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Non-linear Optimalization - AP413006

Title:	Non-linear Optimalization
Guaranteed by:	Department of Mathematics (413)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2019 to 2020
Semester:	both
Points:	0
E-Credits:	0
Examination process:
Hours per week, examination:	3/0, other [HT]
Capacity:	winter:unknown / unknown (unknown) summer:unknown / unknown (unknown)
Min. number of students:	unlimited
Language:	English
Teaching methods:	full-time
Teaching methods:	full-time
Level:
For type:	doctoral
Note:	course is intended for doctoral students only can be fulfilled in the future you can enroll for the course in winter and in summer semester

Guarantor:	Kubíček Milan prof. RNDr. CSc. Isoz Martin Ing. Ph.D.
Interchangeability :	P413006

Examination dates Schedule

Annotation -

Extreme values of functions of several real variables. Extreme with equality and inequality constrainst. Linear programming. Nonlinear programming, direct search methods, gradient methods, Newton method. Methods for constrained extreme. Elements of dynamic programming. Vector optimization, Pareto set construction.

Last update: Pátková Vlasta (16.11.2018)

Aim of the course -

Students will be able to: Understand and formulate an optimization problems. Solve the problem in simple cases, use the appropriate software in more complex cases. Classify the problem and propose an efficient solution. Solve the given project.

Last update: Pátková Vlasta (16.11.2018)

Literature -

Edgar T. F., Himmelblau D. M.,Lasdon L. S.: Optimization of Chemical Processes, McGraw-Hill, Boston, 2001.

Further literature individually.

Last update: Jahoda Milan (28.11.2018)

Teaching methods -

Self-study, consultation, solution of given problem.

Last update: Pátková Vlasta (16.11.2018)

Syllabus -

1. Formulation of the optimization problem.

2. Extrems of functions of real variables - methods of classical analysis.

3. Extrems of functions of real variables - unconstrained extreme, extrem with equality constraints.

4. Extrems of real variable functions - Extrems with inequality constraints.

5. Linear programming.

6. Simplex method.

7. Non-linear programming.

8. Adaptive search methods.

9. Gradient methods.

10. Penalty functions.

11. Fundamentals of dynamic programming.

12. Resource distribution problem.

13. Fundamentals of vector optimization.

14. Construction of a Pareto set.

Last update: Pátková Vlasta (16.11.2018)

Entry requirements -

Mathematics A, B

Last update: Borská Lucie (16.09.2019)

Registration requirements -

none

Last update: Borská Lucie (16.09.2019)

Course completion requirements -

Solving the given problem. Written and oral exam.

Last update: Pátková Vlasta (16.11.2018)