CODE  80155 

ACADEMIC YEAR  2022/2023 
CREDITS 

SCIENTIFIC DISCIPLINARY SECTOR  MAT/09 
LANGUAGE  English 
TEACHING LOCATION 

SEMESTER  1° Semester 
TEACHING MATERIALS  AULAWEB 
The Course introduces to optimization models and methods for the solution of decision problems. It is structured according to the basic topics of problem modelling, its tractability, and its solution by means of algorithms that can be implemented on computers. Case studies from Engineering, with particular attention to Information Technology, are presented and investigated.
The lectures are organized in i) methodology and ii) casestudies from realworld applications. Additional exercises and use of software tools are presented during exercise hours.
The Course introduces to optimization models and methods for the solution of decision problems. It is structured in the main topics of problem modelling, computational tractability, and solution by means of algorithms that can be implemented on a computer. Several applications are considered and various case studies are detailed. The target of the Course consists in making the students acquire the expertise to face decision problems by means of models and methods that can operate in the presence of limited resources. The students will be taught to: understanding and modelling a decision process in terms of an optimization problem by defining the decision variables, the cost function to be minimized (or the figure of merit to be maximized), and the constraints; framing the obtained problem within the range of the reference optimization problems (linear/nonlinear, discrete/continuous, deterministic/stochastic, static/dynamic, etc); achieving the matching between the corresponding solving algorithm and a suitable software.
The students will be taught to:
 interpret and shape a decisionmaking process in terms of an optimization problem, identifying the decisionmaking variables, the cost function to minimize (or the figure of merit to maximize), and the constraints;
 framing the problem in the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.);
 realizing the "matching" between the solving algorithm (to choose from existing or to be designed) and an appropriate processing software support.
Linear Algebra. Vector and matrix calculus. Basic concepts of Mathematical Analysis and Geometry.
Lectures and exercises.
INTRODUCTION TO OPERATIONS RESEARCH
LINEAR PROGRAMMING
DUALITY
INTEGER PROGRAMMING
GRAPH AND NETWORK OPTIMIZATION
COMPLEXITY THEORY
NONLINEAR PROGRAMMING
DYNAMIC PROGRAMMING
CASE STUDIES FROM INFORMATION TECHNOLOGY
SOFTWARE TOOLS FOR OPTIMIZATION
Lecture notes provided by the teacher and available in electronic format.
Office hours: By appointment
MARCELLO SANGUINETI (President)
MAURO GAGGERO
DANILO MACCIO'
ANGELA LUCIA PUSILLO
MASSIMO PAOLUCCI (President Substitute)
All class schedules are posted on the EasyAcademy portal.
Written, if it will be possible to make exams "in presence". Otherwise, the teacher will decide whether the exam via Teams will be written or oral.
Comprehension of the concepts explained during the Course.
Capability to:
 interpret and shape a decisionmaking process in terms of an optimization problem, identifying the decisionmaking variables, the cost function to minimize (or the figure of merit to maximize), and the constraints;
 frame the problem in the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.);
 choose and/or develop a solving algorithm and apply it to solve the problem.
Date  Time  Location  Type  Notes 

21/12/2022  11:30  GENOVA  Scritto  
09/01/2023  10:30  GENOVA  Scritto  
02/02/2023  10:30  GENOVA  Scritto  
29/05/2023  10:30  GENOVA  Scritto  
17/07/2023  10:30  GENOVA  Scritto  
11/09/2023  10:30  GENOVA  Scritto 
For the Laurea in Mathematics, which "borrows" only 7 cfu, the following topics are excluded:
DYNAMIC PROGRAMMING
CASE STUDIES FROM INFORMATION TECHNOLOGY