The course is articulated in 5 weeks,
each comprising 5 hours of class lectures and lab activities.Below is a list of the 10 lectures. More details can be found in the attached sheet.
 Introduction to optimization problems, sets,
linear algebra basics, derivatives, convex sets and functions. Definition
of an optimization problem of interest for each student (or groups). (2h)
 Introduction to unconstrained optimization, line
search methods, trust region methods. Basic
operations in Python (3h).
 Conjugate gradient methods, QuasiNewton methods,
derivative calculations, derivativefree methods (2h).
 Leastsquare problems, nonlinear systems of
equations. Unconstrained optimization (kinetic example) and solution of
systems of equations in Python (3h).
 Introduction to constrained optimization,
optimality conditions (2h).
 Linear programming problems (simplex,
interiorpoint). Examples of LPs in Python. Introduction nonlinear
programming problems. Quadratic programming problems (active set,
interiorpoint) (3h).
 Penalty and augmented Lagrangian methods for NLP.
Sequential Quadratic Programming, Interiorpoint methods for NLP. (2h)
 Solving NLP in Python and solution of the student
optimization problems (3h).
 Optimization examples: optimal control problems (2h)
 Optimization examples: state and parameter
estimation, equilibrium shapes of elastic structures. Review
of student problems (3h).

Updating...
Ċ Giovanni Mengali, 25 gen 2018, 10:28
