Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for finding a local minimum of a differentiable...

stochastic (or "on-line") gradient descent, the true gradient of Q ( w ) {\displaystyle Q(w)} is approximated by a gradient at a single sample: w := w...

In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose...

out-of-core versions of machine learning algorithms, for example, stochastic gradient descent. When combined with backpropagation, this is currently the de facto...

introduced the view of boosting algorithms as iterative functional gradient descent algorithms. That is, algorithms that optimize a cost function over...

stochastic gradient descent, where gradients are computed on a random subset of the total dataset and then used to make one step of the gradient descent. Federated...

can be derived through dynamic programming. Gradient descent, or variants such as stochastic gradient descent, are commonly used. Strictly the term backpropagation...

grids. If used in gradient descent methods, random preconditioning can be viewed as an implementation of stochastic gradient descent and can lead to faster...

theory, where it is used to minimize a function by gradient descent. In coordinate-free terms, the gradient of a function f ( r ) {\displaystyle f(\mathbf...

Armijo–Goldstein condition. Backtracking line search is typically used for gradient descent (GD), but it can also be used in other contexts. For example, it can...