
Course unit
MODEL IDENTIFICATION, CALIBRATION AND DATA ANALYSIS
INP5070359, A.A. 2017/18
Information concerning the students who enrolled in A.Y. 2017/18
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Core courses 
INGINF/04 
Automatics 
9.0 
Mode of delivery (when and how)
Period 
Second semester 
Year 
1st Year 
Teaching method 
frontal 
Organisation of didactics
Type of hours 
Credits 
Hours of teaching 
Hours of Individual study 
Shifts 
Lecture 
9.0 
72 
153.0 
No turn 
Start of activities 
26/02/2018 
End of activities 
01/06/2018 
Prerequisites:

None 
Target skills and knowledge:

Objective
Introduce the students to the advanced topics of linear algebra and model identification.
Outcomes
A student who has met the objectives of the course will have a fundamental knowledge of :
• Linear algebra and numerical methods for large sparse matrices
• Deterministic and stochastic methods for model identification and calibration 
Course unit contents:

1. Review of linear algebra concepts;
2. Iterative methods for the solution of large, sparse linear systems: a) conjugate gradient methods for symmetric systems; b) projection methods for nonsymmetric systems (GMRESBiCGSTAB); c) preconditioning; incomplete factorizations; sparse factorized approximate inverses; d) implementation techniques; sparse (CSR) matrix storage;
3. Methods for the calculation of eigenvalues and eigenvectors: a) Power and inverse power (with shift) methods; b) QR method.
4. Newton methods for nonlinear systems: a) derivation of the Newton methods; b) local convergence properties and introduction to globalization techniques; c) Picard method; d) implementation of the NewtonKrylov and inexact Newton methods.
5. The calibration as an ill posed problem;
6. Penalizing functions;
7. Likelihood method for estimation;
8. Generalized Method of Moments;
9. Deterministic and stochastic algorithms. 
Textbooks (and optional supplementary readings) 


