- gradient Hessian
- Макаров: градиентный гессиан
Универсальный англо-русский словарь. Академик.ру. 2011.
gradient Hessian
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Hessian matrix — In mathematics, the Hessian matrix (or simply the Hessian) is the square matrix of second order partial derivatives of a function; that is, it describes the local curvature of a function of many variables. The Hessian matrix was developed in the… … 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
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
Electric field gradient — Mathematically, the electric field gradient (EFG) is the hessian matrix (the matrix of the second derivatives) of the electrical potential V: :V {ij} = frac{partial^2 V}{partial x i partial x j}It is an important structural property of a… … 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
Newton's method in optimization — A comparison of gradient descent (green) and Newton s method (red) for minimizing a function (with small step sizes). Newton s method uses curvature information to take a more direct route. In mathematics, Newton s method is an iterative method… … Wikipedia
Quasi-Newton method — In optimization, quasi Newton methods (also known as variable metric methods) are well known algorithms for finding local maxima and minima of functions. Quasi Newton methods are based on Newton s method to find the stationary point of a function … 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
Scale-invariant feature transform — Feature detection Output of a typical corner detection algorithm … Wikipedia
BFGS method — In mathematics, the Broyden Fletcher Goldfarb Shanno (BFGS) method is a method to solve an unconstrained nonlinear optimization problem. The BFGS method is derived from the Newton s method in optimization, a class of hill climbing optimization… … Wikipedia
CMA-ES — stands for Covariance Matrix Adaptation Evolution Strategy. Evolution strategies (ES) are stochastic, derivative free methods for numerical optimization of non linear or non convex continuous optimization problems. They belong to the class of… … Wikipedia
