| Name | lumix-opt JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | A type-safe, IDE-friendly wrapper for optimization solvers with automatic data-driven modeling |
| upload_time | 2025-10-30 11:24:51 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
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| keywords |
optimization
mathematical-programming
linear-programming
solver
ortools
gurobi
cplex
academic
|
| VCS |
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| requirements |
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<p align="center">
<img src="docs/source/_static/Lumix_Logo_1024.png" alt="LumiX Logo" width="200"/>
</p>
# LumiX
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/AFL-3.0)
[](https://lumix.readthedocs.io)
**A modern, type-safe wrapper for optimization solvers with automatic data-driven modeling**
LumiX makes mathematical programming accessible, maintainable, and enjoyable by providing a unified, type-safe interface to multiple optimization solvers.
## β¨ Key Features
- π― **Type-Safe & IDE-Friendly** β Full type hints and autocomplete support for a superior development experience
- π **Multi-Solver Support** β Seamlessly switch between OR-Tools, Gurobi, CPLEX, GLPK, and CP-SAT
- π **Data-Driven Modeling** β Build models directly from your data with automatic indexing and mapping
- π **Automatic Linearization** β Automatically linearize non-linear constraints (bilinear, absolute value, piecewise)
- π **Advanced Analysis** β Built-in sensitivity analysis, scenario analysis, and what-if analysis tools
- π― **Goal Programming** β Native support for multi-objective optimization with priorities and weights
- β‘ **ORM Integration** β Map solutions directly to your ORM models for seamless data flow
## π Quick Start
### Installation
```bash
# Install core library
pip install lumix
# Install with a solver (e.g., OR-Tools - free and open-source)
pip install lumix[ortools]
# Or install with multiple solvers
pip install lumix[ortools,gurobi,cplex]
```
### Simple Example
```python
from dataclasses import dataclass
from lumix import (
LXModel,
LXVariable,
LXConstraint,
LXLinearExpression,
LXOptimizer,
)
# Define your data
@dataclass
class Product:
id: str
name: str
profit: float
resource_usage: float
products = [
Product("A", "Product A", profit=30, resource_usage=2),
Product("B", "Product B", profit=40, resource_usage=3),
]
# Define decision variables
production = (
LXVariable[Product, float]("production")
.continuous()
.bounds(lower=0)
.indexed_by(lambda p: p.id)
.from_data(products)
)
# Build the model
model = (
LXModel("production_plan")
.add_variable(production)
.maximize(
LXLinearExpression()
.add_term(production, lambda p: p.profit)
)
)
# Add constraints
model.add_constraint(
LXConstraint("resource_limit")
.expression(
LXLinearExpression()
.add_term(production, lambda p: p.resource_usage)
)
.le()
.rhs(100) # Resource capacity
)
# Solve
optimizer = LXOptimizer().use_solver("ortools")
solution = optimizer.solve(model)
# Access results
if solution.is_optimal():
print(f"Optimal profit: ${solution.objective_value:,.2f}")
for product in products:
qty = solution.variables["production"][product.id]
print(f"Produce {qty:.2f} units of {product.name}")
```
## π§ Supported Solvers
LumiX provides a unified interface to multiple solvers:
| Solver | Linear | Integer | Quadratic | Advanced Features | License | Best For |
|--------|--------|---------|-----------|-------------------|---------|----------|
