Syllabus
1. Optimization problems and their formulations. Applications in economics, finance, logistics and mathematical statistics.
2. Basic parts of convex analysis (convex sets, convex multivariate functions).
3. Linear Programming (structure of the set of feasible solutions, simplex algorithm, duality, Farkas theorem).
4. Integer Linear Programming (applications, branch-and-bound algorithm).
5. Nonlinear Programming (local and global optimality conditions, constraint qualifications).
6. Quadratic Programming as a particular case of nonlinear programming problem.
Annotation
Introduction to optimization theory. Recommended for bachelor's program in General Mathematics, specialization
Stochastics.