Link to the moodle e-learning site: Optimization Methods 2016/2017

Teaching Language:  Italian (with english slides)

Brief Contents (Dipl.Sup.)
Optimality Conditions
Local unconstrained optimization
Local Constrained Optimization
Optimization methods for Machine Learning
Large Scale optimization

 

Teachin material:
Metodi di ottimizzazione non vincolata, L. Grippo, M. Sciandrone, Springer-Verlag, 2011

Additional Lecture Notes will be distributed

Prerequisites:

Elementary knowledge of calculus (Taylor expansions, gradients, Hessian matrix)
Linear algebra
A course on operations Research / linear programming might prove useful

Teaching style: Front lectures

Exams: Written or oral (in alternative) exam on all the course subjects

Syllabus:

Introduction;
Optimization models and examples
Basic definitions
Optimality conditions (unconstrained case)
Convergence of algorithms
One-dimensional optimization
Gradient descent methods
Newton methods
Conjugate direction methods
Quasi-Newton methods
Trust Region methods
Optimality conditions for constrained optimization (KKT conditions)
Constrained optimization methods
Introduction to machine learning
Stochastic gradient methods
Large scale optimization