| **OR-Tools** | β | β | β | SOS, Indicator | Apache 2.0 (Free) | General LP/MIP, Learning |
| **Gurobi** | β | β | β | SOCP, PWL, Callbacks | Commercial/Academic | Large-scale, Production |
| **CPLEX** | β | β | β | SOCP, PWL, Callbacks | Commercial/Academic | Large-scale, Production |
| **GLPK** | β | β | β | Basic | GPL (Free) | Small problems, Teaching |
| **CP-SAT** | β | β | β | Constraint Programming | Apache 2.0 (Free) | Scheduling, Assignment |
### Switching Solvers
```python
# Just change one line to switch solvers
optimizer = LXOptimizer().use_solver("ortools") # Free
optimizer = LXOptimizer().use_solver("gurobi") # Requires license
optimizer = LXOptimizer().use_solver("cplex") # Requires license
optimizer = LXOptimizer().use_solver("glpk") # Free
optimizer = LXOptimizer().use_solver("cpsat") # Free
```
## π Core Capabilities
### Variables with Automatic Indexing
```python
# Single-dimension indexing
production = (
LXVariable[Product, float]("production")
.continuous()
.indexed_by(lambda p: p.id)
.from_data(products)
)
# Multi-dimension indexing
from lumix import LXCartesianProduct
assignment = (
LXVariable[tuple[Driver, Date, Shift], int]("assignment")
.binary()
.indexed_by(lambda t: (t[0].id, t[1].id, t[2].id))
.from_data(LXCartesianProduct(drivers, dates, shifts))
)
```
### Type-Safe Expressions
```python
# Linear expressions with automatic coefficient extraction
profit_expr = (
LXLinearExpression()
.add_term(production, lambda p: p.profit)
)
# Quadratic expressions
quadratic_expr = (
LXQuadraticExpression()
.add_quadratic_term(x, y, coefficient=0.5)
)
```
### Automatic Linearization
LumiX can automatically linearize non-linear terms:
```python
from lumix import LXBilinearTerm, LXAbsoluteTerm, LXPiecewiseLinearTerm
# Bilinear products (x * y)
bilinear = LXBilinearTerm(x, y, bounds_x=(0, 10), bounds_y=(0, 5))
# Absolute values |x|
absolute = LXAbsoluteTerm(x)
# Piecewise-linear functions
piecewise = LXPiecewiseLinearTerm(
variable=x,
breakpoints=[0, 10, 20, 30],
slopes=[1.0, 0.5, 0.2]
)
```
### Advanced Analysis
```python
from lumix import (
LXSensitivityAnalyzer,
LXScenarioAnalyzer,
LXWhatIfAnalyzer,
)
# Sensitivity analysis
sens = LXSensitivityAnalyzer(model, solution)
report = sens.generate_report()
print(report)
# Scenario analysis
scenario_analyzer = LXScenarioAnalyzer(model, optimizer)
scenario_analyzer.add_scenario("base", {})
scenario_analyzer.add_scenario("high_demand", {"demand": 150})
results = scenario_analyzer.solve_all()
# What-if analysis
whatif = LXWhatIfAnalyzer(model, optimizer)
result = whatif.increase_constraint_rhs("capacity", by=10)
print(f"Impact: ${result.delta_objective:,.2f}")
```
### Goal Programming
```python
from lumix import (
LXGoal,
LXGoalMode,
solve_goal_programming,
)
# Define multiple goals with priorities
goals = [
LXGoal(
name="profit",
target=1000,
priority=1,
weight=1.0,
is_minimization=False,
),
LXGoal(
name="quality",
target=95,
priority=2,
weight=0.8,
),
]
# Solve with goal programming
solution = solve_goal_programming(
model,
goals,
mode=LXGoalMode.SEQUENTIAL,
solver="gurobi",
)
```
## π Documentation
- **[Installation Guide](docs/source/getting-started/installation.rst)** β Install LumiX and solvers
- **[Quick Start](docs/source/getting-started/quickstart.rst)** β Build your first model
- **[Solver Guide](docs/source/getting-started/solvers.rst)** β Choose the right solver
- **[Examples](examples/)** β 11 comprehensive examples
### Examples
The repository includes 11 examples demonstrating various features:
