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  2. Limited-memory BFGS - Wikipedia

    en.wikipedia.org/wiki/Limited-memory_BFGS

    SciPy's optimization module's minimize method also includes an option to use L-BFGS-B. Notable non open source implementations include: The L-BFGS-B variant also exists as ACM TOMS algorithm 778. [8] [12] In February 2011, some of the authors of the original L-BFGS-B code posted a major update (version 3.0).

  3. Coordinate descent - Wikipedia

    en.wikipedia.org/wiki/Coordinate_descent

    Coordinate descent is an optimization algorithm that successively minimizes along coordinate directions to find the minimum of a function.At each iteration, the algorithm determines a coordinate or coordinate block via a coordinate selection rule, then exactly or inexactly minimizes over the corresponding coordinate hyperplane while fixing all other coordinates or coordinate blocks.

  4. Newton's method in optimization - Wikipedia

    en.wikipedia.org/wiki/Newton's_method_in...

    The geometric interpretation of Newton's method is that at each iteration, it amounts to the fitting of a parabola to the graph of () at the trial value , having the same slope and curvature as the graph at that point, and then proceeding to the maximum or minimum of that parabola (in higher dimensions, this may also be a saddle point), see below.

  5. Assignment problem - Wikipedia

    en.wikipedia.org/wiki/Assignment_problem

    Worked example of assigning tasks to an unequal number of workers using the Hungarian method. The assignment problem is a fundamental combinatorial optimization problem. In its most general form, the problem is as follows:

  6. MM algorithm - Wikipedia

    en.wikipedia.org/wiki/Mm_algorithm

    The MM algorithm is an iterative optimization method which exploits the convexity of a function in order to find its maxima or minima. The MM stands for “Majorize-Minimization” or “Minorize-Maximization”, depending on whether the desired optimization is a minimization or a maximization.

  7. Levenberg–Marquardt algorithm - Wikipedia

    en.wikipedia.org/wiki/Levenberg–Marquardt...

    The primary application of the Levenberg–Marquardt algorithm is in the least-squares curve fitting problem: given a set of empirical pairs (,) of independent and dependent variables, find the parameters ⁠ ⁠ of the model curve (,) so that the sum of the squares of the deviations () is minimized:

  8. Gaussian filter - Wikipedia

    en.wikipedia.org/wiki/Gaussian_filter

    Edge Detection: Gaussian filters are often used as a preprocessing step in edge detection algorithms. By smoothing the image, they help to minimize the impact of noise before applying methods like the Sobel or Canny edge detectors. Image Resizing: In image resizing tasks, Gaussian filters can prevent aliasing artifacts. Smoothing the image ...

  9. Nelder–Mead method - Wikipedia

    en.wikipedia.org/wiki/Nelder–Mead_method

    The method uses the concept of a simplex, which is a special polytope of n + 1 vertices in n dimensions. Examples of simplices include a line segment in one-dimensional space, a triangle in two-dimensional space, a tetrahedron in three-dimensional space, and so forth.