A repository containing Stanford ACM-ICPC team notebook and lecture slides for competitive programming.
Stanford ACM-ICPC is a repository of educational materials and code resources for competitive programming. It includes a team notebook with algorithms and data structures in multiple languages, along with lecture slides from Stanford's CS 97SI course. The project helps students and programmers prepare for ACM-ICPC contests by providing proven code and structured learning content.
University students, competitive programmers, and educators involved in ACM-ICPC or similar programming contests. It's particularly useful for Stanford team members and those taking CS 97SI.
It offers a curated, battle-tested collection of algorithms and educational slides from a top competitive programming institution. The ability to generate notebooks in multiple formats with customizable syntax highlighting adds flexibility for different use cases.
Stanford ACM-ICPC related materials
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The notebook is compiled from codes by previous Stanford team members and coaches, ensuring battle-tested algorithms relevant for ACM-ICPC contests.
Complete slides from CS 97SI provide a structured curriculum for learning competitive programming, directly from Stanford's course materials.
Python scripts allow generating the notebook in PDF or HTML formats, as described in the README, offering flexibility for different use cases.
Automatic highlighting for C/C++/Java/Python in generated outputs enhances code readability, with support for common file extensions.
LaTeX and HTML templates can be modified to change color schemes or add languages, though it requires editing scripts, as noted in the README.
Generating outputs requires installing Python and external tools like latexmk or enscript, which can be cumbersome and a barrier for quick setup.
To alter color schemes or add languages, users must edit both generation scripts and templates, as admitted in the README, making it less user-friendly.
Since the code is from past team members, it may not include the latest algorithms or optimizations for current contest problems, risking staleness.
Focused on Stanford's specific needs, the notebook might not cover all algorithms or be as comprehensive as community-driven resources like Codeforces.