TMA947 / MMG621 Nonlinear optimisation Autumn 21
Course PM
Welcome to the course homepage for TMA947/MMG621: Nonlinear Optimisation.
This page contains the program of the course: lectures, exercise sessions and computer labs. Also other information, such as teachers, literature and examination, are included.
The TimeEdit schedule can be found HERE
Hybrid teaching 2021
Lectures
The lectures will be pre-recorded and uploaded to Canvas. Lecture notes are available for all lectures and all notes taken by the lecturer during the lectures will also be available.
During the schedules lecture time slots, the lecturer Emil Gustavsson will be available on Zoom for questions regarding the lecture contents. The first session where you will be able to ask questions regarding the lectures are on Monday 6/9 08-10.
Link to the Zoom lecture is :
https://chalmers.zoom.us/j/68023829747
Password: 432164
Exercise
The exercises will be held both on campus (3 groups) and online in Zoom (2 groups) at the time slots given in the schedule. Links to the Zoom-meetings will be posted here on Canvas.
Mondays:
Euler: Jan
MVH12: Gustav
SB-H6: Kalle
Zoom https://chalmers.zoom.us/j/67222326973 (password: 895704): Caroline
Zoom https://chalmers.zoom.us/j/67862683431 : Mykola
Fridays:
Euler: Jan
MVF31: Gustav
Pascal: Kalle
Zoom https://chalmers.zoom.us/j/67222326973 (password: 895704): Caroline
Zoom https://chalmers.zoom.us/j/67862683431 : Mykola
Consultation hours
Once a week, a consultation session is available. These take place on Wednesdays, 13:15-15:00, either at campus in the room MVH11 or on Zoom (Caroline's room). Feel free to use this time to ask any questions you might have about the course material.
Computer labs
The computer labs should be performed at home or in the computer rooms at Chalmers. Examination will be performed both on campus and in Zoom meetings at the time slots given in the schedule.
You should submit the time slot for when you will attend the computer labs in the choodles below:
Computer Lab 1: https://choodle.portal.chalmers.se/pET3i6OZg95kJ7Do
Computer Lan 2: https://choodle.portal.chalmers.se/lCyehc1Bs9LVRZEc
Exam
The exam will be held on campus. So in order to take the exam you need to physically attend at the Chalmers campus.
Teachers
Emil Gustavsson
Lecturer and course responsible
Business area leader within Machine Learning, Data Science, Optimization
Fraunhofer-Chalmers Centre
Email: emil.gustavsson@fcc.chalmers.se
Ann-Brith Strömberg
Examiner
Professor of Mathematical Optimization, Mathematical Sciences
Email: anstr@chalmers.se
Caroline Granfeldt
Exercise assistant
PhD student, Mathematical Sciences
Email: cargranf@chalmers.se
Gustav Lindwall
Exercise assistant
PhD student, Mathematical Sciences
Email: guslindw@chalmers.se
Nikolay Pochekai
Exercise assistant
PhD student, Mathematical Sciences
Email: pochekai@chalmers.se
Jan Gundelach
Exercise assistant
PhD student, Mathematical Sciences
Email: jangund@chalmers.se
Kalle Thorsager
Exercise assistant
Master's student, Mathematical Sciences
Email: tkalle@student.chalmers.se
Course literature
An Introduction to Continuous Optimization, 3rd Edition
N. Andréasson, A. Evgrafov, E. Gustavsson, Z. Nedělková, M. Patriksson, K.C. Sou, and M. Önnheim
Published by Studentlitteratur 2016 and found in the Cremona book store
Program
Lectures
The program is preliminary. Chapter numbers refer to the course book
| Lecture | Date | Chapter | Content | Videos |
|---|---|---|---|---|
| 0 | 31/8 |
