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RES, a regularized stochastic version of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton method, is proposed to solve strongly convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional problems. Application of second-order...
A regularized stochastic version of the Broyden-Fletcher- Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve optimization problems with stochastic objectives that arise in large scale machine learning. Stochastic gradient descent is the currently preferred solution methodology but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional...
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