cumcm-math-modeling

Automating the CUMCM modeling workflow involves parsing problem statements, auditing data integrity, generating three modeling routes, solving with reproducible code, validating results, and continuously mapping outputs into a final TeX paper. This ensures full traceability from raw data to paper-ready narrative.

Designing modeling routes for mathematical contests requires generating three explicit paths: baseline, primary, and fallback. Each route needs clear justifications, variable mappings, and constraint checks to ensure reproducibility and validate that the problem facts translate directly into solvable equations.

Validating data integrity and units before solving modeling problems requires auditing attachments, time ranges, and risk words extracted during problem parsing. This ensures that all variables and constraints map accurately to the problem facts before generating reproducible solving code.

Python with pandas and matplotlib supports reproducible mathematical modeling by implementing deterministic solving paths, saving tables and figures, and performing baseline and sensitivity checks. These outputs are then continuously integrated into the paper narrative.

Mapping problem facts to variables and constraints requires extracting subquestions and risk words during parsing, then generating step-by-step modeling derivations. This directly translates problem statements into solvable equations and reproducible code paths for paper-ready outputs.

The CUMCM modeling workflow supports continuous integration of figures into a LaTeX paper by updating main.tex with paper-ready content, ensuring the narrative aligns with saved tables, figures, and validated modeling artifacts throughout the solving process.