Difference between revisions of "Gradient descent"
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Karl Jones (Talk | contribs) (Created page with "'''Gradient descent''' is a first-order iterative optimization algorithm. == Description == To find a local minimum of a function using gradie...") |
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| − | '''Gradient descent''' is a [[First-order method|first-order]] [[iterative]] optimization [[algorithm]]. | + | '''Gradient descent''' is a [[First-order method|first-order]] [[Iterative method|iterative]] optimization [[algorithm]]. |
== Description == | == Description == | ||
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* [[Delta rule]] | * [[Delta rule]] | ||
* [[First-order method]] | * [[First-order method]] | ||
| + | * [[Iterative method]] | ||
* [[Nelder–Mead method]] | * [[Nelder–Mead method]] | ||
* [[Preconditioning]] | * [[Preconditioning]] | ||
Revision as of 20:36, 12 October 2016
Gradient descent is a 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
- Iterative method
- Nelder–Mead method
- Preconditioning
- Rprop
- Stochastic gradient descent
- Wolfe conditions
External links
- Gradient descent @ Wikipedia
