斯坦福大学教授的CS261类：优化和算法范例的讲义。 它们涵盖了近似算法，精确优化和在线算法的主题。

"Approximate Dynamic Programming: Solving the Curses of Dimensionality" 介绍了动态规划的算法、理论和应用。是学习动态规划的极佳教材

Approximate.Dynamic.Programming 和Reinforcement learning an introduction 一起学习

非线性动力学近似熵算法，对于处理一维脑电信号等生理信号非常适用

由国外著名大学编写的非常有效近似最近邻分类算法，可直接使用，也可作为学习

For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data? ” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm that applies priority search on hierarchical k-means trees, which we have found to provide the best known performance on many datasets. After testing a range of alternatives, we have found that multiple randomized k-d trees provide the best performance for other datasets. We are releasing public domain code that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection.

Powell,.Approximate.Dynamic.Programming.Solving.the.Curses.of.Dimensionality,.2ed,.Wiley,.2011