Neptun ID, timetable, classroom
Attendance of the lectures is highly recommended. Attendance is registered. In-class performance is assessed and its results form part of the end-term grade. The final score (%) of the course is constructed as follows: 20% lecture and seminar attendance, 30% in-class test 50% homework/project presentation (last two lectures of the semester).
Grades (based on percentages)
- 80-100: 5 (excellent)
- 70-79: 4 (good)
- 60-69: 3 (medium)
- 50-59: 2 (satisfactory)
- 0-49: 1 (fail)
Online available material
Motivational examples and introduction to nonlinear programming, examples, basic definitions.
Unconstrained optimization: Gradient, Hessian, definitness of matrices and its relation to optima, Weierstrass-theorem.
Unconstrained optimization: 1st and 2nd order necessary condition for optimality; some geometry; the general line search method. Convexity and minimization.
Newton’s method for unconstrained optimization, convergence rate, excercises.
Quadratic optimization problem and examples.
Steepest descent method for unconstrained optimization. Conjugate gradient method.
Constrained optimization I: Lagrange dual function, duality theorems, KKT theorem.
Constrained optimization II: more on dulaity
Constrained optimization III: penalty and barrier methods
More on nonlienar programming models of real-life problems
More on nonlienar programming in machine learning
Project presentations, closing the semester.