The main objective of the course is to acquire knowledge on the types of nonlinear optimization methods and evolutionary programming techniques.

The acquired knowledge can be used in solving practical engineering problems and forms a basis for future engineering subjects.

Basic principles of optimization. Optimization problem. One-dimensional optimization. Sufficient and necessary conditions in the scalar case. One-dimensional search algorithms. Multidimensional search optimization without constraints. Bounded variation method. Lagrange multipliers method. Multidimensional numerical optimization. Newton and quasi-Newton algorithms. Nealder-Meade algorithms. Multidimensional constrained optimization methods. Basic principles of convex programming. Kuhn-Tucker conditions. Numerical methods of multidimensional constrained programming. Linear programming. Quadratic programming. Basic principles of global optimization. Evolutionarz and genetic computation. Particle swarm optimization. Basic principles of modern global optimization algorithms: ACO (Ant Colony Optimization), BFO (Bacteria Foragging Optimization), ...

Lectures. Study. Research