Link to the moodle e-learning site: Optimization Methods 2018/2019
Teaching Language: Italian (with english slides)
Brief Contents (Dipl.Sup.)
Optimality Conditions
Local unconstrained optimization
Local Constrained Optimization
Optimization methods for Machine Learning
Global optimization
Teaching 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 for constrained optimization (KKT conditions)
Application: Support Vector Machines
Convergence of algorithms
One-dimensional optimization
Gradient descent methods
Newton methods
Conjugate direction methods
Quasi-Newton methods
Trust Region methods
Constrained optimization methods
Global optimization
