OPTIMISATION TECHNIQUES

OPTIMISATION TECHNIQUES

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iten
Code
86733
ACADEMIC YEAR
2019/2020
CREDITS
5 credits during the 1st year of 10635 ROBOTICS ENGINEERING (LM-32) GENOVA
SCIENTIFIC DISCIPLINARY SECTOR
MAT/09
LANGUAGE
English
TEACHING LOCATION
GENOVA (ROBOTICS ENGINEERING )
semester
1° Semester
Teaching materials

OVERVIEW

The Course introduces to optimization models and methods for the solution of decision problems, with particular attention to models and problems arising in Robotics Engineering. 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. 

AIMS AND CONTENT

LEARNING OUTCOMES

The lecture presents different theoretical and computational aspects of a wide range of optimization methods for solving a variety of problems in engineering and robotics.

AIMS AND LEARNING OUTCOMES

The Course aims at providing the students with the skills required to deal with engineering problems, with particular emphasis on Robotics Engineering, by developing models and methods that work efficiently in the presence of limited resources.

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 within 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.

PREREQUISITES

Linear algebra. Vector and matrix calculus. Basic mathematical analysis and geometry.

Teaching methods

Lectures and exercises. Continuous assessmnet. Mandatory attendance.

SYLLABUS/CONTENT

Introduction. Optimization and Operations Research for Robotics. Optimization models and methods.

Linear programming model and algorithms

Integer linear programming model and  algorithms

Nonlinear programming model and algorithms

Graph optimization models and algorithms

N-stage optimization: dynamic programming model and algorithms

Putting things together: models, methods, and algorithms for the optimisation of robotic systems

Software tools for optimization

Case studies from Robotics

RECOMMENDED READING/BIBLIOGRAPHY

Lecture notes provided by the teacher (study material will be available in the official study portal or in the teacher's web page)

TEACHERS AND EXAM BOARD

Ricevimento: By appointment.

Exam Board

RENATO UGO RAFFAELE ZACCARIA (President)

MARCELLO SANGUINETI (President)

MASSIMO PAOLUCCI

DANILO MACCIO'

MAURO GAGGERO

LESSONS

Teaching methods

Lectures and exercises. Continuous assessmnet. Mandatory attendance.

LESSONS START

Sempember 23, 2019

EXAMS

Exam description

Written exam. There will be questions on the main concepts explained during the lectures and it will be required to develop models and propose solution algorithms for problems arising in various applicative scenarios of engineering and robotics.

Assessment methods

Continuous assessment (30% of the overall evaluation) and final written exam (70% of the overall evaluation).

Exam schedule

Date Time Location Type Notes
09/06/2020 09:00 GENOVA Scritto Aula G1 - Inizio ore 10 Room G1 - Starting at 10 am Unica data disponibile per studenti EMARO/RobEng : 06/02/2020 ***** Only available date for EMARO/RobEng students: 06/02/2020
30/06/2020 09:00 GENOVA Scritto Aula G1 - Inizio ore 10 Room G1 - Starting at 10 am Unica data disponibile per studenti EMARO/RobEng : 06/02/2020 ***** Only available date for EMARO/RobEng students: 06/02/2020
10/09/2020 09:00 GENOVA Scritto Aula G1 - Inizio ore 10 Room G1 - Starting at 10 am Unica data disponibile per studenti EMARO/RobEng : 06/02/2020 ***** Only available date for EMARO/RobEng students: 06/02/2020

FURTHER INFORMATION

The lectures are organized in theory and case-studies from real-world applications. Other  supervised exercises and practice to use of software tools for optimization are available during additional hours with an instructor.