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  2. Levenberg–Marquardt algorithm - Wikipedia

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

    In this example we try to fit the function = ⁡ + ⁡ using the Levenberg–Marquardt algorithm implemented in GNU Octave as the leasqr function. The graphs show progressively better fitting for the parameters a = 100 {\displaystyle a=100} , b = 102 {\displaystyle b=102} used in the initial curve.

  3. Cost function - Wikipedia

    en.wikipedia.org/wiki/Cost_function

    Cost function In economics, the cost curve , expressing production costs in terms of the amount produced. In mathematical optimization, the loss function , a function to be minimized.

  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. Quadratic assignment problem - Wikipedia

    en.wikipedia.org/wiki/Quadratic_assignment_problem

    For each pair of locations, a distance is specified and for each pair of facilities a weight or flow is specified (e.g., the amount of supplies transported between the two facilities). The problem is to assign all facilities to different locations with the goal of minimizing the sum of the distances multiplied by the corresponding flows.

  6. Transportation theory (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Transportation_theory...

    The following simple example illustrates the importance of the cost function in determining the optimal transport plan. Suppose that we have books of equal width on a shelf (the real line), arranged in a single contiguous block. We wish to rearrange them into another contiguous block, but shifted one book-width to the right.

  7. Low-rank approximation - Wikipedia

    en.wikipedia.org/wiki/Low-rank_approximation

    In mathematics, low-rank approximation refers to the process of approximating a given matrix by a matrix of lower rank. More precisely, it is a minimization problem, in which the cost function measures the fit between a given matrix (the data) and an approximating matrix (the optimization variable), subject to a constraint that the approximating matrix has reduced rank.

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  9. Newton's method - Wikipedia

    en.wikipedia.org/wiki/Newton's_method

    The following is an example of a possible implementation of Newton's method in the Python (version 3.x) programming language for finding a root of a function f which has derivative f_prime. The initial guess will be x 0 = 1 and the function will be f ( x ) = x 2 − 2 so that f ′ ( x ) = 2 x .