#+title: Optimization for Data Sciences
#+date: 2026-09-09
#+status: in progress

* Schedule
Sept. 9: Introduction and Convex Analysis (S. Vaiter)

- [[lecture:a-first-look-at-convex-analysis][Lecture 1 notes]]
- [[lecture-pdf:a-first-look-at-convex-analysis][Lecture 1 (PDF)]]

Sept. 16 (morning): Gradient descent (S. Vaiter)

- [[lecture:following-the-steepest-direction][Lecture 2 notes]]
- [[lecture-pdf:following-the-steepest-direction][Lecture 2 (PDF)]]

Sept. 16 (afternoon): Automatic differentiation (S. Vaiter)

- [[lecture:automatic-differentiation][Lecture 3 notes]]
- [[lecture-pdf:automatic-differentiation][Lecture 3 (PDF)]]

Sept. 23: Conjugate gradient (A. Gramfort)

Sept. 30: Proximal methods (S. Vaiter)

* References
- [[https://web.stanford.edu/~boyd/cvxbook/][Convex Optimization by Boyd and Vandenberghe]].
- Lecture Notes on Convex Optimization by Nesterov.
- [[https://www.ime.unicamp.br/~pulino/MT404/TextosOnline/NocedalJ.pdf][Numerical Optimization by Nocedal and Wright]].
- Optimization for Modern Data Analysis by Recht and Wright.