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  2. Rosenbrock function - Wikipedia

    en.wikipedia.org/wiki/Rosenbrock_function

    Plot of the Rosenbrock function of two variables. Here =, =, and the minimum value of zero is at (,). In mathematical optimization, the Rosenbrock function is a non-convex function, introduced by Howard H. Rosenbrock in 1960, which is used as a performance test problem for optimization algorithms. [1]

  3. 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.

  4. 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 .

  5. Gradient descent - Wikipedia

    en.wikipedia.org/wiki/Gradient_descent

    The gradient descent can take many iterations to compute a local minimum with a required accuracy, if the curvature in different directions is very different for the given function. For such functions, preconditioning, which changes the geometry of the space to shape the function level sets like concentric circles, cures the slow convergence ...

  6. Maximum and minimum - Wikipedia

    en.wikipedia.org/wiki/Maximum_and_minimum

    In mathematical analysis, the maximum and minimum [a] of a function are, respectively, the greatest and least value taken by the function. Known generically as extremum , [ b ] they may be defined either within a given range (the local or relative extrema) or on the entire domain (the global or absolute extrema) of a function.

  7. Here's why the Kroger merger with Albertsons was killed - AOL

    www.aol.com/heres-why-kroger-merger-albertsons...

    From the start, Cincinnati-based Kroger and Boise, Idaho-based Albertsons said the merger was necessary to stay efficient and competitive in a grocery industry increasingly dominated by ...

  8. Least absolute deviations - Wikipedia

    en.wikipedia.org/wiki/Least_absolute_deviations

    Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based on minimizing the sum of absolute deviations (also sum of absolute residuals or sum of absolute errors) or the L 1 norm of such values.

  9. Why a 12-team College Football Playoff is just right

    www.aol.com/sports/why-12-teams-perfect-number...

    Oh, and as for you Miami, Ole Miss and South Carolina fans — we understand why y’all would want a retroactive expansion. But next year will be your year, guaranteed. Show comments.