This repository tracks a Claude LLM-Generated self-study course in Convex Optimization and Numerical Methods
To improve or troubleshoot AI Generated code dedicated to theory or application of the topics discussed in the Syllabus, consider using gitingest.com/dataopsnick/Applied-Math-Bootcamps_Convex-Optimization with "Exclude: *.ipynb & *.pdf"
There is a "Lecture 0" (Syllabus Boilerplate) ready to be modified to instructor preferences
The exercises and solutions have not been reviewed for accuracy. Some effort was made to coordinate the .ipynb notebooks to align with the content of the lecture notes, but no corresponding textbook chapters or verified solutions have been provided. The contents of this repository are provided with no warranty as to correctness, and are intended to be a starting point for self-study rather than a definitive or authoritative reference. All text is LLM generated and cannot be trusted to be factual.
Nicholas Cantrell assumes no liability for the accuracy or correctness of LLM outputs or any damages that may arise from trusting those outputs to be correct.