Information
Neptun ID, timetable, classroom
IMEN102E/G,Monday 1013
Description
Requirements
Attendance of the lectures is highly recommended. Attendance is registered. Inclass performance is assessed and its results form part of the endterm grade. The final score (%) of the course is constructed as follows: 20% lecture and seminar attendance, 30% inclass test 50% homework/project presentation (last two lectures of the semester).
Grades (based on percentages)
 80100: 5 (excellent)
 7079: 4 (good)
 6069: 3 (medium)
 5059: 2 (satisfactory)
 049: 1 (fail)
Online available material
see Coospace
Syllabus

Lecture 1
Motivational examples and introduction to nonlinear programming, examples, basic definitions.

Lecture 2
Unconstrained optimization: Gradient, Hessian, definitness of matrices and its relation to optima, Weierstrasstheorem.

Lecture 3
Unconstrained optimization: 1st and 2nd order necessary condition for optimality; some geometry; the general line search method. Convexity and minimization.

Lecture 4
Newton’s method for unconstrained optimization, convergence rate, excercises.

Lecture 5
Quadratic optimization problem and examples.

Lecture 6
Steepest descent method for unconstrained optimization. Conjugate gradient method.

Lecture 7
Constrained optimization I: Lagrange dual function, duality theorems, KKT theorem.

Lecture 8
Constrained optimization II: more on dulaity

Lecture 9
Constrained optimization III: penalty and barrier methods

Lecture 10
More on nonlienar programming models of reallife problems

Lecture 11
More on nonlienar programming in machine learning

Lecture 12
Project presentations, closing the semester.