BSc: Introduction to Optimization.F22
Introduction to Optimization
- Course name: Introduction to Optimization
- Code discipline: CSE???
- Subject area: Data Science and Artificial Intelligence
Short Description
The course outlines the classification and mathematical foundations of optimization methods, and presents algorithms for solving linear and nonlinear optimization. The purpose of the course is to introduce students to methods of linear and convex optimization and their application in solving problems of linear, network, integer, and nonlinear optimization. Course starts with linear programming and moving on to more complex problems. primal and dual simplex methods, network flow algorithms, branch and bound, interior point methods, Newton and quasi-Newton methods, and heuristic methods. Current states, literature, techniques, theories, and methodologies are presented and discussed during the semester.
Prerequisites
Prerequisite subjects
- CSE201: Mathematical Analysis I
- CSE202: Analytical Geometry and Linear Algebra I
- CSE203: Mathematical Analysis II
- CSE204: Analytical Geometry and Linear Algebra II
Prerequisite topics
- Basic programming skills.
- OOP, and software design.
Course Topics
| Section | Topics within the section |
|---|---|
| Linear programming |
|
| Nonlinear programming |
|
| Extensions |
|
Intended Learning Outcomes (ILOs)
What is the main purpose of this course?
The main purpose of this course is to enable a student to go from an idea to implementation of different optimization algorithms to solve problems in different fields of studies like machine and deep learning, etc.
ILOs defined at three levels
We specify the intended learning outcomes at three levels: conceptual knowledge, practical skills, and comprehensive skills.
Level 1: What concepts should a student know/remember/explain?
By the end of the course, the students should be able to ...
- remember the different classifications and mathematical foundations of optimization methods
- remember basic properties of the corresponding mathematical objects
- remember the fundamental concepts, laws, and methods of linear and convex optimization
- distinguish between different types of optimization methods
Level 2: What basic practical skills should a student be able to perform?
By the end of the course, the students should be able to ...
- Funderstand the basic concepts of optimization problems
- evaluate the correctness of problem statements
- explain which algorithm is suitable for solving problems
- evaluate the correctness of results
Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?
By the end of the course, the students should be able to ...
- understand the basic foundations behind optimization problems.
- classify optimization problems.
- choose a proper algorithm to solve optimization problems.
- validate algorithms that students choose to solve optimization problems.
Grading
Course grading range
| Grade | Range | Description of performance |
|---|---|---|
| A. Excellent | 90-100 | - |
| B. Good | 75-89 | - |
| C. Satisfactory | 60-74 | - |
| D. Fail | 0-59 | - |
Course activities and grading breakdown
| Activity Type | Percentage of the overall course grade |
|---|---|
| Midterm | 25 |
| 2 Intermediate tests | 30 (15 for each) |
| Final exam | 30 |
| Final presentation | 15 |
Recommendations for students on how to succeed in the course
- Participation is important. Attending lectures is the key to success in this course.
- Review lecture materials before classes to do well.
- Reading the recommended literature is optional, and will give you a deeper understanding of the material.
Resources, literature and reference materials
Open access resources
- Convex Optimization – Boyd and Vandenberghe. Cambridge University Press.
Closed access resources
- Engineering Optimization: Theory and Practice, by Singiresu S. Rao, John Wiley and Sons.
- Bertsimas, Dimitris, and John Tsitsiklis. Introduction to Linear Optimization. Belmont, MA: Athena Scientific, 1997. ISBN: 9781886529199.
Software and tools used within the course
- MATLAB
- Python
- Excel
Activities and Teaching Methods
| Teaching Techniques | Section 1 | Section 2 | Section 3 |
|---|---|---|---|
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) | 0 | 0 | 0 |
| Project-based learning (students work on a project) | 1 | 1 | 1 |
| Modular learning (facilitated self-study) | 0 | 0 | 0 |
| Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) | 1 | 1 | 1 |
| Contextual learning (activities and tasks are connected to the real world to make it easier for students to relate to them) | 0 | 0 | 0 |
| Business game (learn by playing a game that incorporates the principles of the material covered within the course) | 0 | 0 | 0 |
| Inquiry-based learning | 0 | 0 | 0 |
| Just-in-time teaching | 0 | 0 | 0 |
| Process oriented guided inquiry learning (POGIL) | 0 | 0 | 0 |
| Studio-based learning | 0 | 0 | 0 |
| Universal design for learning | 0 | 0 | 0 |
| Task-based learning | 0 | 0 | 0 |
| Learning Activities | Section 1 | Section 2 | Section 3 |
|---|---|---|---|
| Lectures | 1 | 1 | 1 |
| Interactive Lectures | 1 | 1 | 1 |
| Lab exercises | 1 | 1 | 1 |
| Experiments | 0 | 0 | 0 |
| Modeling | 0 | 0 | 0 |
| Cases studies | 0 | 0 | 0 |
| Development of individual parts of software product code | 0 | 0 | 0 |
| Individual Projects | 1 | 1 | 1 |
| Group projects | 0 | 0 | 0 |
| Flipped classroom | 0 | 0 | 0 |
| Quizzes (written or computer based) | 0 | 0 | 0 |
| Peer Review | 0 | 0 | 0 |
| Discussions | 1 | 1 | 1 |
| Presentations by students | 1 | 1 | 1 |
| Written reports | 0 | 0 | 0 |
| Simulations and role-plays | 0 | 0 | 0 |
| Essays | 0 | 0 | 0 |
| Oral Reports | 0 | 0 | 0 |
Formative Assessment and Course Activities
Ongoing performance assessment
Section 1
Section 2
Section 3
Final assessment
Section 1
Section 2
Section 3
The retake exam
Section 1
- Grading criteria for the final project presentation:
- Problem: short clear statement on what you are solving, and why it’s important.
- User: should be a specific user, can start from generic and then show how you narrowed it.
- Solution: how do you target the problem, what were the initial assumptions/hypotheses
- Elicitation process: interviews, how many people, what questions you asked, what you learnt.
Section 2
- Arriving at MVP: how you chose features, describe prototyping and learning from it, when did you launch, and how it went.
- Team and development process: how it evolved, what were the challenges, what fixes you made to keep progressing.
- Product demo: make it clear what your current product progress is.
Section 3
- Hypothesis-driven development: how did you verify value and understandability of your product, what were the main hypotheses you had to check through MVP.
- Measuring product: what metrics you chose, why, what funnels did you set for yourself, and what was the baseline for your MVP.