This page contains information about the course;
lectures, exercise classes, take-home assignments, etc.
It will be updated during the course.
Exam and partial solutions Apr 05, 2018
Exam and Solutions Jan 10, 2018
Intro Slides
First set of slides from lecture 13
Second set of slides from lecture 13
Summary slides of important concepts
Some formal information about the course:
In Swedish.
In English.
Lecturer and examiner
Johan Karlsson,
johan.karlsson(a)math.kth.se
Office 3550, Lindstedtsv 25, phone 08-790 8440.
Teaching assistants
Tove Odland,
odland(a)math.kth.se.
David Ek,
daviek(a)kth.se.
Teaching assistants' page. Updated during the course.
Home assignment 2.
This home assignment is on Quadratic optimization.
The deadline is 17:00, December 15, 2017. Only hand in one solution for each group. Each solution should consist of one report (pdf/doc) and a separate file for the code (m-file).
Matlab script for the covariance matrix.
Home assignment 1.
This home assignment is on Linear optimization.
The deadline is 15:00 on Wednesday, November 22, 2017.
The matrices Redarc and Bluearc.
The home assignments should be carried out either alone or together
with one other student.
A possible way to find another student to work with
might be to write a post in the "News feed" ("Nyhetsflödet")
on the
course web.
Course literature
The main literature for the course is the compendium
"Optimization"
by Amol Sasane and Krister Svanberg (ASKS),
which you can buy at the
KTH bookstore.
ASKS contains some exercises, for which solutions
are available below.
Additional exercises:
"Exercises in Optimization" (EXOPT).
We also recommend the
book
Linear and Nonlinear Optimization, second edition,
by Griva, Nash and Sofer.
We encourage you to buy this book, especially
if you consider taking the follow-up courses
SF2812 and
SF2822,
since it is used as course literature in both these courses.
Here you find some information about the book.
Information about the exam
Schedule: Times and locations
Detailed schedule
Preliminary schedule for the lectures 2017
| 1. | Course intro. Linear Programming (LP). |
| 2. | The Simplex method for solving LP problems. |
| 3. | More on the Simplex method. |
| 4. | Network flows and linear algebra. |
| 5. | Duality and complementarity in LP. |
| 6. | LP duality and a game. Quadratic functions. |
| 7. | QP without constraints. LDLT factorization. |
| 8. | QP with linear equality constraints. |
| 9. | Least Squares problems and the pseudoinverse. |
| 10. | Nonlinear Programming (NLP). Convex problems. |
| 11. | NLP without constraints. Newton and Gauss-Newton. |
| 12. | Inequality constraints and the KKT conditions. |
| 13. | Equality constraints and the Lagrange conditions. |
| 14. | Lagrange relaxation and dual problems. |
| 15. | Summary of the course. |
Preliminary schedule for the exercise classes 2017
Tove's exercise classes will be in English, in the first of the two
scheduled rooms (L51, etc.)
David's exercise classes will be in Swedish, in the second of the two
scheduled rooms (L52, etc.)
| 1. | Linear Programming and the Simplex method |
| 2. | Network flows and some linear algebra. |
| 3. | Duality and complementarity in LP. |
| 4. | Quadratic Programming. |
| 5. | Linear and nonlinear LS problems. |
| 6. | Convex functions. Newtons method. |
| 7. | The KKT optimality conditions. |
| 8. | Lagrange relaxation and dual problems. |
Recommended reading in the course compendium
before and after each lecture.
Recommended exercises in ASKS and EXOPT after each lecture.
Previous exams
Solutions to the exercises from the lecture notes: