Nonlinear Programming (81-404), Summer 2002

Lectures (Christoph Helmberg): 4, Tue 11:45-13:15, Room 48-208, Wed 11:45-13:15, Room 48-210,
Tutorial (Berhanu Guta): 2, Fri 10:00-11:30, Room 48-438

Overview

We discuss basic algorithmic approaches for solving smooth nonlinear optimization problems. Aspects of interest are convergence rate, computational efficiency and numerical behavior.
Part I Unconstrained Optimization: line search, trust regions, conjugate gradients, Newton and quasi-Newton methods, approximate and automatic differentation
Part II Constrained Optimization: Lagrange multipliers, optimality conditions, quadratic programming, penalty barrier and augmented Lagrangian methods, sequential quadratic programming

Literature

Main source: J. Nocedal, S.J. Wright; Numerical Optimization, Springer 1999;

Further reading:
Bazaraa, Sherali, Shetty; Nonlinear Programming: Theory and Algorithms, Wiley, 1993;
Luenberger; Linear and Nonlinear Programming, Addison-Wesley, 1984.

Exercises


Last modified: Tue Jul 16 10:31:07 CEST 2002