control course

Numerical Optimization

Teacher: Alberto Bemporad

COURSE DESCRIPTION

Abstract

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
Friday February 19, 2021 11.00-13.00
Monday February 22, 2021 11.00-13.00
Wednesday February 24, 2021 11.00-13.00
Friday February 26, 2021 11.00-13.00
Monday March 1, 2021 11.00-13.00
Wednesday March 3, 2021 11.00-13.00
Friday March 5, 2021 11.00-13.00
Monday March 11, 2021 11.00-13.00
Wednesday March 10, 2021 11.00-13.00
Friday March 12, 2021 11.00-13.00
LOCATION

Virtual classroom

LECTURE SLIDES
Last update: February 8, 2021