Optimization for Data Sciences

École Polytechnique, Master 2 · 2026–2027

Schedule

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

• Lecture 1 notes
• Lecture 1 (PDF)

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

• Lecture 2 notes
• Lecture 2 (PDF)

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

• Lecture 3 notes
• Lecture 3 (PDF)

Sept. 23: Conjugate gradient (A. Gramfort)

Sept. 30: Proximal methods (S. Vaiter)

References

• Convex Optimization by Boyd and Vandenberghe.
• Lecture Notes on Convex Optimization by Nesterov.
• Numerical Optimization by Nocedal and Wright.
• Optimization for Modern Data Analysis by Recht and Wright.

Other years: 2025–2026 · all years