View source for Gradient descent

'''Gradient descent''' is a [[First-order method|first-order]] [[iterative]] optimization [[algorithm]].

== Description ==

To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point. If instead one takes steps proportional to the positive of the gradient, one approaches a local maximum of that function; the procedure is then known as gradient ascent.

Gradient descent is also known as steepest descent, or the method of steepest descent. Gradient descent should not be confused with the method of steepest descent for approximating integrals.

== See also ==

* [[BFGS method]]
* [[Conjugate gradient method]]
* [[Delta rule]]
* [[First-order method]]
* [[Nelder–Mead method]]
* [[Preconditioning]]
* [[Rprop]]
* [[Stochastic gradient descent]]
* [[Wolfe conditions]]

== External links ==

* [https://en.wikipedia.org/wiki/Gradient_descent Gradient descent] @ Wikipedia


[[Category:Mathematics]]