Optimization and Systems Theory
SF3840 Numerical nonlinear programming, 7.5cr
This course is primarily intended for graduate students in optimization
and systems theory, or other graduate students with a good background in
Summary of contents
The course deals with algorithms and fundamental theory for nonlinear
finite-dimensional optimization problems. Fundamental optimization
concepts, such as convexity and duality are also introduced. The main
focus is nonlinear programming, unconstrained and constrained. Areas
considered are unconstrained minimization, linearly constrained
minimization and nonlinearly constrained minization. The focus is on
methods which are considered modern and efficient today.
Unconstrained nonlinear programming: optimality conditions, Newton
methods, quasi-Newton methods, conjugate gradients, least-squares
Constrained nonlinear programming: optimality conditions, quadratic
programming, SQP methods, penalty methods, barrier methods, dual
Linear programming is treated as a special case of nonlinear
Semidefinite programming and linear matrix inequalities are also
Suitable prerequisites are the courses SF2822 Applied Nonlinear
Optimization, DN2251 Applied Numerical Methods III and SF2713
Foundations of Analysis, or similar knowledge.
The lecture notes  are available
in the form of a pdf file.
||P. E. Gill and M. H. Wright,
Computational optimization: Nonlinear programming.
Students may, if they wish, choose textbooks such as ,  and  for
The textbooks are not required for the course, and will not be
distributed through KTH.
||P. E. Gill, W. Murray, and M. H. Wright.
Academic Press, London and New York, 1981.|
Athena Scientific, 1996.|
||J. Nocedal and S. J. Wright.
Lectures are held Wednesdays 13.15-15.00 in Room
3733, Lindstedtsvägen 25.
There will tentatively be 12 lectures, one lecture a week.
The examination is by five sets of homework assignments and a final oral exam.
Anders Forsgren, room 3533,
Lindstedtsvägen 25, tel. 790 71 27. E-mail: firstname.lastname@example.org.