TMA947 / MMG621 Nonlinear optimisation Autumn 21

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)
Lecture 3 (part 2)
Lecture 3 notes (part 1)
Lecture 3 notes (part 2

4 13/9 11 Unconstrained optimization algorithms

Lecture 4
Lecture 4 notes

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

14

18/10 13 Constrained optimization Lecture 14
Lecture 14 notes
15 19/10 --- Summary of the course Summary
Summary notes

 L

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

tenta_TMA947_210817.pdf

tenta_TMA947_210102.pdf

tentalosning_TMA947_210102.pdf

tenta_TMA947_201029.pdf

tentalosning_TMA947_201029.pdf

tenta_TMA947_200818.pdf

tentalosning_TMA947_200818.pdf

tenta_TMA947_191031.pdf

tentalosning_TMA947_191031.pdf

tenta_TMA947_181101.pdf

tentalosning_TMA947_181101.pdf

tenta_TMA947_180821.pdf 

tentalosning_TMA947_180821.pdf

tenta_TMA947_180405.pdf

tentalosning_TMA947_180405.pdf

tenta_TMA947_180109.pdf

tentalosning_TMA947_180109.pdf

tenta_TMA947_170824.pdf

tentalosning_TMA947_170824.pdf

tenta_TMA947_170412.pdf

tentalosning_TMA947_170412.pdf

tenta_TMA947_170110.pdf

tentalosning_TMA947_170110.pdf

tenta_TMA947_160825.pdf

tentalosning_TMA947_160825.pdf

tenta_TMA947_160405.pdf

tentalosning_TMA947_160405.pdf

tenta_TMA947_160112.pdf

tentalosnins_TMA947_160112.pdf

tenta_TMA947_150827.pdf

tentalosning_TMA947_150827.pdf

tenta_TMA947_150414.pdf

tentalosning_TMA947_150414.pdf

tenta_TMA947_150113.pdf

tentalosning_TMA947_150113.pdf

tenta_TMA947_140422.pdf

tentalosning_TMA947_140422.pdf

tenta_TMA947_131217.pdf

tentalosning_TMA947_131217.pdf

tenta_TMA947_130829.pdf

tentalosning_TMA947_130829.pdf

tenta_TMA947_121217.pdf

tentalosning_TMA947_121217.pdf

tenta_TMA947_120410.pdf

tentalosning_TMA947_120410.pdf

tenta_TMA947_111212.pdf

tentalosning_TMA947_111212.pdf

tenta_TMA947_110426.pdf

tentalosning_TMA947_110426.pdf

tenta_TMA947_101213.pdf

tentalosning_TMA947_101213.pdf

tenta_TMA947_100406.pdf

tentalosning_TMA947_100406.pdf

tenta_TMA947_091214.pdf

tentalosning_TMA947_091214.pdf

tenta_TMA947_090827.pdf

tentalosning_TMA947_090827.pdf

tenta_TMA947_090414.pdf

tentalosning_TMA947_090414.pdf

tenta_TMA947_081215.pdf

tentalosning_TMA947_081215.pdf

tenta_TMA947_080828.pdf

tentalosning_TMA947_080828.pdf

tenta_TMA947_080325.pdf

tentalosning_TMA947_080325.pdf

tenta_TMA947_071217.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:

lecture1.pdf

lecture2.pdf

lecture3.pdf

lecture4.pdf

lecture5.pdf

lecture6.pdf

lecture7.pdf

lecture8.pdf

lecture9.pdf

lecture10.pdf

lecture11.pdf

lecture12.pdf

lecture13.pdf

lecture14.pdf

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

- Project part 1, PM

 

AMPL Installation

Download AMPL

 

 

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Course summary:

Date Details Due