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Primal Averaging: A New Gradient Evaluation Step to Attain the Optimal Individual Convergence 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: 50, 期号: 2, 页码: 835-845
Authors:  Tao, Wei;  Pan, Zhisong;  Wu, Gaowei;  Tao, Qing
Favorite  |  View/Download:11/0  |  Submit date:2020/03/30
Convergence  Convex functions  Machine learning  Optimization methods  Linear programming  Cybernetics  Individual convergence  machine learning  mirror descent (MD) methods  regularized learning problems  stochastic gradient descent (SGD)  stochastic optimization