The Matlab environment and programming language. Direct methods for sparse linear systems. Iterative methods for nonlinear systems of equations. Newton-Cotes, composite and adaptive quadrature rules. Numerical methods for the Cauchy problem. Numerical methods for partial differential equations.
A. Quarteroni, R. Sacco, F. Saleri, Matematica numerica, Springer–Verlag.
J. Epperson, Introduzione all'analisi numerica,Teoria, metodi, algoritmi, Mc-Graw Hill.
Learning Objectives
Knowledge of standard and advanced numerical methods for solving linear and nonlinear equation, ordinary and partial diferential equations, and integrals.
Ability to solve problems modelling engineering applications, Matlab programming.
Prerequisites
Numerical Calculus
Teaching Methods
Lectures and Matlab laboratory.
Type of Assessment
Oral exam on the numerical methods presented, their theoretical properties and implementation.
Course program
Matlab: environment and programming language. Direct factorization for sparse linear systems. Newton method and its variants, linesearch strategies. Newton-Cotes and composite quadrature rules.
One-step merical methods for the Cauchy problem: Runge-Kutta and Runge-Kutta-Fehlberg methods, some implicit schemes. Finite differences methods for partial differential equations, an introduction to finite elements methods.