| Code |
Course Name |
Language |
Type |
| MAT 351E |
Computational Optimization |
English |
Compulsory |
| Local Credits |
ECTS |
Theoretical |
Tutorial |
Laboratory |
| 3 |
6.5 |
3 |
0 |
0 |
| Course Prerequisites and Class Restriction |
| Prerequisites |
MAT 287 MIN DD
or MAT 287E MIN DD
|
| Class Restriction |
None |
| Course Description |
| Problem formulation in optimization and their graphical solutions. Unconstrained Optimization; conditions for local minima. Line
Search Methods; Golden Section method, Newton’s method. Multi Variable Problems; steepest descent method and scaling,
conjugate gradient methods: The Fletcher and Reeves Method, Modified Newton Method, Marquardt Modification, Quasi-Newton
methods: Davidon Fletcher Powel (DFP) method, Broyden Fletcher Goldfarb Shanno (BFGS) method. Least squares method,
Trust-region methods. Linear and Nonlinear Constrained Optimization Problems; Lagrange multipliers, Kuhn-Tucker conditions,
Sensitivity analysis, Quadratic programming, Penalty and Barrier methods, Simplex method. |
|
