Gradient method

In optimization, a gradient method is an algorithm to solve problems of the form

$\underset{x\in {ℝ}^n}{min}f(x)$

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.