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

min x ∈ ∈ R n f ( x ) {\displaystyle \min _{x\in \mathbb {R} ^{n}}\;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

• References

• 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

• citerefelijah-polak1997Elijah Polak (1997). Optimization : Algorithms and Consistent Approximations. Springer-Verlag. ISBN 0-387-94971-2.