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, Quasi-Newton methods,
derivative calculations, derivative-free methods (2h).
- Least-square 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,
interior-point). Examples of LPs in Python. Introduction nonlinear
programming problems. Quadratic programming problems (active set,
interior-point) (3h).
- Penalty and augmented Lagrangian methods for NLP.
Sequential Quadratic Programming, Interior-point 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).
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 Updating... Ċ Giovanni Mengali, 31 gen 2020, 06:30
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