Learning objectives
One of the main aims of the course is to provide the mathematical foundation underlying the different methods or algorithms, recall the main theoretical properties: stability, accuracy, algorithmic complexity, and show examples and counterexamples which illustrate the advantages and weaknesses. It also aims to test the algorithms presented in a simple and fairly universal software such as MATLAB.
Prerequisites
Basics: Calculus and Linear Algebra.
Course unit content
Error Analysis - Approximation of data and functions - Numerical integration: Newton-Cotes formulas - Hint formulas for integrals in multiple dimensions - Systems of linear equations: direct methods, factorization, iterative methods - Nonlinear equations - Ordinary differential equations (discrete methods one step) - Introduction to Matlab
Full programme
Error Analysis, Representation of numbers in a computer, rounding errors, machine operations, Cancellation numerical conditioning of a problem and stability of an algorithm.
Accuracy of data and functions: polynomial interpolation, Lagrange interpolation formula, Hermite interpolation formula, the formula of Newton divided differences, interpolation of piecewise polynomial functions, spline functions, interpolation of functions of several variables (hint).
Numerical integration: interpolatory quadrature formulas, according to Newton-Cotes Integration, Error estimates, Formule composed, Applications of quadrature formulas.
Numerical linear algebra: direct methods, the method of Gaussian elimination, Gauss decomposition and LU factorization, Iterative Refinement, matrix inverse. Iterative methods: Jacobi method, Gauss-Seidel method, Method of sovrarilassamento (SOR).
Equations and nonlinear systems: real roots of nonlinear equations, bisection method, secant methods, the tangents (Newton-Raphson), Test of convergence, iterative methods in general, Aitken acceleration method.
Ordinary differential equations: methods one-step explicit Runge-Kutta methods, the local behavior of one-step methods, convergence of the methods one-step explicit estimate local truncation error and the choice of the integration step. Multistep methods (hint). Stability of numerical methods.
Bibliography
A.Quarteroni, R.Sacco, F.Saleri: Matematica Numerica, Springer.
G.Naldi, Lorenzo Pareschi, G.Russo, Introduzione al Calcolo Scientifico, McGraw-Hill.
G.Monegato, Fondamenti di Calcolo Numerico, CLUT.
Teaching methods
Lectures and exercises in the classroom. MATLAB numerical exercises in the laboratory. Correction of exercises assigned individually.
Assessment methods and criteria
Written test laboratory followed by an oral.
Other information
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