Gradient method explained

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

min _{x\in 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

• 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

• Book: 1997 . Optimization : Algorithms and Consistent Approximations . Springer-Verlag . 0-387-94971-2 . Elijah Polak.