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Showing below up to 50 results in range #71 to #120.
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- Ford–Fulkerson, Java, JGraphT
- Ford–Fulkerson, Python, NetworkX
- Gaussian elimination, compact scheme for tridiagonal matrices, serial variant
- Givens method, locality
- HITS, VGL
- HPCG, locality
- HPCG, scalability
- Hopcroft–Karp, Java, JGraphT
- Horners, locality
- 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 the QR decomposition, SCALAPACK
- Householder (reflections) method for the QR decomposition, locality
- Householder (reflections) reduction of a matrix to bidiagonal form, SCALAPACK
- Householder (reflections) reduction of a matrix to bidiagonal form, locality
- Hungarian, Java, JGraphT
- Johnson's, C++, Boost Graph Library
- 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
- Kruskal's, C++, Boost Graph Library
- Kruskal's, C++, MPI, Parallel Boost Graph Library
- Kruskal's, Java, JGraphT
- Kruskal's, Python, NetworkX
- LU decomposition via Gaussian elimination, locality