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B024334 - OPTIMIZATION OF COMPLEX SYSTEMS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2020-21
Coorte 2020 - Second Cycle Degree in Computer Engineering
Course year
First year - Second Semester
Belonging Department
Information Engineering (DINFO)
Course Type
Single education field course
Scientific Area
MAT/09 - OPERATIONS RESEARCH
Credits
6
Teaching Hours
48
Teaching Term
01/03/2021 ⇒ 11/06/2021
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Teaching Language
Italian
Course Content
Decomposition methods. Multiobjective optimization. Equilibrium models. Sparse optimization. Multiobjective optimization.
Suggested readings (Search our library's catalogue)
Lecture notes.
Learning Objectives
1) Ability to formulate nonlinear optimization problems.
2) Knowledge of algorithms for large-scale optimization, sparse optimization, newtork equilibrium, machine learning,
multi-agent optimization, multiobjective optimization.
3) Ability to use e adapt standard nonlinear optimization algorithms to specific practical contexts.
2) Knowledge of algorithms for large-scale optimization, sparse optimization, newtork equilibrium, machine learning,
multi-agent optimization, multiobjective optimization.
3) Ability to use e adapt standard nonlinear optimization algorithms to specific practical contexts.
Prerequisites
Linear algebra and analysis
Teaching Methods
Frontal lectures
Book Metodi di Ottimizzazione non vincolata, Grippo-Sciandrone, Springer 2011
Book Metodi di Ottimizzazione non vincolata, Grippo-Sciandrone, Springer 2011
Type of Assessment
Written (or oral) examination having theoretical questions:
- to verify the ability to formulate optimization problems;
- to verify the ability to use and adapt standard nonlinear optimization algorithms;
- to verify the knowledge of algorithms for classes of complex optimization problems.
- to verify the ability to formulate optimization problems;
- to verify the ability to use and adapt standard nonlinear optimization algorithms;
- to verify the knowledge of algorithms for classes of complex optimization problems.
Course program
Decomposition algorithms for unconstrained and constrained optimization. Decomposition algorithms for machine learning. Steepest descent methods for multiobjective optimization. Nash equilibrium: models and algorithms. . LASSO methods for sparse optimization. Concave programming for minimizing the zero norm. Algorithms for multiobjective optimization.