Intro to course, administrative stuff |
Lecture 0 | |
| 1 | 31/8 | 1-2 | Course presentation, introduction to optimization, notations, classification | Lecture 1 |
| 2 | 6/9 | 3 | Convex sets, convex functions, convex problems | Lecture 2 (part 1) Lecture 2 (part 2) Lecture 2 notes (part 1) |
| 3 | 7/9 | 4 | Introduction to optimality conditions |
Lecture 3 (part 1) |
| 4 | 13/9 | 11 | Unconstrained optimization algorithms | |
| 5 | 14/9 | 5.1-5.4 | Optimality conditions | Lecture 5 Lecture 5 notes |
| 6 | 20/9 | 5.5-5.9 | Optimality conditions | Lecture 6 Lecture 6 notes |
| 7 | 21/9 | 6 | Lagrangian duality | Lecture 7 Lecture 7 notes |
| 8 | 27/9 | 7-8 | Introduction to linear programming | Lecture 8 Lecture 8 notes |
| 9 | 28/9 | 9 | Linear programming | Lecture 9 Lecture 9 notes |
| 10 | 4/10 | 10 | Linear programming duality | Lecture 10 Lecture 10 notes |
| 11 | 5/10 | 3, 4.4, 6.4 | Convex optimization | Lecture 11 Lecture 11 notes |
| 12 | 11/10 | --- | Integer programming | Lecture 12 Lecture 12 notes |
| 13 | 12/10 | 12 | Feasible direction methods | Lecture 13 Lecture 13 notes |
| 18/10 | 13 | Constrained optimization | Lecture 14 Lecture 14 notes |
|
| 15 | 19/10 | --- | Summary of the course | Summary Summary notes |
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Exercises
Exercises numbered EX.Y can be found in the exercise sets (can be downloaded below under "Files"). Exercises numbered X.Y can be found in the book (3rd Edition).
For a translation of the exercise numbers for the 2nd Edition of the course book, see the previous year, which can be found here.
| Exercise | Date | Assignment exercises | Exercises | Teacher exercises |
|---|---|---|---|---|
| 1 | 3/9 | E1.2, E1.6-E1.9, 1.1, 1.2, 1.4 | E1.1, E1.4, 1.3 | |
| 2 | 6/9 |
3.1--3.3, 3.5, 3.7, 3.8, 3.10, 3.12-3.14, 3.16-3.19 | 3.4, 3.6, 3.9, 3.11, 3.15 | |
| 3 | 10/9 |
E1.3, E1.5, E1.10, E1.11 | 4.2, 4.3, 4.4, 4.5, 4.12, 4.15, 4.16 | 4.1, 4.4b, 4.6 |
| 4 | 13/9 | 11.3, 11.6, 11.9, 11.11, 11.13 | 4.13, 11.5, 11.7, 11.4 | |
| 5 |
17/9 |
E2.5, E2.7, E2.8, 11.2 | 5.2, E3.3 | |
| 6 | 20/9 |
5.1, 5.3--5.10, 5.12, E3.1, E3.2, E3.4, E3.6 | 5.11, E3.7, 6.4, 6.10 | |
| 7 | 24/9 | E3.5, E3.9, E3.10, E3.11 | E3.8, 6.1--6.3, 6.5--6.9, 6.11--6.12 | 8.1, E4.2 |
| 8 | 27/9 |
8.2--8.5, 8.7, E4.1, E4.4 | 8.6, 9.1, 9.4 | |
| 9 | 1/10 | E4.3, E4.5, E4.7, E4.12 | 9.2--9.3, 9.5--9.6, E4.6, E4.8--E4.11 | E5.1, E5.2, 10.13 |
| 10 | 4/10 |
10.1-10.12,10.14-10.17, E5.4, E5.6, E5.8 | E5.9, E5.13 | |
| 11 | 8/10 | E5.3, E5.5, E5.7, E5.11 | E5.10, E5.12, Section 6.4.2 in the course book | 12.4 |
| 12 | 11/10 |
12.1--12.3, 12.5--12.14 | E6.4, 13.3, 13.5 | |
| 13 | 15/10 | E6.1, E6.2, E6.5, E6.6 | 13.1--13.4, 13.6--13.8 | |
| 14 | 18/10 |
Old exam | ||
| 15 | 22/10 | QA |
Computer labs
Students are supposed to attend one of the sessions for each computer exercise.
Please fill in the following poll regarding Computer Lab 1:
https://choodle.portal.chalmers.se/pET3i6OZg95kJ7Do
| Lab | Date | Content |
|---|---|---|
| 1 | 16/9 | Steepest descent, Newton's method |
| 1 | 23/9 | Steepest descent, Newton's method |
| AMPL | 30/9 | Students test AMPL and can ask teachers for help |
| 2 | 7/10 | Penalty methods, KKT condition |
| 2 | 14/10 | Penalty methods, KKT condition |
Assignment exercises
On the exercise sessions on Fridays you will mark which assignment exercises you have solved that particular week. The teacher will then randomly select a student to present each assignment exercise.
