Course Details for A.Y. 2018/2019
Name:
Ricerca Operativa / Operations Research
Basic information
Credits:
: Bachelor Degree in Computer Science 6 CFU (c)
Degree(s):
Bachelor Degree in Computer Science 2^{nd} anno curriculum General Compulsory
Language:
Italian
Course Objectives
Introduce the student to the formulation of basic Optimization problems, particularly Linear Optimization problems, and train him/her to the related solution algorithms.
Course Content
 Optimization problems: decision varibles, objectives and constraints; modeling techniques and model classification
 Convex optimization problems; local and global optima
 Geometry of Linear Programming
 The Simplex method
 Duality theory in Linear Programming and its applications
 Dual interpretation of the simplex method and the dual simplex method
Learning Outcomes (Dublin Descriptors)
On successful completion of this course, the student should

Acquire the knowledge of Optimization problems and of the mathematical modeling techniques for complex decisions. Acquire the knowledge of some solution algorithms for Linear Programming problems.

Acquire the ability to recognize optimization problems and develop mathematical models of decisionmaking problems. Acquire the ability of computing solutions of linear programming problems

Acquire autonomy in modeling and algorithmic choices for problems related to complex decisionmaking

Be able to hold a conversation and to read texts on topics related to the modeling of decision problems and Linear Programming

Acquire the ability of upgrading flexible knowledge and skills in the field of Optimization and related problems that arise in various areas, such as mathematics, computer science and management science
Prerequisites and Learning Activities
Vector space, scalar product, matrix product, inverse matrix
Assessment Methods and Criteria
1. paper test consisting of various exercises (problem formulation, insights about algebraic or geometric problems properties, problem solution by know algorithms)
2. oral test about theoretical topics; this is accessible only for the students who earned a passing grade at the paper test; NOTE: a sufficient paper test allows the student only for the oral at the same date, but NOT for next dates.
3. A midterm test is also planned: a positive grade to it allows the student to skip the corresponding topics in the final test.
Textbooks
 Dimitris Bertsimas and John N. Tsitsiklis, Introduction to Linear Optimization , Athena Scientific. 1997.
 Matteo Fischetti, Lezioni di Ricerca Operativa , Progetto Libreria Padova. 1995.
 Antonio Sassano, Modelli e Algoritmi della Ricerca Operativa , Franco Angeli. 1992.
Course page updates
This course page is available (with possible updates) also for the following academic years:
To read the current information on this course, if it is still available, go to the university course catalogue .
Course information last updated on: 05 marzo 2019, 13:30