Language: English | 简体中文 | 繁體中文 | 日本語

Mathematical Modeling Contest Knowledge Base

A comprehensive knowledge base designed for all types of mathematical modeling competitions. Centered on mathematical models + code implementation, with thesis writing as a supplement, covering both Python and MATLAB.

Directory Structure

math-modeling-kb/
├── README.md                    ← You are here
├── requirements.txt             # Python dependencies (tiered: core / algorithm / optional)
├── pyproject.toml               # pytest + ruff + coverage configuration
├── pyrightconfig.json           # Type checking configuration
├── models/                      # Model knowledge system (organized by problem type)
│   ├── optimization/            # Optimization and Control
│   ├── evaluation/              # Evaluation and Decision Making
│   ├── prediction/              # Prediction and Forecasting
│   ├── classification/          # Classification and Discrimination
│   ├── differential/            # Mechanistic Analysis (Differential Equations / Physical Modeling)
│   ├── statistics/              # Statistics and Econometrics
│   ├── simulation/              # Simulation and Modeling
│   └── auxiliary/               # General Auxiliary Methods
├── algorithms/                  # Runnable code
│   ├── python/
│   │   └── tests/               # pytest test suite (67 tests)
│   └── matlab/
├── competitions/                # Competition-specific guides
├── templates/                   # Thesis/Data processing templates
│   ├── python/
│   ├── matlab/
│   └── latex/
├── ai-tools/                    # ★ AI-Assisted Toolset (New in 2026)
│   ├── prompts/                 # Stage-specific prompt templates
│   ├── skills/                  # Claude Code Skill configurations
│   └── workflow/                # AI-assisted full workflow
├── data/                        # Sample datasets (CSV)
├── .github/                     # CI/CD + Issue/PR templates
│   └── workflows/               # GitHub Actions (pytest + link check + lint)
├── tools/                       # Utility scripts
├── papers/                      # Excellent paper analysis notes
├── notes/                       # Learning notes
└── scripts/                     # Helper scripts

Competition-Method Quick Reference Matrix

Problem Feature	Corresponding Model Directory	Priority Reading Order
Need optimal solution	models/optimization/	linear_programming → multi_objective → intelligent_algorithm
Need ranking/scoring	models/evaluation/	ahp → topsis → entropy_weight
Predict future trends	models/prediction/	grey_prediction → time_series → deep_learning_prediction
Classification/Clustering	models/classification/	clustering → svm → xgboost_lightgbm
Physics/Engineering modeling	models/differential/	ode_basics → pde_numerical → dimensional_analysis
Policy evaluation/Causal inference	models/statistics/	did → panel_data → synthetic_control
Uncertainty/Stochastic problems	models/simulation/	monte_carlo → bootstrap
Text data processing	models/classification/	nlp_basics → clustering
Financial data	models/optimization/ + models/statistics/	portfolio_optimization → time_series
Need AI assistance	ai-tools/	prompts/stage-prompts → skills/claude-skill

AI-Assisted Tools (New in 2026)

Starting from MCM/ICM 2026, the use of AI tools is explicitly permitted. This repository provides:

• Stage-Specific Prompt Templates — Complete prompts from topic selection to thesis writing
• De-AI-ification Writing Techniques — 40+ techniques to make AI-generated text sound human-written
• Claude Code Skill — Automatically activates mathematical modeling expertise after installation
• AI-Assisted Full Workflow — 96-hour timeline + AI checklist

Covered Competitions

Tier	Competition	Time	Core Problem Types
1	MCM/ICM	Late January	A Continuous B Discrete C Data Insights D Operations Research E Environmental F Policy
1	CUMCM (China National)	Mid-September	A Engineering Mechanism B Optimization Decision C Data Analysis
1	Huawei Cup (Graduate)	Late September	Graduate level, six major directions
2	MathorCup	Mid-April	Real-world corporate problems (set by Alibaba, Didi, etc.)
2	Statistics Modeling Contest	May	Thematic, focuses on econometrics DID/RDD/Panel Data
2	Electrical Engineering Cup	Late May	Power/Energy direction, free
2	Shenzhen Cup	July-August	Open-ended problems, high difficulty
2	APMCM (Asia-Pacific)	November	Full English, MCM simulation
S	Teddy Cup	April	Big Data/Data Mining
S	Greater Bay Area Cup	November	Financial Mathematical Modeling

See detailed competition-specific guides under the competitions/ directory.

