алгоритм наискорейшего спуска
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[Л.Г.Суменко. Англо-русский словарь по информационным технологиям. М.: ГП ЦНИИС, 2003.]
Англо-русский словарь нормативно-технической терминологии. academic.ru. 2015.
Method of steepest descent — For the optimization algorithm, see Gradient descent. In mathematics, the method of steepest descent or stationary phase method or saddle point method is an extension of Laplace s method for approximating an integral, where one deforms a contour… … Wikipedia
Gradient descent — For the analytical method called steepest descent see Method of steepest descent. Gradient descent is an optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the… … Wikipedia
Gauss–Newton algorithm — The Gauss–Newton algorithm is a method used to solve non linear least squares problems. It can be seen as a modification of Newton s method for finding a minimum of a function. Unlike Newton s method, the Gauss–Newton algorithm can only be used… … Wikipedia
Levenberg–Marquardt algorithm — In mathematics and computing, the Levenberg–Marquardt algorithm (LMA)[1] provides a numerical solution to the problem of minimizing a function, generally nonlinear, over a space of parameters of the function. These minimization problems arise… … Wikipedia
алгоритм наискорейшего спуска — — [Л.Г.Суменко. Англо русский словарь по информационным технологиям. М.: ГП ЦНИИС, 2003.] Тематики информационные технологии в целом EN steepest descent algorithm … Справочник технического переводчика
Non-linear least squares — is the form of least squares analysis which is used to fit a set of m observations with a model that is non linear in n unknown parameters (m > n). It is used in some forms of non linear regression. The basis of the method is to… … Wikipedia
Conjugate gradient method — A comparison of the convergence of gradient descent with optimal step size (in green) and conjugate vector (in red) for minimizing a quadratic function associated with a given linear system. Conjugate gradient, assuming exact arithmetic,… … Wikipedia
Nonlinear conjugate gradient method — In numerical optimization, the nonlinear conjugate gradient method generalizes the conjugate gradient method to nonlinear optimization. For a quadratic function : The minimum of f is obtained when the gradient is 0: . Whereas linear conjugate… … Wikipedia
Least mean squares filter — Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the… … Wikipedia
Mathematical optimization — For other uses, see Optimization (disambiguation). The maximum of a paraboloid (red dot) In mathematics, computational science, or management science, mathematical optimization (alternatively, optimization or mathematical programming) refers to… … Wikipedia
Optimization (mathematics) — In mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables from within an… … Wikipedia
