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  2. L4 microkernel family - Wikipedia

    en.wikipedia.org/wiki/L4_microkernel_family

    Specifying the general idea of a microkernel, Liedtke states: . A concept is tolerated inside the microkernel only if moving it outside the kernel, i.e., permitting competing implementations, would prevent the implementation of the system's required functionality.

  3. List of numerical libraries - Wikipedia

    en.wikipedia.org/wiki/List_of_numerical_libraries

    Lis is a scalable parallel library for solving systems of linear equations and eigenvalue problems using iterative methods. Intel MKL (Math Kernel Library) contains optimized math routines for science, engineering, and financial applications, and is written in C/C++ and Fortran. Core math functions include BLAS, LAPACK, ScaLAPACK, sparse ...

  4. Template:L4 - Wikipedia

    en.wikipedia.org/wiki/Template:L4

    Place {} where normally would be written L 4. Optionally takes an argument nolink=yes to suppress the hyperlink, for use in headings and to avoid overlinking. Optionally takes an argument pt=yes to append the word "point" or "points". Optionally takes up to four unnamed parameters to allow the listing of a set of Lagrangian points

  5. Runge–Kutta–Fehlberg method - Wikipedia

    en.wikipedia.org/wiki/Runge–Kutta–Fehlberg...

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file

  6. Godunov's scheme - Wikipedia

    en.wikipedia.org/wiki/Godunov's_scheme

    In numerical analysis and computational fluid dynamics, Godunov's scheme is a conservative numerical scheme, suggested by Sergei Godunov in 1959, [1] for solving partial differential equations.

  7. Successive over-relaxation - Wikipedia

    en.wikipedia.org/wiki/Successive_over-relaxation

    Spectral radius () of the iteration matrix for the SOR method .The plot shows the dependence on the spectral radius of the Jacobi iteration matrix := ().. The choice of relaxation factor ω is not necessarily easy, and depends upon the properties of the coefficient matrix.

  8. Iteratively reweighted least squares - Wikipedia

    en.wikipedia.org/wiki/Iteratively_reweighted...

    IRLS can be used for ℓ 1 minimization and smoothed ℓ p minimization, p < 1, in compressed sensing problems. It has been proved that the algorithm has a linear rate of convergence for ℓ 1 norm and superlinear for ℓ t with t < 1, under the restricted isometry property, which is generally a sufficient condition for sparse solutions.

  9. Plotting algorithms for the Mandelbrot set - Wikipedia

    en.wikipedia.org/wiki/Plotting_algorithms_for...

    The result of each iteration is used as the starting values for the next. The values are checked during each iteration to see whether they have reached a critical "escape" condition, or "bailout". If that condition is reached, the calculation is stopped, the pixel is drawn, and the next x, y point is examined. For some starting values, escape ...