Foundations and Trends® in Machine Learning(共63册), 这套丛书还有 《Data Analytics on Graphs》《Tensor Networks for Dimensionality Reduction and Large-scale Optimization》《Divided Differences, Falling Factorials, and Discrete Splines》《Metric Learning: A Survey》《Machine Learning for Automated Theore...
Foundations and Trends in Machine Learning|January 2015, Vol 8(4): pp. 231-357 Publication This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recen...
DeepLearning.ai笔记:(2-2)-- 优化算法(Optimization algorithms) 首发于个人博客:fangzh.top,欢迎来访 这周学习了优化算法,可以让神经网络运行的更快。 主要有: mini-batch 动量梯度下降(momentum) RMSprop Adam优化算法 学习率衰减 mini-batch(小批量) 原本的梯度下降算法,在每一次的迭代中,要把所有的数据都进...
Complexity and algorithms for convex network optimization and other nonlinear problems Nonlinear optimization algorithms are rarely discussed from a complexity point of view. Even the concept of solving nonlinear problems on digital computers... DS Hochbaum - 《4or Quarterly Journal of the Belgian ...
S. (2005). Complexity and algorithms for convex network optimization and other nonlinear problems. 4OR , 3 , 171–216.D. S. Hochbaum. Complexity and algorithms for convex network optimization and other nonlinear problems. 4OR, 3(3):171-216, 2005....
Bubeck, S., 2015. Convex optimization: Algorithms and complexity.Foundations and Trends® in ...
2017Convex optimization_ Algorithms and complexity阅读笔记 1 介绍一些概念 本专题的总体目标是介绍凸优化中的主要复杂性定理和相应的算法。我们将重点放在凸优化的五个主要结果上,这些结果给出了本文的整体结构:存在具有最优预言复杂度的有效切面方法(第2章),对一阶预言复杂度和曲率之间关系的完整表征。目标函数(第...
Convex Optimization: Algorithms and Complexity 电子书 读后感 评分☆☆☆ 评分☆☆☆ 评分☆☆☆ 评分☆☆☆ 评分☆☆☆ 类似图书 点击查看全场最低价 出版者:Now Publishers Inc 作者:Sébastien Bubeck 出品人: 页数:142 译者: 出版时间:2015 价格:0 ...
6]. We believe that the paradigm of stepsize hedging will be useful for the design and analysis of optimization algorithms in many contexts, and that the present setting of gradient descent is just scratching the surface of this algorithmic opportunity....
Nonconvex and nonsmooth optimization problems are frequently encountered in much of statistics, business, science and engineering, but they are not yet widely recognized as a technology in the sense of scalability. A reason for this relatively low degree of popularity is the lack of a well develop...