1. **Production Planning** β Single-model indexing, data-driven modeling
2. **Driver Scheduling** β Multi-dimensional indexing, scheduling
3. **Facility Location** β Binary variables, fixed costs
4. **Basic LP** β Simple linear programming
5. **CP-SAT Assignment** β Constraint programming solver
6. **McCormick Bilinear** β Bilinear term linearization
7. **Piecewise Functions** β Piecewise-linear approximations
8. **Scenario Analysis** β Multiple scenario comparison
9. **Sensitivity Analysis** β Parameter sensitivity
10. **What-If Analysis** β Decision support
11. **Goal Programming** β Multi-objective optimization
## π― Why LumiX?
### Before (Traditional Approach)
```python
# Manual indexing, no type safety
x = {}
for i in range(len(products)):
x[i] = model.addVar(name=f"x_{i}")
# String-based error-prone expressions
model.addConstr(
sum(x[i] * data[i] for i in range(len(products))) <= capacity
)
```
### After (LumiX)
```python
# Type-safe, data-driven, IDE-friendly
production = (
LXVariable[Product, float]("production")
.continuous()
.indexed_by(lambda p: p.id)
.from_data(products)
)
model.add_constraint(
LXConstraint("capacity")
.expression(
LXLinearExpression()
.add_term(production, lambda p: p.usage)
)
.le()
.rhs(capacity)
)
```
**Benefits:**
- β Full IDE autocomplete
- β Type checking catches errors early
- β No manual indexing
- β Data-driven coefficients
- β Readable, maintainable code
- β Easy to refactor
## π οΈ Development
### Setup Development Environment
```bash
git clone https://github.com/tdelphi1981/LumiX.git
cd LumiX
pip install -e .[dev]
```
### Run Tests
```bash
pytest
```
### Type Checking
```bash
mypy src/lumix
```
### Code Formatting
```bash
black src/lumix
ruff check src/lumix
```
## π¦ Project Structure
```
lumix/
βββ src/lumix/
β βββ core/ # Core model building (variables, constraints, expressions)
β βββ solvers/ # Solver interfaces (OR-Tools, Gurobi, CPLEX, GLPK, CP-SAT)
β βββ analysis/ # Analysis tools (sensitivity, scenario, what-if)
β βββ linearization/ # Automatic linearization engine
β βββ goal_programming/ # Goal programming support
β βββ indexing/ # Multi-dimensional indexing
β βββ nonlinear/ # Non-linear terms
β βββ solution/ # Solution handling and mapping
β βββ utils/ # Utilities (logger, ORM, rational converter)
βββ examples/ # 11 comprehensive examples
βββ tests/ # Test suite
βββ docs/ # Sphinx documentation
```
## π€ Contributing
Contributions are welcome! Please:
1. Open an issue to discuss your idea
2. Fork the repository
3. Create a feature branch
4. Add tests for new functionality
5. Ensure all tests pass
6. Submit a pull request
## π License
LumiX is licensed under the [Academic Free License v3.0](LICENSE).
This is a permissive open-source license that:
- β Allows commercial use
- β Allows modification and distribution
- β Provides patent protection
- β Is OSI-approved
## π Acknowledgments
LumiX builds upon the excellent work of:
- [OR-Tools](https://developers.google.com/optimization) by Google
- [Gurobi](https://www.gurobi.com/) Optimization
- [CPLEX](https://www.ibm.com/products/ilog-cplex-optimization-studio) by IBM
- [GLPK](https://www.gnu.org/software/glpk/) by GNU
## π Support
- **Documentation**: https://lumix.readthedocs.io
- **Issues**: https://github.com/tdelphi1981/LumiX/issues
- **Discussions**: https://github.com/tdelphi1981/LumiX/discussions
## πΊοΈ Roadmap
- [ ] Additional solver support (HiGHS, SCIP)
- [ ] Jupyter notebook integration
- [ ] Interactive visualization tools
- [ ] Cloud solver integration
- [ ] Extended ORM support (SQLAlchemy, Django)
- [ ] Advanced constraint programming features
- [ ] Parallel scenario evaluation
- [ ] Model versioning and serialization
## Authors
This project is maintained by:
- **Tolga BERBER** - tolga.berber@fen.ktu.edu.tr
- **Beyzanur SΔ°YAH** - beyzanursiyah@ktu.edu.tr
For a complete list of contributors, see [AUTHORS](AUTHORS).
