallel Boost Graph Library (2 categories)
- Single-qubit transform of a state vector, scalability (2 categories)
- Cholesky decomposition, SCALAPACK (2 categories)
- Tarjan's algorithm for finding the bridges of a graph (2 categories)
- Complete cyclic reduction, locality (2 categories)
- Tarjan's strongly connected components, Python, NetworkX (2 categories)
- DCSC for finding the strongly connected components, C++, MPI, Parallel Boost Graph Library (2 categories)
- The serial-parallel summation method (2 categories)
- Dense matrix multiplication, locality (2 categories)
- Triangular decomposition of a Gram matrix (2 categories)
- Dijkstra, Google (2 categories)
- Ullman's algorithm (2 categories)
- Disjoint set union, Java, JGraphT (2 categories)
- VF2, Python, NetworkX (2 categories)
- Finding maximal flow in a transportation network (2 categories)
- Ford–Fulkerson, Java, JGraphT (2 categories)
- Gaussian elimination, compact scheme for tridiagonal matrices and its modifications (2 categories)
- Givens method (2 categories)
- High Performance Conjugate Gradient (HPCG) benchmark (2 categories)
- Householder (reflections) method for reducing a symmetric matrix to tridiagonal form, locality (2 categories)
- Householder (reflections) reduction of a matrix to bidiagonal form, SCALAPACK (2 categories)
- K-means clustering, Accord.NET (2 categories)
- K-means clustering, Mathematica (2 categories)
- K-means clustering, Spark (2 categories)
- K-means clustering, scikit-learn (2 categories)
- Kruskal's, Python, NetworkX (2 categories)
- Lanczos, C++, MPI, 2 (2 categories)
- Longest shortest path (2 categories)
- Meet-in-the-middle attack, scalability (2 categories)
- Newton's method for systems of nonlinear equations, parallel1 (2 categories)
- Numerical quadrature (cubature) rules on an interval (for a multidimensional cube), scalability (2 categories)
- Pairwise summation of numbers, locality (2 categories)
- BFS, Ligra (2 categories)
- Poisson equation, solving with DFT, P3DFFT (2 categories)
