Politecnico di Torino
Politecnico di Torino
   
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Politecnico di Torino
Academic Year 2015/16
01OUVOV, 01OUVNG
Optimization methods and algorithms
Master of science-level of the Bologna process in Computer Engineering - Torino
Master of science-level of the Bologna process in Mathematical Engineering - Torino
Teacher Status SSD Les Ex Lab Years teaching
Tadei Roberto ORARIO RICEVIMENTO PO MAT/09 40 20 0 6
SSD CFU Activities Area context
MAT/09 6 C - Affini o integrative Attivitą formative affini o integrative
Esclusioni:
04CFI; 01PDC; 01PDX; 01NNE
ORA-01722: invalid number
Subject fundamentals
The course is taught in English.
The aim of the course is to provide students with theoretical and operational tools for modeling and solving optimization problems in information and communication engineering.
The optimization provides powerful mathematical methods (mathematical models, algorithms, and software) for solving complex problems involving the minimization (or maximization) of objective functions, subject to appropriate constraints.
Starting from real-life problems, such as the design of computer or telecommunication networks, data base management, and network flows, the theory and algorithms for solving the optimization problems which underlie those problems will be studied. Particular attention will be paid to the problem computational complexity and the required solution methods.
Expected learning outcomes
Expected knowledge:
Students will study methods and algorithms for solving constrained optimization problems. They will learn how to use linear continuous and integer programming and develop the most suitable solution method to solve the problem. Particular attention will be given to graph problems such as the min cost flow problem and the max flow problem. The computational complexity of optimization problems, which affects the choice of suitable solving algorithms, will be studied. The considered solution methods are both exact (Branch and Bound, Dynamic Programming, and Branch and Cut) and approximated (heuristics and metaheuristics).

Expected skills:
The skills developed by students will consist of the construction of mathematical models and related algorithms for solving optimization problems of information and communication engineering, such as flows on networks, network design, location, scheduling, and routing.
Prerequisites / Assumed knowledge
Prerequisites - Students must know at least one of the following programming languages: C, C++, and Java.
The only prior knowledge are those already acquired in the first years courses.
Contents
-1. Linear programming: modeling techniques, basic concepts of the Simplex method (10% of the course).
2. Computational complexity: problem classes P, NP, NP-complete, and CoNP-complete (10% of the course).
3. Exact optimization methods: Branch and Bound, Dynamic Programming, and Branch and Cut (25% of the course).
4. Heuristic Optimization methods: greedy algorithms, GRASP, Beam Search, meta-heuristics (Tabu Search, Simulated Annealing, Genetic Algorithms, ACO, VNS, RBS), and math-heuristics (45% of the course).
5. Network flow problems: min cost flow and max flow (10% of the course).