ECE1505: Convex Optimization

Department of Electrical and Computer Engineering
University of Toronto

Project Information

Project requirement: The course project is an integral part of this course. The purpose of the project is for students to apply the techniques covered in class in an area of their technical interests. It is essential that students start to think about the course project and to discuss project ideas with their supervisor or the course instructor as early as possible. Students may work in a group of two (but no more than two). Individual projects are acceptable.

One component of the project should be a review of the state-of-the-art in using optimization approach to solve an engineering problem. Most projects should have a significant numerical component (but purely theoretical papers are possible as well). The students are expected to say something interesting about the optimization problem beyond the existing literature, e.g. a comparison of different approaches in two different papers, a new problem formulation, theoretical development, numerical technique, or new insight into the problem. You will be graded on the clarity of the problem formulation and the technical depth of the solution. Please be sure to provide a concise survey of the literature and clearly state any novelty in problem formulation and/or solution techniques.

Use of GenAI: If you decide to use large-language models (LLMs) for literature search, problem formulation, or code generation, you must document: (i) how you use AI; and (ii) your own intellectual contributions beyond the output of the AI models. Please include your critique of the AI output and your judgement of whether the AI output is correct. You must state how you have independently verified the references, understood the existing techniques, and checked the correctness of any AI generated mathematical derivations and computer code. You may use AI to help generate text for part of the project report, but you must document your own contributions, and you are ultimately responsible for the correctness and the logic of the final project report.

Project Proposal: Each group must submit a one-page project proposal. The proposal should have a title, a list of authors, a brief description of the problem, a brief survey of existing approaches, and a list of references. The proposal is not graded.

Project Report: Each group must produce an IEEE conference style, maximum 5-page, double-column project report, including references. The report must contain a title, a list of authors, an abstract, an introduction, a clear description of the optimization problem, the methodology, and the numerical results and their interpretations. The report should put your work in the context of the existing literature and clarify the novelty of your approach. The report should contain a complete list of references. If you use AI in the process of completing the project, please use one additional page to document: (i) how you use AI; and (ii) your own intellectual contributions beyond the output of the AI models. This additional page cannot contain AI-generated text.

Grades: The project counts for 20% of the course grade. The project is graded based on both the presentation and the technical aspects, with particular emphasis on your own intellectual contributions beyond the output of LLMs. If time permits, there may be an oral presentation component, with details to be announced.