Content:
- 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
TU-Ilmenau | Prozessoptimierung