Competition Problem Analysis and Modeling Design
Reference for Mathematical Modeling Specifications
For domain judgment, read the sections "Problem Understanding and Sub-problem Identification", "Hypothesis and Model Establishment", and "Quick Check for Problem Type Error Prevention" in
../_references/math_modeling_norms.md
. This file only serves as a specification knowledge base and does not replace the analysis report structure of this phase.
Mandatory Deliverables
Create or update in the
subdirectory of the current working directory:
reports/ANALYSIS_MODELING_REPORT.md
- Competition problem analysis, sub-problem decomposition, data and attachment understanding, evaluation criteria, key ambiguities, and hypothesis pre-check.
- Variables, symbols, model assumptions, objective functions, constraint conditions, solution algorithms, implementation specifications for each sub-problem, and code phase task list
Do not write the main body of the paper in this phase, do not generate the final
, and do not advance the chart typesetting task to this stage.
Workflow
Step 1: Sub-problem Decomposition
Only treat the top-level problems with clear numbering in the problem statement as sub-problems, such as "Problem 1/2/3" or "Problem 1/2/3". Do not mistake sub-questions, background descriptions, data explanations, or submission requirements as independent sub-problems.
Clearly write at the beginning of
ANALYSIS_MODELING_REPORT.md
:
Adjust the number of problems dynamically according to the topic
This competition problem has a total of X sub-problems.
Each sub-problem should include explanations of:
- Input data and known conditions.
- Decision variables or prediction objects.
- Objective functions or evaluation indicators.
- Constraint conditions.
- Dependencies with other sub-problems.
- Which graphs or tables to draw to display results.
Step 2: Hypothesis Sensitivity Pre-check
List key ambiguities and do not rush to finalize the model. Provide at least two interpretations for ambiguities that affect results, and judge the selection through simple verification or logical progression.
Must include the following in
ANALYSIS_MODELING_REPORT.md
:
markdown
## Hypothesis Sensitivity Pre-check
### Ambiguous Expressions and Interpretations
...
### Quick Verification and Progressive Check
...
### Final Adopted Interpretations
...
### Plotted Graphs and Comparison Tables
If a certain assumption makes the new conditions of subsequent problems have no marginal effect, go back to adjust the interpretation.
Step 3: Data Understanding and Modeling Route
Conduct data understanding for each attachment:
- Row and column scale and field explanations.
- Missing values, anomalies, duplicates, and inconsistent units.
- Variables that can be directly used for modeling.
- Indicators that need to be derived.
Then provide the overall route:
text
Problem Statement -> Data Cleaning(EDA) -> Sub-problem 1 Model -> Sub-problem 2 Model -> 。。。。 -> Result Verification -> Paper Presentation
Step 4: Modeling Report
Write a complete solution in
ANALYSIS_MODELING_REPORT.md
that can be handed over to the code phase for implementation.
Each sub-problem must include at least:
- Problem objectives.
- Symbols and variables.
- Model assumptions.
- Objective functions.
- Constraint conditions.
- Solution methods.
- Input and output.
- Key points for code implementation.
- Result verification methods.
Formulas must be clear enough for direct implementation in the code phase. Algorithm descriptions should include core steps, stop conditions, complexity or feasibility explanations.
Recommended structure:
markdown
# Modeling Report
## 1. Overall Modeling Framework
## 2. Data Processing Scheme
## 3. Symbol Explanation
## 4. Sub-problem 1 Model
## 5. Sub-problem 2 Model
## 6. Sub-problem 3 Model
....
## 7. Sensitivity Analysis and Verification Scheme
## 8. Code Implementation Task List
If the number of sub-problems is not 3, adjust the chapters according to the actual problem statement, do not force-fit.
Step 5: Interface for the Code Phase
Write a "Code Implementation Task List" at the end of
ANALYSIS_MODELING_REPORT.md
in the following format:
markdown
## Code Implementation Task List
| --- | --- | --- | --- | --- |
| Sub-problem 1 | ... | ... | ... | ... |
| Sub-problem 2 | ... | ... | ... | ... |
Quality Requirements
- All conclusions can be traced back to the problem statement or data.
- Do not fabricate data fields or values.
- Do not skip ambiguity analysis.
- The model should have both mathematical expressions and be implementable by code.
- If data is insufficient or the problem statement is unclear, clearly record risks and alternative solutions.