Optimization Methods

Introductory level knowledge of numerical nonlinear optimization. Both unconstraint and constraint methods are discussed. A review of methods for searching for optima of convex and non-convex optimization problems will be provided. In particular, gradient, nongradient and heuristic methods will be discussed. During computer laboratory activity case studies of practical applications of introduced methods will be implemeneted.

1. Acknowledgment of classification of nonlinear optimization problems.

2. Demonstration of numeric procedures relevant to searching for optimal solutions referring to non-linear problems.

3. Discussion about advantages and disadvantages of particular methods.

4. Continuation of learning skills as regards to ordering a structure of decision problems and choosing adequate models

Literatura podstawowa:

E. K. P. Hong, S. Zak, An Introduction to Optimization, Willey, 2008

Literatura uzupełniająca:

4. J. Nocedall, S. J. Wright, Numerical Optimization, Springerr, 2006.

5. R. K. Sundarm, A Fisrt Course in Optimization, Cambridge University Press, 1996.

Wiedza:

Correctly define non-linear optimization problem.

Identify basic analytic features of problems on the basis of known fragments of nonlinear optimization theory.

Understand rules of operating of nonlinear optimization algorithms.

Umiejętności:

Formulate nonlinear optimization model to a given decision case.

Specify reasons of choosing a numeric procedure to searching for the optimal decision.

Carry out the calculations in Octave.

Kompetencje społeczne:

Basic programming skills in Octave.

Presentation of results to decision makers.

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