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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.
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
Download QR code; Print/export Download as PDF; Printable version; ... Stochastic gradient descent; Stochastic gradient Langevin dynamics; Stochastic variance reduction
Download QR code; Print/export Download as PDF; Printable version; ... [29] [30] In the direction of updating, stochastic gradient descent adds a stochastic property ...
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.
Download QR code; Print/export ... It is a stochastic gradient descent method in that the filter is only adapted based on ... This is based on the gradient descent ...
In the stochastic setting, under the same assumption that the gradient is Lipschitz continuous and one uses a more restrictive version (requiring in addition that the sum of learning rates is infinite and the sum of squares of learning rates is finite) of diminishing learning rate scheme (see section "Stochastic gradient descent") and moreover ...
The algorithm starts with an initial estimate of the optimal value, , and proceeds iteratively to refine that estimate with a sequence of better estimates ,, ….The derivatives of the function := are used as a key driver of the algorithm to identify the direction of steepest descent, and also to form an estimate of the Hessian matrix (second derivative) of ().