Scheda programma d'esame
TEORIA E METODI DELL'OTTIMIZZAZIONE
|TEORIA E METODI DELL'OTTIMIZZAZIONE||MAT/09||LEZIONI||42|
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KnowledgeStudents are expected to acquire: knowledge of the theories of nonlinear optimization, with particular reference to convex optimization knowledge of the main methods for the solution of nonlinear optimization problems; tools for modeling specific problems through optimization from the following areas: regression and parameter estimation in statistics, approximation and data fitting, machine learning, computer graphics, logistics, transportation, economic equilibria and finance.
Assessment criteria of knowledgeThe student will be assessed on his/her demonstrated ability to discuss the main course contents using the appropriate terminology. During the oral report and exam the student must be able to demonstrate his/her knowledge of the course material together with adequate language and proper terminology. Critical awareness of the topics will be also evaluated.
- Final oral exam
- Oral report
- Written report
Oral report and final oral exam 70%, written report 30%
Delivery: face to face
- attending lectures
- participation in seminar
- preparation of oral/written report
Attendance: Not mandatory
SyllabusClassification of optimization problems. Nonlinear optimization: convex analysis, local and global minima, optimality conditions, duality theory, algorithms for unconstrained optimization (gradient, Newton, derivative-free methods) and constrained optimization (conditional and projected gradient, penalization, interior point methods). Nonlinear least-squares. Equilibria in noncooperative games. Applications.
BibliographyLectures are not baes on a unique textbook. Lecture notes by the instructor will be provided, and a precise reference for each topic will be given as well. Recommended general reading includes: N M.S. Bazaraa, H.D. Sherali, C.M. Shetty, Nonlinear Programming: Theory and Algorithms, Wiley, 1993 D. Bertsekas, Nonlinear Programming, Athena, 2004 J.-B. Hiriart-Urruty, C. Lemarechal, Convex Analysis and Minimization Algorithms, Springer, 1996.
Ultimo aggiornamento 14/11/2016 17:27