About the Book

Preface

The objective of the book is to assist the reader to acquire Python programming experience of the convex optimization theory, by first reproducing the examples and the figures in the textbook ([book-convex-2004]^[1]) by Python, and then by tuning the model parameters for further understanding the characteristics of the convex problems and their solutions.

These characteristics include,

• The possible variations and restrictions of the convex problems

• The convergence rate

• The uniqueness of the optimal solution,

• The bounds of the optimal value

• The feasibility and infeasibility of the problems

• The challenges in numerical implementations

The reader will also learn how to implement and verify the algorithm through the convex optimization theory by himself. The jupyter noteboks for all the demos have been in the appendix.

The textbook ([book-convex-2004]) has been popularly used in many open courses about convex optimization, including

* Engineering Everywhere open course, https://see.stanford.edu/Course/EE364A

However, many learners from the convex optimization course have been complaining time is pressing to deep dive into the details of the algorithms. This book will help learners catch the essential cruxes of the convex optimization theory without writing the algorithms from the scratch. Readers are encouraged to use this book as the supplementary material to the course.

The content of the book is organized in a series of demos. Each demo is corresponding to an example in the textbook ([book-convex-2004]). The jupyter notebook implementation for each demo is also provided.

Only the examples in the textbook have been implemented; the implementations for the exercises at the end of each chapter in the textbook haven’t been provided. However, readers are encouraged to reuse the demo codes in the book as the baseline, to implement not only the exercises, but also any new idea.

All the source codes in python jupyter notebook can be accessed through the website:

https://wuzhuchun.club:9091/lab/tree/welcome.ipynb

All the demos have been written in jupyter notebooks. Readers are encouraged to run the jupyter notebooks in the JupyterLab server (provided in the above website). The JupyterLab server has already installed the necessary Python libraries. It also has provided the online debugging environment to facilitate interested readers to deep dive into the implementation details.

Have fun!

Author: Edwin Jiang

Hangzhou, China

1 [book-convex-2004] Boyd, S. & Vandenberghe, L., 2004. Convex optimization, Cambridge university press. Download: https://web.stanford.edu/ boyd/cvxbook/bv_cvxbook.pd

About the Author

Edwin JIANG

Edwin Jiang

Career Ambition

To become a certificated algorithm researcher and engineer, to explore the computer algorithm engineering and communication technology to unleash the potential in connecting human with nature. To achieve this ambition, I am reinventing myself in,

• 1. Optimization theory including but not limited to, linear and convex optimization[1] , meta-heuristic optimization[2][3]
• 2. Machine Learning[4][5][6][7] and Reinforcement Learning[8][9][10]
• 3. To apply the optimization theory and ML/AI to wireless communication technology including signal design, transmitter and receiver techniques, resource scheduling algorithm and network architecture evolution

Programming Skills 

1. Expert in C/C++, MATLAB, Java, Python
2. Intermediate in LATEX