Optimization Theory (DATA 620020)
Spring 2025
Lecture Time and Venue:
Friday, 18:30-21:05, HGX307
Reading Materials:
- 刘浩洋, 户将, 李勇锋, 文再文. "最优化:建模、算法与理论". 高教出版社, 2020. [link]
- 林宙辰, 李欢, 方聪. "机器学习中的加速一阶优化算法". 机械工业出版社, 2021. [link]
- Jorge Nocedal, Stephen J. Wright. "Numerical optimization". Springer, 2006. [link]
- Yurii Nesterov. "Lectures on convex optimization". Springer, 2018. [link]
- Ralph Tyrell Rockafellar. "Convex Analysis". Princeton University Press, 1997. [link]
Courseware (subject to changes):
- Course Overview, Optimization for Machine Learning, Review of Linear Algebra. [pdf]
- Matrix Calculus, Topology, Convergence Rates. [pdf]
- Convex Set, Convex Function, Optimal Condition. [pdf]
- Subgradient and Subdifferential, Subdifferential Calculus. [pdf]
- Optimal Condition, Regularity Conditions. [pdf]
- Subgradient and Subdifferential, Subdifferential Calculus. [pdf]
- Black Box Model, Gradient Descent Methods, Polyak–Łojasiewicz Condition, Line Search Methods, Barzilai-Borwein Step Size. [pdf]
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