Reading Lists
These are some quite useful and interesting books and papers that I think of great importance for doing research in the field of optimization and computational intelligence. Some of them have been read, while some others are not. Never mind, research is life-long journey, keep on reading, learning and thinking!
(This page is still under construction, I will try to complete in an incremental manner.)
Books
Writing
Websites
ECE236C-Optimization Methods for Large-Scale Systems (Spring 2019). Prof. L. Vandenberghe, UCLA
Matlab's tours. Codes: matlab-toolboxes
Convex Optimization: Fall 2019. Instructor: Ryan Tibshirani
AIMS South Africa Short Course on Optimization. Instructor: Stephen Becker
Convex Optimization: Fall 2019. Instructor: Zaiwen Wen
Article
S.Ruder. An overview of gradient descent optimization algorithms. arXiv preprint, arXiv:1609.04747. (2016).
Note: A version of Chinese translationPeter Ochs et al. iPiano: Inertial Proximal Algorithm for Nonconvex Optimization.
Note: Homepage seminar-ipiano Slides CodesDonghwan Kim, Jeffrey A. Fessler. Another look at the fast iterative shrinkage/thresholding algorithm (FISTA). arXiv preprint, arXiv:1608.03861. (2016).
Variational Inequality
Federica Tinti. A MatLab collection of variational inequality problems.
Superiorization: theory and applications
Inverse Problems. Special issue on superiorization: theory and applications.
Manifold Optimization
Jiang Hu, Xin Liu, Zaiwen Wen, Yaxiang Yuan. A Brief Introduction to Manifold Optimization. arXiv preprint, arXiv:1906.05450. (2019).