
Course unit
NUMERICAL ANALYSIS (Ult. numero di matricola da 0 a 4)
IN18101050, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Other 
 
 
3.0 
Basic courses 
MAT/08 
Numerical Analysis 
6.0 
Course unit organization
Period 
Second semester 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Lecture 
9.0 
72 
153.0 
No turn 
Prerequisites:

The knowledge of the main topics developed in the Mathematical Analysis course is useful. 
Target skills and knowledge:

The Numerical Analysis class aims at providing the engineering students with the basic knowledge of the numerical techniques for solving linear and nonlinear systems of equations, computing integrals and derivatives, interpolating and approximating data.
The main skills consist in the acquisition of the principles of numerical programming and the ability to develop codes for the solution of specific numerical applications. 
Examination methods:

The exam is subdivided into two partial tests carried out in different dates.
1. A written test (2 hours), consisting of two exercises and a theoretical question.
2. A programming test (1 hour and 15 minutes), carried out at the computer.
Both tests are passed with a score greater than or equal 10 18/30. The final score is the weighted average of the two scores, with weight 0.75 for the written test and 0.25 for the programming test. The score registration is performed through an appropriate list. In that occasion, the student can check the partial tests and carry out an oral test on a voluntary basis. 
Assessment criteria:

The evaluation is based on:
 the knowledge and capability of using numerical algorithms for the solution of specific mathematical problems
 the ability of implementing at the computer the algorithms for solving specific numerical problems
 the ability of analyzing critically and independently the acquired knowledge 
Course unit contents:

Introduction to Numerical Analysis: problem definition, computer representation, stability and illconditioning. Introduction to Numerical Programming: principles, use of the Matlab language. Solution to linear systems: direct (Gauss elimination, triangular factorization) and iterative (Jacobi, Seidel) methods. Solution to nonlinear systems: bisection, fixed point iteration, NewtonRaphson, Regula Falsi. Data interpolation and approximation: Lagrange polynomial, Newton differences, least square approximation. Numerical quadrature: NewtonCotes and Gauss formulas. Numerical integration of ordinary differential equations (hints). 
Planned learning activities and teaching methods:

Lectures and practical exercises for numerical programming in the computer science lab.
During the lectures, the course topics are discussed. The theory is enriched by several exercises.
Lectures in the computer room consist of two parts: (1) theory by the professor; (b) practical exercises taken independently by the student under the professor supervision. 
Additional notes about suggested reading:

Reference textbook and lecture notes. 
Textbooks (and optional supplementary readings) 

G. Gambolati, M. Ferronato, Lezioni di Metodi Numerici per l'Ingegneria. Seconda Edizione.. Padova: Libreria Progetto, 2017.

A. Mazzia, Laboratorio di Calcolo Numerico. MilanoTorino: Pearson Italia, 2014.


