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Mar, 2017
自适应重要性采样加速坐标下降
Faster Coordinate Descent via Adaptive Importance Sampling
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Dmytro Perekrestenko, Volkan Cevher, Martin Jaggi
TL;DR
本文提出了一种新的自适应规则来随机选择决策变量的部分更新,以解决大规模的凸优化问题,并通过数学理论推导和数值实验加以验证。
Abstract
coordinate descent methods
employ random partial updates of decision variables in order to solve huge-scale
convex optimization
problems. In this work, we introduce new
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