Unconstrained Optimization

This lecture note is intended for use in the course 04212 Optimization and Data Fitting at the Technincal University of Denmark. It covers about 25 % of the curriculum. Hopefully, the note may be useful also to interested persons not participating in that course. The aim of the note is to giveanintroduction to algorithms for unconstrained optimization. We present Conjugate Gradient, Damped Newton and Quasi Newton methods together with the relevant theoretical background. The reader is assumed to be familiar with algorithms for solving linear and nonlinear system of equations, at a level corresponding to an introductory course in numerical analysis. The algorithms presented in the note appear in any goodprogram library, and implementations can be found via GAMS (Guide to Available Mathematical Software) at the Internet addres

and Data Fitting

note isin tended for use in the cours