When.com Web Search

  1. Ad

    related to: how to find maximum and minimum of quadratic

Search results

  1. Results From The WOW.Com Content Network
  2. 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.

  3. Quadratic function - Wikipedia

    en.wikipedia.org/wiki/Quadratic_function

    A bivariate quadratic function is a second-degree polynomial of the ... the function achieves the maximum/minimum at a line—a minimum if A>0 and a maximum if A ...

  4. Newton's method - Wikipedia

    en.wikipedia.org/wiki/Newton's_method

    Newton's method can be used to find a minimum or maximum of a function f(x). The derivative is zero at a minimum or maximum, so local minima and maxima can be found by applying Newton's method to the derivative. [39] The iteration becomes: + = ′ ″ ().

  5. Quadratic equation - Wikipedia

    en.wikipedia.org/wiki/Quadratic_equation

    The extreme point of the parabola, whether minimum or maximum, corresponds to its vertex. The x -coordinate of the vertex will be located at x = − b 2 a {\displaystyle \scriptstyle x={\tfrac {-b}{2a}}} , and the y -coordinate of the vertex may be found by substituting this x -value into the function.

  6. Maximum and minimum - Wikipedia

    en.wikipedia.org/wiki/Maximum_and_minimum

    Furthermore, a global maximum (or minimum) either must be a local maximum (or minimum) in the interior of the domain, or must lie on the boundary of the domain. So a method of finding a global maximum (or minimum) is to look at all the local maxima (or minima) in the interior, and also look at the maxima (or minima) of the points on the ...

  7. Quadratic programming - Wikipedia

    en.wikipedia.org/wiki/Quadratic_programming

    The quadratic programming problem with n variables and m constraints can be formulated as follows. [2] Given: a real-valued, n-dimensional vector c, an n×n-dimensional real symmetric matrix Q, an m×n-dimensional real matrix A, and; an m-dimensional real vector b, the objective of quadratic programming is to find an n-dimensional vector x ...

  8. Golden-section search - Wikipedia

    en.wikipedia.org/wiki/Golden-section_search

    The golden-section search is a technique for finding an extremum (minimum or maximum) of a function inside a specified interval. For a strictly unimodal function with an extremum inside the interval, it will find that extremum, while for an interval containing multiple extrema (possibly including the interval boundaries), it will converge to one of them.

  9. Completing the square - Wikipedia

    en.wikipedia.org/wiki/Completing_the_square

    That is, h is the x-coordinate of the axis of symmetry (i.e. the axis of symmetry has equation x = h), and k is the minimum value (or maximum value, if a < 0) of the quadratic function. One way to see this is to note that the graph of the function f(x) = x 2 is a parabola whose vertex is at the origin (0, 0).