Related Materials

Documents & Notes

• 优化算法复杂度分析
  这篇文档给出了优化算法的复杂度分析，Oct 2022。
• 最优化理论基础
  这篇文档给出了最优化理论的数学基础，包含范数、梯度、海瑟矩阵、Lipschitz连续、闭函数、凸集、仿射集、超平面、凸函数的定义与保凸运算、共轭函数等内容，Jul 2021。
• 最优化简介
  这篇文档给出了最优化理论的基本知识，包含优化问题的分类、解的形式、收敛速度等，Jun 2021。
• 深度学习理论速查清单
  这篇文档给出了深度学习模型的理论原理，Oct 2020。
• 理解共轭梯度法
  这篇文档给出了共轭梯度法的设计原理、算法实现以及收敛性分析，Jun 2020。
• 需要熟练掌握的算法理论基础 (ver. 21.06.22)
  这篇文档从“为什么要这样”的角度罗列并分析了概率论、数理统计、微积分、优化理论以及线性代数中一些广泛应用的话题，May 2020。
• 里亚普洛夫优化导论
  这篇文档给出了里亚普洛夫优化技术的使用方法及性能分析，Nov 2018。
• 线性代数中的一些基本观念
  这篇文档对线性代数中的一些基本观念给出了有趣的阐释，Oct 2018。

Discussions & Slides

• Everything about ADMM
  If you want to learn the past and present of the alternating direction method of multipliers (ADMM), you should not miss this slide. Nov 2022.
• Preliminaries for Optimization Algorithm Design and Analysis
  This slide demonstrates the mathematical preliminaries of optimization algorithms that should be kept in mind firmly, Oct 2022.
• An Overview of Kubernetes Scheduling
  This slide is a report on the Kubernetes scheduling background and research actuality, Oct 2022.
• Dependent Function Embedding for Distributed Serverless Edge Computing
  This slide is a report on my preprint Placement is not Enough: Embedding with Proactive Stream Mapping on the Heterogenous Edge. This talk was held in the PhD Symposium, CCF ICSS '21, May 2021.
• DPoS: Decentralized, Privacy-Preserving, and Low-Complexity Online Slicing for Multi-Tenant Networks
  This slide is a report on my paper DPoS: Decentralized, Privacy-Preserving, and Low-Complexity Online Slicing for Multi-Tenant Networks, published in IEEE TMC. This talk was held in Apr 2021.
• Park: An Open Platform for Learning-Augmented Computer Systems
  This slide is a report on Hongzi Mao’s paper Park: An Open Platform for Learning Augmented Computer Systems, published in NeurIPS '19. This paper won the ICML Workshop Best Paper Award. This talk was held in Jan 2021.
• Review the Transport Layer: TCP and UDP
  This slide is a reminder for chapter 3 of the book Computer Networking: A Top-Down Approach, Sep 2020.
• Review the Application Layer
  This slide is a reminder for chapter 2 of the book Computer Networking: A Top-Down Approach, Sep 2020.
• Computer Networks and the Internet: A Comprehensive View
  This slide is a reminder for chapter 1 of the book Computer Networking: A Top-Down Approach, Sep 2020.
• How Should I Slice My Network?
  This slide is a report on the paper How Should I Slice My Network? A Multi-Service Empirical Evaluation of Resource Sharing Efficiency, published in MobiCom '18. This talk was held in Dec 2019.
• CrowdBC: A Blockchain-Based Decentralized Framework for Crowdsourcing
  This slide is a report on the paper CrowdBC: A Blockchain-Based Decentralized Framework for Crowdsourcing, published in IEEE TPDS, Jun 2019.
• Edge Intelligence: the Confluence of Edge Computing and Artificial Intelligence
  This slide is a report on our preprint Edge Intelligence: the Confluence of Edge Computing and Artificial Intelligence, Sep 2019.
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
  This slide is a report on the paper Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing, preprinted on arXiv, May 2019.
• On Mobile Edge Computing: Game Theory, Edge AI and Other New Ideas
  The slide reviews some important concepts and methods in Game Theory and Edge Intelligence. Especially their utilization in wireless communication and networks. The talk was held in Jan 2019.
• Lyapunov Optimization: An Introduction
  The slide is a simplified version of the monograph Lyapunov Optimization: An Introduction.
• Application of Optimization Methods and Edge AI
  In this slide, we review the methods used in MEC and discuss how to design a better system model with methods embedded naturally, Nov 2018.
• Towards an Intelligence Edge: Wireless Communication Meets Machine Learning
  In this slide, we study the learning-driven communication scheme, proposed in Towards an Intelligence Edge: Wireless Communication Meets Machine Learning, Sep 2018.
• Edge Intelligence: On-demand Deep Learning Model Co-inference with Device-edge Synergy
  In this slide, we study the Edgent framework, published in MECOMM'18.
• Models and Methods in Mobile Edge Computing Systems
  In this slide, we review the classic models and methods proposed in edge computing systems up to right now, Aug 2018.