---
**Made with β€οΈ by the LumiX Contributors**
Raw data
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"keywords": "optimization, mathematical-programming, linear-programming, solver, ortools, gurobi, cplex, academic",
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"description": "<p align=\"center\">\n <img src=\"docs/source/_static/Lumix_Logo_1024.png\" alt=\"LumiX Logo\" width=\"200\"/>\n</p>\n\n# LumiX\n\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/AFL-3.0)\n[](https://lumix.readthedocs.io)\n\n**A modern, type-safe wrapper for optimization solvers with automatic data-driven modeling**\n\nLumiX makes mathematical programming accessible, maintainable, and enjoyable by providing a unified, type-safe interface to multiple optimization solvers.\n\n## \u2728 Key Features\n\n- \ud83c\udfaf **Type-Safe & IDE-Friendly** \u2014 Full type hints and autocomplete support for a superior development experience\n- \ud83d\udd0c **Multi-Solver Support** \u2014 Seamlessly switch between OR-Tools, Gurobi, CPLEX, GLPK, and CP-SAT\n- \ud83d\udcca **Data-Driven Modeling** \u2014 Build models directly from your data with automatic indexing and mapping\n- \ud83d\udd04 **Automatic Linearization** \u2014 Automatically linearize non-linear constraints (bilinear, absolute value, piecewise)\n- \ud83d\udcc8 **Advanced Analysis** \u2014 Built-in sensitivity analysis, scenario analysis, and what-if analysis tools\n- \ud83c\udfaf **Goal Programming** \u2014 Native support for multi-objective optimization with priorities and weights\n- \u26a1 **ORM Integration** \u2014 Map solutions directly to your ORM models for seamless data flow\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\n# Install core library\npip install lumix\n\n# Install with a solver (e.g., OR-Tools - free and open-source)\npip install lumix[ortools]\n\n# Or install with multiple solvers\npip install lumix[ortools,gurobi,cplex]\n```\n\n### Simple Example\n\n```python\nfrom dataclasses import dataclass\nfrom lumix import (\n LXModel,\n LXVariable,\n LXConstraint,\n LXLinearExpression,\n LXOptimizer,\n)\n\n# Define your data\n@dataclass\nclass Product:\n id: str\n name: str\n profit: float\n resource_usage: float\n\nproducts = [\n Product(\"A\", \"Product A\", profit=30, resource_usage=2),\n Product(\"B\", \"Product B\", profit=40, resource_usage=3),\n]\n\n# Define decision variables\nproduction = (\n LXVariable[Product, float](\"production\")\n .continuous()\n .bounds(lower=0)\n .indexed_by(lambda p: p.id)\n .from_data(products)\n)\n\n# Build the model\nmodel = (\n LXModel(\"production_plan\")\n .add_variable(production)\n .maximize(\n LXLinearExpression()\n .add_term(production, lambda p: p.profit)\n )\n)\n\n# Add constraints\nmodel.add_constraint(\n LXConstraint(\"resource_limit\")\n .expression(\n LXLinearExpression()\n .add_term(production, lambda p: p.resource_usage)\n )\n .le()\n .rhs(100) # Resource capacity\n)\n\n# Solve\noptimizer = LXOptimizer().use_solver(\"ortools\")\nsolution = optimizer.solve(model)\n\n# Access results\nif solution.is_optimal():\n print(f\"Optimal profit: ${solution.objective_value:,.2f}\")\n for product in products:\n qty = solution.variables[\"production\"][product.id]\n print(f\"Produce {qty:.2f} units of {product.name}\")\n```\n\n## \ud83d\udd27 