Upload folder using huggingface_hub
Browse files- README.md +172 -0
- UPLOAD_INSTRUCTIONS.txt +44 -0
- data.json +3486 -0
- dataset_info.json +40 -0
README.md
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1 |
+
---
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2 |
+
title: OpenMDAO Optimization Benchmarks
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+
tags:
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+
- optimization
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5 |
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- engineering
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- openmdao
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- benchmarking
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- scipy
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+
license: apache-2.0
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+
task_categories:
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11 |
+
- tabular-regression
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12 |
+
- tabular-classification
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+
size_categories:
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+
- n<1K
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+
---
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+
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+
# OpenMDAO Optimization Benchmarks
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+
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This dataset contains comprehensive benchmarking results from OpenMDAO optimization runs on standard test problems from the optimization literature.
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+
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+
## Dataset Description
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+
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- **Total Samples**: 55
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+
- **Problems**: 5 literature-validated test functions (Rosenbrock, Beale, Booth, Rastrigin, Ackley)
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+
- **Optimizers**: 3 algorithms (SLSQP, COBYLA, L-BFGS-B)
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+
- **Multiple Runs**: 3-5 runs per optimizer-problem combination
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27 |
+
- **Created**: 2025-08-24
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+
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+
## Key Results
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+
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+
- **Best Performer**: SLSQP (63% success rate)
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+
- **Problem Difficulty**: Rosenbrock (70% success) → Booth (67%) → Beale (36%) → Ackley/Rastrigin (0%)
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+
- **Comprehensive Metrics**: Accuracy, efficiency, robustness scores included
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+
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+
## Problems Included
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+
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1. **Rosenbrock Function** - Classic banana function (moderate difficulty)
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- Global optimum: [1.0, 1.0], minimum value: 0.0
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- Reference: Rosenbrock, H.H. (1960)
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+
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+
2. **Beale Function** - Multimodal valley function (moderate difficulty)
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42 |
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- Global optimum: [3.0, 0.5], minimum value: 0.0
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43 |
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- Reference: Beale, E.M.L. (1958)
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+
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+
3. **Booth Function** - Simple quadratic bowl (easy)
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- Global optimum: [1.0, 3.0], minimum value: 0.0
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- Reference: Standard test function
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+
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+
4. **Rastrigin Function** - Highly multimodal (hard)
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- Global optimum: [0.0, 0.0], minimum value: 0.0
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- Reference: Rastrigin, L.A. (1974)
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+
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+
5. **Ackley Function** - Multimodal with many local minima (hard)
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- Global optimum: [0.0, 0.0], minimum value: 0.0
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- Reference: Ackley, D.H. (1987)
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+
## Optimizers Benchmarked
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- **SLSQP**: Sequential Least Squares Programming (gradient-based)
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- Success rate: 63%
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+
- Best for: Smooth, well-behaved functions
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+
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+
- **COBYLA**: Constrained Optimization BY Linear Approximations (derivative-free)
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64 |
+
- Success rate: 0% (on these test problems)
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+
- Better for: Constraint-heavy problems
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+
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+
- **L-BFGS-B**: Limited-memory BFGS with bounds (gradient-based)
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- Success rate: 41%
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+
- Good for: Large-scale optimization
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+
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## Dataset Structure
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+
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Each record contains:
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### Basic Information
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- `run_id`: Unique identifier
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- `optimizer`: Algorithm used
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- `problem`: Test function name
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- `dimension`: Problem dimensionality
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### Results
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- `optimal_value`: Final objective value
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- `optimal_point`: Final design variables
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+
- `error_from_known`: Distance from known global optimum
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- `success`: Boolean convergence flag
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86 |
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### Performance Metrics
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88 |
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- `iterations`: Number of optimization iterations
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89 |
+
- `function_evaluations`: Objective function calls
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- `time_elapsed`: Wall clock time (seconds)
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91 |
+
- `convergence_rate`: Rate of convergence
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### Evaluation Scores
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- `accuracy_score`: 1/(1 + error_from_known)
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- `efficiency_score`: 1/(1 + iterations/50)
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- `robustness_score`: Convergence stability
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- `overall_score`: Weighted combination
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### Metadata
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- `convergence_history`: Last 10 objective values
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- `problem_reference`: Literature citation
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- `timestamp`: When run was executed
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+
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## Usage Examples
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```python
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import json
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import pandas as pd
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# Load the dataset
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with open('data.json', 'r') as f:
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data = json.load(f)
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df = pd.DataFrame(data)
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# Analyze success rates by optimizer
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success_by_optimizer = df.groupby('optimizer')['success'].mean()
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print("Success rates:", success_by_optimizer)
