KTH /
Engineering Science
/
Mathematics
/
Optimization and Systems Theory
SF2822 Applied Nonlinear Optimization, 7.5hp, 2012/2013
Instructor and examiner
Anders Forsgren
(andersf@kth.se),
room 3703, Lindstedtsv. 25, tel 790 71 27.
Office hours: Monday 11-12.
(Or by agreement.)
Exercise leader and project leader
Tove Odland
(odland@kth.se),
room 3727,
Lindstedtsv. 25, tel. 790 75 07.
Office hours: By agreement.
Course material
-
Linear and Nonlinear Optimization, second edition,
by I. Griva, S. G. Nash och A. Sofer, SIAM, 2009.
Information on how to order the book can be
found here.
- Exercises in applied nonlinear optimization, 2012/2013.
For sale at the department's student expedition, Lindstedtsv. 25.
- Supplementary course material in applied nonlinear
optimization, 2012/2013.
For sale at the department's student expedition, Lindstedtsv. 25.
- Lecture notes in applied nonlinear optimization,
2012/2013. Can be downloaded from this web page, see the
schedule below. Also for sale at
the department's student expedition, Lindstedtsv. 25.
- GAMS, A user's guide.
For sale at the department's student expedition, Lindstedtsv. 25.
Additional GAMS documentation can be found here.
- GAMS. GAMS is installed in computer rooms for F and
MMT. It may also be downloaded from the
web for use
on a personal computer.
- Two project assignments that are handed out during the
course, March 27 and April 22 respectively.
Additional notes that may be handed out during the course are also included.
Course goals
After completed course, the student should be able to:
-
explain fundamental concepts of nonlinear programming;
-
explain how fundamental methods for nonlinear programming work;
-
illustrate how these methods work by solving small problems by hand
calculations;
-
starting from a suitably modified real problem, formulate a nonlinear
program; make a model in a modeling
language and solve the problem;
-
analyze the solutions of the optimization problem solved, and present
the analysis in writing as well as orally;
-
interact with other students when modeling and analyzing the
optimization problems.
Examination
The examination is in two parts, projects and final exam.
To pass the course, the following is required:
-
Pass project assignment 1, with presence at compulsory presentation
lecture on Monday April 22, and precence at the following dicussion
session.
-
Pass project assignment 2, with presence at compulsory presentation
lecture on Wednesday May 8, and precence at the following dicussion
session.
-
Pass final exam.
Course registration
Due to the project based nature of this course, students must register
no later than March 25. Registration lists will be circulated at the
initial lectures. Each student must give an e-mail address where
he/she can be reached.
Project assignments
The project assignments are performed in groups, where the instructor determines the division of
groups. This division is changed between the two
assignments. Assignment 1 is carried out using the modeling language
GAMS. For project 2, there is a choice between a modeling assignment,
to be carried out using GAMS, or a method assignment, to be carried
out using Matlab. The project assignments must be carried out
during the duration of the course and completed by the above mentioned
presentation lectures. Presence at the presentation lectures is
compulsory. For passing the projects, the following requirements must
be fulfilled:
-
At the beginning of the presentation lecture, each group must hand in
a well-written report which describes the exercise and the group's
suggestion for solving the exercise. Suitable word processor should be
used. The report should be on a level suitable for another participant
in the course who is not familiar with the group's specific problem.
-
When handing in the report, each student should append an individual
sheet with a brief self-assessment of his/her contribution to the project
work, quantitatively as well as qualitatively.
-
At the presentation lecture, all assignments will be presented and
discussed. Each student is expected to be able to present the
assignment of his/her group. In particular, each student is expected
to take part in the discussion. The presentation and discussion should
be on a level such that students having had the same assignment can
discuss, and students not having had the same assignment can
understand the issues that have arisen and how they have been solved.
- Each group should make an appointment for a discussion session
with the course leaders. There is no presentation at this session, but
these sessions are in the form of a 20 minutes question session, one
group at a time. There will be times available the days after the
presentation session. One week prior to the presentation lecture, a
list of available times for discussion sessions will be made available
at Doodle, reachable from the course home page. Each group should sign
up for a discussion session prior to the presentation lecture.
- Each participant in the course must contribute to the work of the
group. Each group must solve their task independently. Discussion
between the groups is encouraged, but each group must individually
solve the assignments. It is not allowed to use solutions
made by others in any form. If these rules are violated, disciplinary
actions in accordance with the KTH regulations will be taken.
Each project assignment is awarded a grade which is either fail or
pass with grading E, D, C, B and A. Here, the mathematical treatment
of the problem as well as the report and the oral presentation or
discussion is taken into account. Normally, the same grade is given to
all members of a group.
Final exam
The final exam consists of five exercises and gives a maximum of 50
points. At the exam, the grades F, Fx, E, D, C, B and A are awarded.
