CODE 80155 ACADEMIC YEAR 2018/2019 CREDITS 7 cfu anno 1 MATEMATICA 9011 (LM-40) - GENOVA 7 cfu anno 3 MATEMATICA 8760 (L-35) - GENOVA 9 cfu anno 1 INGEGNERIA INFORMATICA 8733 (LM-32) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR MAT/09 TEACHING LOCATION GENOVA SEMESTER 1° Semester TEACHING MATERIALS AULAWEB OVERVIEW 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 Information Technology are presented and investigated. AIMS AND CONTENT LEARNING OUTCOMES The Course introduces optimization models and methods that can be used to solve decision-making problems. It is part of the fundamental themes of problem modeling, study of computational handling, and resolution through algorithms that can be implemented on a computer. Various application contexts are considered, and some "case studies" in the IT field are discussed in detail. The aim of the course is to acquire the skills to deal with application problems by developing models and methods that work efficiently in the presence of limited resources. Students will be taught to: interpret and shape a decision-making process in terms of an optimization problem, identifying decision-making variables, the cost function to minimize (or the merit digit to maximize) and 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 designing) and an appropriate processing software support. AIMS AND LEARNING OUTCOMES The students will be taught to: - interpret and shape a decision-making process in terms of an optimization problem, identifying the decision-making 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. The students will be taught to: - interpret and shape a decision-making process in terms of an optimization problem, identifying decision-making variables, the cost function to minimize (or the merit digit to maximize) and 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 designing) and an appropriate processing software support. PREREQUISITES Linear Algebra. Vector and matrix calculus. Basic concepts of Mathematical Analysis and Geometry. TEACHING METHODS Lectures and exercises SYLLABUS/CONTENT 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 RECOMMENDED READING/BIBLIOGRAPHY Lecture notes provided by the teacher and available in electronic format. TEACHERS AND EXAM BOARD MARCELLO SANGUINETI Ricevimento: By appointment. LESSONS LESSONS START September 17, 2018 Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Written. ASSESSMENT METHODS Comprehension of the concepts explained during the Course. Capability to: - interpret and shape a decision-making process in terms of an optimization problem, identifying the decision-making 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. Exam schedule Data appello Orario Luogo Degree type Note 07/02/2019 10:00 GENOVA Scritto 11/06/2019 10:00 GENOVA Scritto 27/06/2019 09:00 GENOVA Scritto 05/09/2019 14:00 GENOVA Scritto FURTHER INFORMATION For the Laurea in Mathematics, which "borrows" only 7 cfu, the following topics are excluded: DYNAMIC PROGRAMMING CASE STUDIES FROM INFORMATION TECHNOLOGY