There will be in total 24 assignment exercises.
- 20 marks implies 2 bonus points on the exam
- 12 marks implies 1 bonus point on the exam
Project
How to create groups: Create project group
Project part 1:
The aim of this part is to introduce you to mathematical modelling. The Deadline for handing in the report is 24/9 and it should be done through Canvas
Handing in:
The model assignment is handed in through Canvas in pdf format only!
More information regarding how to hand in the report will be posted soon...
No more than three persons per group; the report must include on the first page the names of each group member, and the e-mail address of at least one group member.
Language:
English
Writing tools:
Prefarably Latex but other word formatting tools are also ok (such as word, etc) as long as the report is readable as pdf.
Student representatives and c ourse evaluation
The student representatives for the course are:
TKTEM rani.alsaberi@hotmail.com Rani Alsaberi
MPDSC basilfabris@protonmail.com Basil Fabris
MPENM veenakannan05@gmail.com Veena Kannan
MPPHS lutomas@student.chalmers.se Tomas Lundberg
UTBYTE papazaka@meng.auth.gr Anastasios Papazakas
Examination
The schedule for exams at MV is found here. Note that this page concerns the GU-version of the course, so instructions about registration for the exam may be different for Chalmers students. The exam and the hours are the same, though.
Dates for exams at Chalmers is found on this link.
Old exams
tentalosning_TMA947_210102.pdf
tentalosning_TMA947_201029.pdf
tentalosning_TMA947_200818.pdf
tentalosning_TMA947_191031.pdf
tentalosning_TMA947_181101.pdf
tentalosning_TMA947_180821.pdf
tentalosning_TMA947_180405.pdf
tentalosning_TMA947_180109.pdf
tentalosning_TMA947_170824.pdf
tentalosning_TMA947_170412.pdf
tentalosning_TMA947_170110.pdf
tentalosning_TMA947_160825.pdf
tentalosning_TMA947_160405.pdf
tentalosnins_TMA947_160112.pdf
tentalosning_TMA947_150827.pdf
tentalosning_TMA947_150414.pdf
tentalosning_TMA947_150113.pdf
tentalosning_TMA947_140422.pdf
tentalosning_TMA947_131217.pdf
tentalosning_TMA947_130829.pdf
tentalosning_TMA947_121217.pdf
tentalosning_TMA947_120410.pdf
tentalosning_TMA947_111212.pdf
tentalosning_TMA947_110426.pdf
tentalosning_TMA947_101213.pdf
tentalosning_TMA947_100406.pdf
tentalosning_TMA947_091214.pdf
tentalosning_TMA947_090827.pdf
tentalosning_TMA947_090414.pdf
tentalosning_TMA947_081215.pdf
tentalosning_TMA947_080828.pdf
tentalosning_TMA947_080325.pdf
tentalosning_TMA947_071217.pdf
Files
Misc:
- Course PM
- List of theorems (3rd Edition of the book)
- List of theorems (2nd Edition of the book)
- Errors in the course book (3rd Edition)
- Index (3rd Edition)
- AMPL intro
Lecture notes:
Lecture videos and written notes from lectures:
Lecture 1 video
Lecture 2 video (part 1)
Lecture 2 video (part 2)
Lecture 3 video (part 1)
Lecture 3 video (part 2)
Lecture 4 video
Lecture 5 video
Lecture 6 video
Lecture 7 video
Lecture 8 video
Lecture 9 video
Lecture 10 video
Lecture 11 video
Lecture 12 video
Lecture 13 video
Lecture 14 video
Summary video
Exercise sets
- Exercise set 1
- Exercise set 2
- Exercise set 3
- Exercise set 4
- Exercise set 5
- Exercise set 6
Computer labs
- Computer exercise 1, PM
- Computer exercise 1, files
- Computer exercise 2, PM
- Computer exercise 2, files
Project
AMPL Installation
Course summary:
| Date | Details | Due |
|---|---|---|