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Stochastic gradient descent competes with the L-BFGS algorithm, [citation needed] which is also widely used. Stochastic gradient descent has been used since at least 1960 for training linear regression models, originally under the name ADALINE. [25] Another stochastic gradient descent algorithm is the least mean squares (LMS) adaptive filter.
Federated stochastic gradient descent [19] is the direct transposition of this algorithm to the federated setting, but by using a random fraction of the nodes and using all the data on this node. The gradients are averaged by the server proportionally to the number of training samples on each node, and used to make a gradient descent step.
It is a stochastic gradient descent method in that the filter is only adapted ... Most linear adaptive filtering problems can be formulated using the block diagram ...
SGLD can be applied to the optimization of non-convex objective functions, shown here to be a sum of Gaussians. Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins–Monro optimization algorithm, and Langevin dynamics, a mathematical extension of molecular dynamics models.
Descent direction; Guess value — the initial guess for a solution with which an algorithm starts; Line search. Backtracking line search; Wolfe conditions; Gradient method — method that uses the gradient as the search direction Gradient descent. Stochastic gradient descent; Landweber iteration — mainly used for ill-posed problems
Stochastic gradient descent; Backpropagation; Rescorla–Wagner model – the origin of delta rule; References This page was last edited on 27 October 2023, at 04:45 ...
Gradient descent with momentum remembers the solution update at each iteration, and determines the next update as a linear combination of the gradient and the previous update. For unconstrained quadratic minimization, a theoretical convergence rate bound of the heavy ball method is asymptotically the same as that for the optimal conjugate ...
In 1967, Shun'ichi Amari reported [22] the first multilayered neural network trained by stochastic gradient descent, which was able to classify non-linearily separable pattern classes. Amari's student Saito conducted the computer experiments, using a five-layered feedforward network with two learning layers.