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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. Nelder–Mead method - Wikipedia

    en.wikipedia.org/wiki/Nelder–Mead_method

    Nelder-Mead optimization in Python in the SciPy library. nelder-mead - A Python implementation of the Nelder–Mead method; NelderMead() - A Go/Golang implementation; SOVA 1.0 (freeware) - Simplex Optimization for Various Applications - HillStormer, a practical tool for nonlinear, multivariate and linear constrained Simplex Optimization by ...

  4. Moving horizon estimation - Wikipedia

    en.wikipedia.org/wiki/Moving_Horizon_Estimation

    Moving horizon estimation (MHE) is an optimization approach that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables or parameters.

  5. Powell's method - Wikipedia

    en.wikipedia.org/wiki/Powell's_method

    Powell's method, strictly Powell's conjugate direction method, is an algorithm proposed by Michael J. D. Powell for finding a local minimum of a function. The function need not be differentiable, and no derivatives are taken.

  6. 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:

  7. Quasi-Newton method - Wikipedia

    en.wikipedia.org/wiki/Quasi-Newton_method

    In numerical analysis, a quasi-Newton method is an iterative numerical method used either to find zeroes or to find local maxima and minima of functions via an iterative recurrence formula much like the one for Newton's method, except using approximations of the derivatives of the functions in place of exact derivatives.

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    The search engine that helps you find exactly what you're looking for. Find the most relevant information, video, images, and answers from all across the Web.

  9. 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.