Blogs

This is the starting page of my own blogs on convex optimization after 5 years of study of optimization theory since my master program at ETH Zurich. I started my optimization career by mistakes: I initially thought it is a compulsory course for master students back in China and start to learn it during the summer vacation, but then realize it is a really fascinating topic and shift my study into it.

In this blog, I will focus on the introduction to a variety of optimization algorithms.

Convex optimization

Blogs

• Basics of convex optimization
  1. What is convex optimization？common constraints and objectives.
  2. Disciplined Convex Programming
• Some notes
  1. Which problems a conic solver can not solve?.
• 凸优化算法
  1. 零阶算法
  2. 一阶算法
     1. Feasible methods
        • 梯度下降
        • 投影梯度下降
        • proximal algorithm
        • simplex (active-set) method
     2. Infeasible method
        • 增广拉格朗日法
        • ADMM
        • 算子分裂法
  3. 二阶算法
     1. 内点法
     2. 拟牛顿法
• 凸优化案例
  • 模块预测控制 (MPC)
  • 支持向量机 (SVM)
  • Portfolio optimization
  • 最优潮流 (Optimal power flow)
  • 最优传输 (Optimal transport)
  • 航天器软着陆控制
• 凸优化求解器
  1. Gurobi
  2. Mosek
  3. Clarabel
  4. PDLP
  5. OSQP
  6. SCS
• 凸优化建模平台
  1. Python
     • cvxpy
  2. Julia
     • JuMP
• 非凸优化

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