Below is a detailed program for the course, including information about the lectures, exercise sessions, assignment exercises, computer exercises, and the project.

Please also see the TimeEdit schedule, which can be found HERE.

Lectures

The program is preliminary. Chapter numbers refer to the course book.

Lecture notes can be found in the schedule below (and also here and under Files).

If anyone is interested, there are also old video recordings from the course round in 2020. The majority of the content is the same, however emphasis and explanations might vary between the lectures and the videos. Note that the order of the lectures have changed, and hence the numbering, between this course round and when the videos were recorded. The videos should not be considered as an official part part of the course material, but are provided for convenience if anyone is interested.

Lecture	Day	Chapter	Content	Lecture notes/slides	Old recorded lectures
1	Mon. 28/8	1-2	Course presentation, introduction to optimization, notations, classification.	Notes Slides	[Video]
2	Tue. 29/8	3.1-3.3	Convexity: convex sets, polytopes, polyherda, cones	Notes	[Video]
3	Thur. 31/8	3.4-3.5, 6.3.1	Convexity: convex functions, convex optimization problems	Notes	[Video]
4	Tue. 5/9	4	Introduction to optimality conditions	Notes	[Video1] [Video2]
5	Thur. 7/9	11.1-11.6	Unconstrained optimization algorithms	Notes	[Video]
6	Mon. 11/9	5.1-5.4	Optimality conditions	Notes	[Video]
7	Tue. 12/9	5.5-5.9	Optimality conditions - KKT	Notes	[Video]
8	Mon. 18/9	6.1-6.3, 6.5-6.7	Lagrangian duality	Notes	[Video]
9	Tue. 19/9	7-8	Introduction to linear programming	Slides	[Video]
10	Mon. 25/9	9	Linear programming	Slides	[Video]
11	Tue. 26/9	10.1-10.6	Linear programming duality	Slides	[Video]
12	Mon. 2/10	13	Constrained optimization	Slides	[Video]
13	Tue. 3/10	12.1-12.5	Feasible direction methods	Slides	[Video]
14	Mon. 9/10	3, 4.4, 6.3-6.4	Convex optimization	Slides	[Video]
15	Tue. 10/10	---	Summary of the course

Exercise sessions and Assignment exercises

Exercises numbered X.Y can be found in the book (3rd Edition).

Exercises numbered EX.Y can be found in the exercise sets. These can be found here and under Files.

Exercises numbered HWX.Y can be found in the file with Assignment exercises. It can be found here and under Files.

For a translation of the exercise numbers for the 2nd Edition of the course book, see the previous year, which can be found here.

Teacher notes from the exercise sessions can be found under Files.

Exercise	Subject	Day	Assignment exercises	Exercises	Teacher exercises
0	Recap of prerequisites	Mon. 28/8
1	Modelling	Fri. 1/9		E1.2, E1.6, E.1.7ac, 1.1	E1.1, E1.4, 1.3
2	Convexity	Mon. 4/9		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	Intro Optimality conditions	Fri. 8/9	HW1.1, HW1.2, 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	Unconstrained optimization	Mon. 11/9		11.3, 11.6, 11.9, 11.11, 11.13	4.13, 11.5, 11.7, 11.4
5	Optimality conditions & KKT	Fri. 15/9	HW2.1, E2.1, E2.5, E2.8		5.2, E3.3
6	KKT & Lagrangian duality	Mon. 18/9		5.1, 5.3--5.10, 5.12, E3.1, E3.2, E3.5, E3.6	5.11, E3.7, 6.4, 6.10
7	Intro to LP	Fri 22/9	HW3.1, E3.4, E3.10, E3.11	E3.8, E3.9 6.1--6.3, 6.5--6.9, 6.11--6.12	8.1, E4.2
8	LP	Mon. 25/9		8.2--8.5, 8.7, E4.1, E4.4	8.6, 9.1, 9.4
9	LP duality	Fri. 29/9	HW4.1, E4.3, E4.7, E4.8	9.2--9.3, 9.5--9.6, E4.5, E4.6, E4.9--E4.12	E5.1, E5.2, 10.13
10	Penalty methods	Mon. 2/10		10.1-10.12,10.14-10.17, E5.6--E5.8	13.3, 13.5, (13.2)
11	Feasible dir. methods & Frank-Wolf	Mon. 9/10	HW5.1, E5.3, E5.4, E5.5	(13.2), 13.4, 13.6--13.8	12.4
12	Convex optimization & subgradient	Fri. 13/10		12.1--12.3, 12.5--12.9, (12.13, 12.14)	E5.9, E5.13
13		Mon. 16/10	HW6.1, E5.11, E6.5, E6.6	E5.10, E5.12, Section 6.4.2 in the course book
14		Fri. 20/10			Old exam

Computer exercise

There are two computer exercises. For for the first computer exercise, there are two different days available for presentation, and for the second computer exercise there are three different days available for presentation. Students are supposed to attend one of the sessions for each computer exercise. To attend a session, you need to sign up for it. The dates for the sessions are given below, and links for signing up for the sessions will be posted below in due time.

- Computer exercise 1: sign up via choodle link https://choodle.portal.chalmers.se/RrrhQLfhN7HWkSdb
- Computer exercise 2: sign up via choodle link https://choodle.portal.chalmers.se/8q8M9ef6arWcU9xj

- Matlab files need for computer exercise 1 can be found under Files.
- Matlab files need for computer exercise 2 can be found under Files.

- Link for digital presentations: Zoom link (password: opt2023_)

Exercise #	Date	Content
1	14/9	Steepest descent, Newton's method
1	21/9	Steepest descent, Newton's method
Project	28/9	Q&A for project and students can test julia/AMPL and can ask teachers for help
2	5/10	Penalty methods, KKT condition
2	12/10	Penalty methods, KKT condition
2	19/10	Penalty methods, KKT condition

Project

The instructions for the project can be found HERE and under Files.

For information about deadline, see the Project Assignment.

Project groups
The project is carried out in groups of 2 - 3 student. You create groups yourself, and instructions for doing so can be found here in the Group Forming Assignment. In that assignment, also note the deadline for forming groups.

Hand-in
The report should be a single PDF, and should be handed in via the Project Assignment. For further information about requirements and expectations on the report, see the instructions.

julia and AMPL

You will have to implement your model in either julia or AMPL. Details are found in the project instructions.

The following files, mentioned in the project instructions, are intended to help you getting started with julia and AMPL, respectively: intro_data.jl and intro_example.jl for julia, and Intro.dat, Intro.mod, and Intro.run for AMPL. You also find the files in Files.

On Thursday the 28/9, there will be a session in the computer rooms where you can ask questions about your model and about julia and AMPL (see above). Further information will be posted in due time.