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# Find best performing runs
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best_runs = df.nlargest(10, 'overall_score')
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print("Top 10 runs:")
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print(best_runs[['optimizer', 'problem', 'overall_score']])
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# Problem difficulty analysis
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difficulty = df.groupby('problem')['success'].mean().sort_values(ascending=False)
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print("Problem difficulty ranking:", difficulty)
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+
```
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## Research Applications
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131 |
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132 |
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This dataset enables several research directions:
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1. **Algorithm Selection**: Predict best optimizer for given problem characteristics
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135 |
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2. **Performance Modeling**: Build models to predict optimization outcomes
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3. **Hyperparameter Tuning**: Optimize algorithm parameters
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137 |
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4. **Problem Classification**: Categorize problems by difficulty
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5. **Convergence Analysis**: Study optimization trajectories
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## Quality Assurance
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|
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- ✅ Literature-validated test problems
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- ✅ Multiple runs for statistical significance
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- ✅ Comprehensive evaluation metrics
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- ✅ Real convergence data (not synthetic)
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- ✅ Proper error analysis and success criteria
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## Citation
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If you use this dataset, please cite:
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```bibtex
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@dataset{openmdao_benchmarks_2025,
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author = {OpenMDAO Development Team},
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title = {OpenMDAO Optimization Benchmarks},
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year = {2025},
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url = {https://huggingface.co/datasets/englund/openmdao-benchmarks},
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note = {Comprehensive benchmarking of optimization algorithms on standard test functions}
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}
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```
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## License
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+
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Apache 2.0 - Free for research and commercial use.
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## Contact
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167 |
+
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For questions or contributions, please open an issue on the dataset repository.
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---
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*This dataset was created using the OpenMDAO optimization framework and represents real benchmark results from optimization algorithm comparisons.*
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UPLOAD_INSTRUCTIONS.txt
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# Manual HuggingFace Upload Instructions
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3 |
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4 |
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## Files in this package:
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5 |
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- data.json (55 records, 90.8 KB)
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6 |
+
- README.md (Complete model card with documentation)
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7 |
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- dataset_info.json (Dataset metadata)
|
8 |
+
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9 |
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## Upload Steps:
|
10 |
+
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1. Go to: https://huggingface.co/new-dataset
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+
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13 |
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2. Fill in details:
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- Name: openmdao-benchmarks
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- License: Apache 2.0
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- Visibility: Public
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17 |
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- Description: OpenMDAO optimization benchmarks with comprehensive evaluation metrics
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18 |
+
|
19 |
+
3. Upload files:
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20 |
+
- Drag and drop all files from this folder
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21 |
+
- Or click "Upload files" and select all
|
22 |
+
|
23 |
+
4. Your dataset will be available at:
|
24 |
+
https://huggingface.co/datasets/englund/openmdao-benchmarks
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25 |
+
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26 |
+
## Alternative: Use HuggingFace CLI
|
27 |
+
|
28 |
+
# If you have CLI access:
|
29 |
+
huggingface-cli repo create englund/openmdao-benchmarks --type dataset
|
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huggingface-cli upload englund/openmdao-benchmarks . .
|
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+
|
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## What you've created:
|
33 |
+
|
34 |
+
✅ First comprehensive OpenMDAO benchmark dataset on HuggingFace
|
35 |
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✅ 55 real optimization runs with evaluation metrics
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36 |
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✅ Literature-validated test problems with proper citations
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37 |
+
✅ Ready for research community use
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38 |
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✅ Complete documentation and model card
|
39 |
+
|
40 |
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The dataset will be valuable for:
|
41 |
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- Optimization algorithm research
|
42 |
+
- Performance prediction models
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43 |
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- Algorithm selection studies
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44 |
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- Educational use in optimization courses
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data.json
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]
|
dataset_info.json
ADDED
@@ -0,0 +1,40 @@
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|
1 |
+
{
|
2 |
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"dataset_info": {
|
3 |
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"features": {
|
4 |
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|
5 |
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|
6 |
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|
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|
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|
11 |
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|
12 |
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|
13 |
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14 |
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|
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|
27 |
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|
28 |
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|
29 |
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|
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|
31 |
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|
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|
33 |
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|
34 |
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|
35 |
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|
36 |
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|
37 |
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}
|
38 |
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|
39 |
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}
|
40 |
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}
|