For a passing grade, normally at least 22 points are required. At the
exam, in addidion to writing material, no other material is allowed at the
exam. Normally, the grade limits are given by E (22-24), D (25-30), C
(31-36), B (37-42) and A (43-50).
The grade Fx is normally given for 20 or 21 points on the final
exam. An Fx grade may be converted to an E grade by a successful
completion of two supplementary exercises, that the student must
complete independently. One exercise among the theory exercises handed
out during the course, and one exercise which is similar to one
exercise of the exam. These exercises are selected by the instructor,
individually for each student. Solutions have to be handed in to the
instructor and also explained orally within three weeks of the date of
notification of grades.
The final exam is given Monday May 20, 8.00-13.00, in rooms D33 and D34.
Final grade
By identitying A=7, B=6, C=5, D=4, E=3, the final grade is given as
round( (grade on proj 1) + (grade on proj 2) + 2 *
(grade on final exam) ) / 4),
where the rounding is made to nearest larger integer in case of a tie.
Preliminary schedule
"L" means lecture, "E" means exercise session, "P" means project sesstion.
| Type | Day | Date | Time | Room | Subject
|
|---|
| L1. | Mon | Mar 18 | 13-15 | Q15
| Introduction. Nonlinear programming models.
(pdf)
|
| L2. | Wed | Mar 20 | 13-15 | Q22
| Optimality conditions for linearly constrained problems.
(pdf)
|
| L3. | Fri | Mar 22 | 15-17 | D32
| Optimality conditions for nonlinearly constrained problems.
(pdf)
|
| P1. | Mon | Mar 25 | 13-15 | B24
| Introduction to GAMS.
|
| P2. | Wed | Mar 27 | 13-15 |
Orange
| GAMS excercise session.
|
| E1. | Thu | Mar 28 | 8-10 | D32
| Optimality conditions.
|
| L4. | Mon | Apr 8 | 13-15 | D35
| Unconstrained optimization.
(pdf)
|
| L5. | Wed | Apr 10 | 13-15 | D35
| Unconstrained optimization, cont.
(pdf)
|
| E2. | Thu | Apr 11 | 13-15 | D35
| Unconstrained optimization.
|
| L6. | Mon | Apr 15 | 15-17 | D42
| Equality-constrained quadratic programming.
(pdf)
|
| L7. | Wed | Apr 17 | 15-17 | D42
| Inequality-constrained quadratic programming.
(pdf)
|
| E3. | Thu | Apr 18 | 13-15 | B21
| Equality-constrained quadratic programming.
|
| P3. | Mon | Apr 22 | 13-15 | D42
| Presentation of project assignment 1.
|
| L8. | Wed | Apr 24 | 15-17 | D42
| Inequality-constrained quadratic programming, cont.
(pdf)
|
| E4. | Thu | Apr 25 | 13-15 | D35
| Inequality-constrained quadratic programming.
|
| L9. | Mon | Apr 29 | 13-15 | D42
| Sequential quadratic programming.
(pdf)
|
| E5. | Tue | Apr 30 | 15-17 | D42
| Sequential quadratic programming.
|
| L10. | Fri | May 3 | 15-17 | D32
| Interior methods for nonlinear programming.
(pdf)
|
| L11. | Mon | May 6 | 13-15 | D42
| Interior methods for nonlinear programming,
cont. Semidefinite programming.
(pdf)
|
E6. | Tue | May 7 | 15-17 | D32
| Interior methods for nonlinear programming.
|
| P4. | Wed | May 8 | 10-12 | D42
| Presentation of project assignment 2.
|
| L12. | Mon | May 13 | 13-15 | Q15
| Semidefinite programming, cont.
|
| E7. | Wed | May 15 | 13-15 | D32
| Semidefinite programming.
|
| E8. | Fri | May 17 | 15-17 | D32
| Selected topics.
|
Overview of course contents
- Unconstrained optimization
Fundamental theory, in particular optimality conditions.
Linesearch algorithms, steepest descent, Newton's method.
Conjugate directions and the conjugate gradient method.
Quasi-Newton methods.
(Chapters 11, 12.1-12.3 and 13.1-13.2 in Griva, Nash and Sofer.)
- Constrained nonlinear optimization
Fundamental theory, optimality conditions, Lagrange multipliers and sensitivity analysis.
Quadratic programming.
Primal methods, in particular active-set methods.
Penalty and barrier methods, in particular primal-dual interior methods.
Dulal methods, local duality, separable problems.
Lagrange methods, in particular sequential quadratic programming.
(Chapters 3, 14.1-14.7, 14.8.1, 15.1-15.5, 16.1-16.3 and 16.7 in
Griva, Nash and Sofer.)
- Semidefinite programming
Fundamental theory.
(Chapter 16.8 in Griva, Nash and Sofer. Separate article in the
supplementary course material. Fundamental concepts only.)
Welcome to the course!
Course web page:
http://www.math.kth.se/optsyst/grundutbildning/kurser/SF2822/.
|