Systems Optimization - Interaktive Studienpläne der TU Ilmenau

Fundamentals of Mathematics and Control Engineering

• fundamentals, problem formulation, and classification of optimization methods
• methods and tools for optimization
• different problem formulations and mathematical derivation of optimization methods
• applications in industrial processes

Linear Optimization:

Theory of linear programming, degree of freedom, feasible region, graphical description/solution, Simplex method, mixing problem, optimal production planning

Nonlinear Optimization:
Convexity analysis, problems without uand with constraints, optimality condition, the gradient-, Newton-, Quasi-Newton-methods, KKT conditions, sequential quadratic programming (SQP) methods, active-set method, approximation of the Hessian matrix, application in optimal design of industrial processes.

Mixed-Integer Optimization :
Mixed-Integer Linear Programming (MILP), Branch-and-Bound method, optimization software GAMS, application in optimal design of industrial processes.

Dynamic Optimization:

Discretization in time, Euler method, orthogonal collocation, solution of the problem with SQP

Video on Demand, Moodle-Kurs, Webex-Veranstaltungen, Folien, Skripte

U. Hoffmann, H. Hofmann: Einführung in die Optimierung, Verlag Chemie, Weinheim, 1982

T. F. Edgar, D. M. Himmelblau: Optimization of Chemical Processes, McGraw-Hill, New York, 1989

Teo, K. L., Goh, C. J., Wong, K. H: A Unified Computational Approach to Optimal Control Problems. John Wiley & Sons, New York, 1991

C. A. Floudas: Nonlinear and Mixed-Integer Optimization, Oxford University Press, 1995

L. T. Biegler, I. E. Grossmann, A. W. Westerberg: Systematic Methods of Chemical Process Design. Prentice Hall, New Jersey, 1997

M. Papageorgiou: Optimierung, Oldenbourg Verlag, München, 2006