Gradient method
From HandWiki
In optimization, a gradient method is an algorithm to solve problems of the form
- [math]\displaystyle{ \min_{x\in\mathbb R^n}\; f(x) }[/math]
with the search directions defined by the gradient of the function at the current point. Examples of gradient methods are the gradient descent and the conjugate gradient.
See also
- Gradient descent
- Stochastic gradient descent
- Coordinate descent
- Frank–Wolfe algorithm
- Landweber iteration
- Random coordinate descent
- Conjugate gradient method
- Derivation of the conjugate gradient method
- Nonlinear conjugate gradient method
- Biconjugate gradient method
- Biconjugate gradient stabilized method
References
- Elijah Polak (1997). Optimization : Algorithms and Consistent Approximations. Springer-Verlag. ISBN 0-387-94971-2.
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