Usage Guide

Quick Start

1. Install dependencies → pip install -r requirements.txt (core deps: numpy, scipy, pandas, scikit-learn)
2. Determine the problem type → Refer to the quick reference matrix above to locate the model directory
3. Read the model documentation → models/<category>/<model-name>.md to understand principles and applicable scenarios
4. Run code examples → algorithms/{python,matlab}/ contains independently runnable implementations
5. Apply thesis templates → templates/latex/ contains MCM/CUMCM thesis templates

Model Document Structure

Each .md file contains:

1. Model principles (mathematical formulas + intuitive explanations)
2. Applicable scenarios (competition problem types + typical problem examples)
3. Modeling steps (step-by-step instructions)
4. Code implementation (Python + MATLAB key snippets)
5. Important notes (pros and cons, common pitfalls, parameter tuning suggestions)
6. Thesis presentation suggestions (equation numbering, chart types, conclusion writing)
7. References

Code Usage

# Install dependencies
pip install -r requirements.txt

# Run a single algorithm example
python algorithms/python/grey_model.py

# MATLAB example (run in MATLAB)
run('algorithms/matlab/grey_model.m')

Each file includes if __name__ == '__main__': (Python) or built-in example data (MATLAB), and can be run directly for verification.

Development

# Run all tests (67 tests covering core algorithms)
pytest algorithms/python/tests/ -v

# Run tests with coverage report
pip install pytest-cov
pytest algorithms/python/tests/ --cov=algorithms/python --cov-report=term-missing

# Lint check
pip install ruff
ruff check algorithms/python/

CI (GitHub Actions) runs pytest + ruff on every push and PR.

Model Classification Overview

Optimization and Control — models/optimization/

Linear Programming · Integer Programming · Nonlinear Programming · Dynamic Programming · Multi-Objective Optimization · Graph Theory Optimization · Queuing Theory · Game Theory · Portfolio Optimization · Scheduling Optimization

Evaluation and Decision Making — models/evaluation/

AHP · TOPSIS · Fuzzy Comprehensive Evaluation · Grey Relational Analysis · Entropy Weight Method · PCA · Data Envelopment Analysis (DEA) · Rank Sum Ratio · Factor Analysis · Combined Evaluation

Prediction and Forecasting — models/prediction/

Grey Prediction · Time Series (ARIMA/SARIMA) · Regression Analysis · Markov Prediction · Neural Network Prediction · Deep Learning Prediction (LSTM/GRU) · Interpolation and Fitting · Combined Forecasting

Classification and Discrimination — models/classification/

SVM · Decision Tree/Random Forest · XGBoost/LightGBM · Clustering (K-means/Hierarchical/DBSCAN) · Fisher Discriminant · Logistic Regression · Ensemble Learning (Stacking) · NLP Fundamentals · Bayesian Methods

Mechanistic Analysis — models/differential/

ODE Modeling · PDE Numerical Solution · Stability Analysis · Population/Ecology Models · Epidemic Models (SIR/SEIR) · Diffusion Models · Dimensional Analysis · Kinematics/Dynamics

Statistics and Econometrics — models/statistics/

Panel Data Analysis · Difference-in-Differences (DID) · Regression Discontinuity (RDD) · Instrumental Variables (IV) · Mediation/Moderation Effects · Structural Equation Modeling (SEM) · Spatial Econometrics · Survival Analysis · Synthetic Control Method

Simulation and Modeling — models/simulation/

Monte Carlo Simulation · System Dynamics · Cellular Automata · Agent-Based Modeling · Bootstrap

General Auxiliary — models/auxiliary/

Sensitivity Analysis · Data Preprocessing · Feature Engineering · Correlation Analysis · Robustness Analysis · Uncertainty Analysis

---

English | 简体中文 | 繁體中文 | 日本語