Course description
Optimization plays a key role in solving a large variety of decision problems that arise in engineering (design, process operations, embedded systems), data science, machine learning, business analytics, finance, economics, and many others. This course focuses on formulating optimization models and on the most popular numerical methods to solve them.
Syllabus
Modeling: linear programming models, convex optimization models. Basic optimization theory: optimality conditions, sensitivity, duality. Algorithms for constrained convex optimization: active-set methods for linear and quadratic programming, proximal methods and ADMM, stochastic gradient, interior-point methods. Line-search methods for unconstrained nonlinear programming, sequential quadratic programming.
Prerequisites
Linear algebra and matrix computation, calculus and mathematical analysis.
Timetable
| Monday | November 20, 2023 | 09.00-11.00 |
|---|---|---|
| Wednesday | November 22, 2023 | 09.00-11.00 |
| Friday | November 24, 2023 | 09.00-11.00 |
| Monday | November 27, 2023 | 09.00-11.00 |
| Wednesday | November 29, 2023 | 09.00-11.00 |
| Friday | December 1, 2023 | 09.00-11.00 |
| Monday | December 4, 2023 | 09.00-11.00 |
| Monday | December 11, 2023 | 09.00-11.00 |
| Wednesday | December 13, 2023 | 09.00-11.00 |
| Friday | December 15, 2023 | 09.00-11.00 |
Location
Hybrid mode: IMT School, Piazza San Francesco, 19 - Lucca / Online.
Lecture slides
| Optimization models, linear and convex programming | (updated 19/11/2023) |
|---|---|
| Optimization theory (optimality conditions, duality) | (updated 01/12/2023) |
| Basics of numerical linear algebra | (updated 10/11/2023) |
| Active-set methods | (updated 10/11/2023) |
| Operator splitting methods (proximal gradient, ADMM), stochastic gradient descent | (updated 10/11/2023) |
| Unconstrained nonlinear optimization, interior-point methods | (updated 15/12/2023) |