I wanted to inform you that starting next week, I will be taking over the lectures for 3 weeks. Our focus will shift to classical methods, and we will start with linear programming and mathematical modeling.
For the next 3 weeks, classes will be held on Tuesdays as normal from 10:00-12:30 in VR2-262 but not on Thursdays due to scheduling conflicts. Instead, classes will be held on Wednesdays at 8:20-9:50 in VR2-Langholt (the conference room next to the teachers' lounge). After this, the schedule will return to normal with Magnús Þór.
For the upcoming classes, I will be implementing a pedagogical approach known as Team-Based Learning (TBL). Here's a brief explanation of how TBL works, especially the terms iRAT and tRAT:
- iRAT (Individual Readiness Assurance Test): At the start of our TBL sessions, each student will be given a set of questions to answer individually. This is designed to assess your understanding of the pre-class materials.
- tRAT (Team Readiness Assurance Test): After the iRAT, students will then convene with their designated teams to discuss and answer the same set of questions collaboratively. This fosters teamwork and promotes deeper understanding as team members teach and learn from each other.
To ensure you're fully prepared, please install Gurobi and acquire a free academic license before the next session. I've uploaded slides for linear programming under the Classical Methods module. These slides contain examples that will help you anticipate the kinds of questions you'll face during iRAT/tRAT. I also recommend watching the video linked in the slides on the simplex method.
To ensure you're fully prepared for our upcoming class:
- Review the Slides: Familiarize yourself with the content as it will give you a clear picture of the type of questions to anticipate during our session.
- Watch the Video Resources: The slides contain links to specific video resources. I strongly recommend watching these ahead of our class. We won't be dedicating time to them in-session, but they are integral for answering the iRAT and tRAT assignments.
- Install Gurobi for Python: Before our next meeting, please install Gurobi for Python. Additionally, request a free academic license. It's important to note that the request must be done via the university network for a successful license authentication.
- Slides on Linear Programming
- TBL (iRAT/tRAT) on Linear Programming
I'd like to clarify a few key aspects regarding the homework for next Wednesday's class and offer some reminders:
- Gurobi iRAT: Kindly complete the Gurobi iRAT by Wednesday morning. This foundational task will be a springboard for our subsequent class activities.
- tRAT Discussion: We'll start our session by comparing notes on the Gurobi iRAT and diving into the tRAT collaboratively. This should take no more than 5 minutes, so make sure to be on time.
- Mixed Integer Programming (MIP) Slides: We will touch upon the MIP slides briefly during class. You are expected to have reviewed these beforehand. Our class discussion will mainly focus on areas you find challenging or wish to delve deeper into, so please come prepared with questions or topics you'd like to discuss further.
- Elevator Pitch Submission & Session: Before class, please submit your 30-second elevator pitch on a mixed integer programming problem from daily life to the Assignments section on Canvas. During class, you'll present your pitch. Remember, we're not looking for solutions just yet! After all presentations, we will vote, and the winning pitch will be formulated and solved as a class exercise. A quick reminder: your pitch problem should be possible to formulate using linear constraints.
Your active participation and insightful contributions will make this class both fun and productive. I'm eager to hear your pitches and engage in meaningful dialogues.
- Elevator Pitch Session and Voting
- Slides on Mixed Integer Programming
- TBL (iRAT/tRAT) on Gurobi
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Create a GitHub Account: If you don't already have a GitHub account, create one at GitHub. I recommend using your hi-email as your primary or alternate email address.
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Accept the Classroom Invitation: Click on the following invitation link to join the class. This will give you access to the assignment repository.
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Data Submission: By our next class on Tuesday, it's imperative that those of you who volunteered for data acquisition have your data ready and submitted to your GitHub repositories. This will ensure that we have a smooth and productive session.
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Working on the Assignment: After accepting the invitation, you'll be directed to a group repository for the assignment ( please keep the current group formation). This space is for you to submit your work and share your progress with your teammates.
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Writing Models in
README: For the initial stages, I suggest using Markdown (which is natively rendered in GitHub) for writing up the model. As the assignment progresses and for the final report, transition to LaTeX for a more refined presentation. -
Collaborative Discussions: Utilize the "Issues" tab in your repository for initiating and joining discussions about the assignment. It operates much like a forum; you can post challenges, questions, or suggestions and engage with responses from your teammates. This collaborative platform can significantly aid in problem-solving and clarification.
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Preparing for the Model: Take some time to think about the model, its constraints, and how best to approach the assignment. Drawing inspiration from classical Mixed Integer Programming (MIP) models, such as the Traveling Salesman Problem (TSP), can be a great starting point.
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Using Online Resources: The internet offers a plethora of resources for this assignment. I encourage you to explore online resources, tutorials, and forums dedicated to GitHub, TSP, and other relevant MIP models. While ChatGPT is a tool you can use, I cannot stress enough the importance of understanding every aspect of any solution it offers. Do not accept solutions at face value. Also, be aware that if you're uncertain about what you're asking, the prompts can be misleading.
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Course Material: While I will be making slides available tomorrow on non-linear optimization which will be discussed in our next class, I want to emphasize that your primary focus should be on the MIP assignment.
Conclusion: This introductory guide is meant to set the stage for your work on the assignment. GitHub is rich with features that can enhance collaboration, code review, and project management. Dive deep, explore, and make the most of the platform. If you run into issues or need clarification on any aspect, don't hesitate to reach out.
Here's to a productive and enlightening assignment experience!
- Slides on Local Global Optimization
- Slides on Non-Linear Optimization
A few important points for the final assignment:
- Report Submission: Please upload your report to GitHub. It's essential that you do not simply share a Google Sheets URL (because I cannot access them). I need the actual file to be hosted on GitHub for consistency and ease of access.
- GitHub URL Submission: Once your report is uploaded to GitHub, kindly share the direct URL to that file by submitting it to the designated assignment section on Canvas.
- Solution Review: After receiving all reports, I will thoroughly review them. Once this process is complete, I will publish my solution on Canvas for your reference.
- Peer Evaluation Form: Along with the final report, each student must submit a peer evaluation form for their team members. You will evaluate your team members' contributions using the percentage method, and the final grade will be scaled based on these evaluations. It's important to reflect on your teammates' contributions honestly, as this will directly impact their individual grades.
Lastly, it is my hope that, after this module, you feel more comfortable creating linear models. Always remember that the foundation of a good implementation begins with a clear model. As you progress in your endeavors, prioritize modeling first and coding next. This approach not only streamlines the development process but also helps in quickly identifying any potential issues in your code implementation.
Thank you for being an active part of this module. I look forward to seeing your reports and peer evaluations. Best of luck in your future projects.
- Preparation for the final MIP assignment
- Presentation of MIP Assignment Reports
- Submission of Peer Evaluation Forms