Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject. Aharon Ben-Tal is professor of operations research at the Technion, Israel Institute for Technology. Laurent El Ghaoui is associate professor of electrical engineering and operations research at the University of California, Berkeley. Arkadi Nemirovski is professor of industrial and systems engineering at Georgia Institute of Technology.

Complete and robust no-fit polygon generation for the irregular stock cutting problem E.K. Burke, R.S.R. Hellier, G. Kendall, G. Whitwell * University of Nottingham, School of Computer Science & IT, Jubilee Campus, Nottingham NG8 1BB, UK 2006

COLMAP 三维重建应用作者的博士论文，对理解 COLMAP 的实现过程有非常大的帮助，推荐阅读。

mainly on the robust control tech.

文件统计学比较权威的matlab应用工具包，里面包含大量（50多个）可调用函数，如：robpca，MCD,fastMCD,classSVD，kernelEVD等，是从事统计学，稳健统计学，粗差判别领域不可或缺的一个matlab工具包。

A COURSE IN ROBUST CONTROL （经典控制书）

经典的回归分析与离群探测英文原版书籍，目前这本书籍还很难找到电子版，而且很清楚哦

Robust PCA的代码。 也有weighted PCA的代码。 附带例子。 matlab。

自适应控制和鲁棒控制同是处理存在于系统的不确定性现象，此因把它们结合起来，取长补短。 这是鲁棒自适应控制的基本特点。鲁棒自适应控制(Robust Adaptive Control)是指对那些存在不 定性的系统进行控制。鲁棒自适应控制有着广泛的应用如机器人，飞行器控制等领域。

鲁棒非线性回归：使用维纳模型和稀疏性优化的鲁棒非线性回归