Supported Solvers\n\nLumiX provides a unified interface to multiple solvers:\n\n| Solver | Linear | Integer | Quadratic | Advanced Features | License | Best For |\n|--------|--------|---------|-----------|-------------------|---------|----------|\n| **OR-Tools** | \u2713 | \u2713 | \u2717 | SOS, Indicator | Apache 2.0 (Free) | General LP/MIP, Learning |\n| **Gurobi** | \u2713 | \u2713 | \u2713 | SOCP, PWL, Callbacks | Commercial/Academic | Large-scale, Production |\n| **CPLEX** | \u2713 | \u2713 | \u2713 | SOCP, PWL, Callbacks | Commercial/Academic | Large-scale, Production |\n| **GLPK** | \u2713 | \u2713 | \u2717 | Basic | GPL (Free) | Small problems, Teaching |\n| **CP-SAT** | \u2717 | \u2713 | \u2717 | Constraint Programming | Apache 2.0 (Free) | Scheduling, Assignment |\n\n### Switching Solvers\n\n```python\n# Just change one line to switch solvers\noptimizer = LXOptimizer().use_solver(\"ortools\") # Free\noptimizer = LXOptimizer().use_solver(\"gurobi\") # Requires license\noptimizer = LXOptimizer().use_solver(\"cplex\") # Requires license\noptimizer = LXOptimizer().use_solver(\"glpk\") # Free\noptimizer = LXOptimizer().use_solver(\"cpsat\") # Free\n```\n\n## \ud83d\udcda Core Capabilities\n\n### Variables with Automatic Indexing\n\n```python\n# Single-dimension indexing\nproduction = (\n LXVariable[Product, float](\"production\")\n .continuous()\n .indexed_by(lambda p: p.id)\n .from_data(products)\n)\n\n# Multi-dimension indexing\nfrom lumix import LXCartesianProduct\n\nassignment = (\n LXVariable[tuple[Driver, Date, Shift], int](\"assignment\")\n .binary()\n .indexed_by(lambda t: (t[0].id, t[1].id, t[2].id))\n .from_data(LXCartesianProduct(drivers, dates, shifts))\n)\n```\n\n### Type-Safe Expressions\n\n```python\n# Linear expressions with automatic coefficient extraction\nprofit_expr = (\n LXLinearExpression()\n .add_term(production, lambda p: p.profit)\n)\n\n# Quadratic expressions\nquadratic_expr = (\n LXQuadraticExpression()\n .add_quadratic_term(x, y, coefficient=0.5)\n)\n```\n\n### Automatic Linearization\n\nLumiX can automatically linearize non-linear terms:\n\n```python\nfrom lumix import LXBilinearTerm, LXAbsoluteTerm, LXPiecewiseLinearTerm\n\n# Bilinear products (x * y)\nbilinear = LXBilinearTerm(x, y, bounds_x=(0, 10), bounds_y=(0, 5))\n\n# Absolute values |x|\nabsolute = LXAbsoluteTerm(x)\n\n# Piecewise-linear functions\npiecewise = LXPiecewiseLinearTerm(\n variable=x,\n breakpoints=[0, 10, 20, 30],\n slopes=[1.0, 0.5, 0.2]\n)\n```\n\n### Advanced Analysis\n\n```python\nfrom lumix import (\n LXSensitivityAnalyzer,\n LXScenarioAnalyzer,\n LXWhatIfAnalyzer,\n)\n\n# Sensitivity analysis\nsens = LXSensitivityAnalyzer(model, solution)\nreport = sens.generate_report()\nprint(report)\n\n# Scenario analysis\nscenario_analyzer = LXScenarioAnalyzer(model, optimizer)\nscenario_analyzer.add_scenario(\"base\", {})\nscenario_analyzer.add_scenario(\"high_demand\", {\"demand\": 150})\nresults = scenario_analyzer.solve_all()\n\n# What-if analysis\nwhatif = LXWhatIfAnalyzer(model, optimizer)\nresult = whatif.increase_constraint_rhs(\"capacity\", by=10)\nprint(f\"Impact: ${result.delta_objective:,.2f}\")\n```\n\n### Goal Programming\n\n```python\nfrom lumix import (\n LXGoal,\n LXGoalMode,\n solve_goal_programming,\n)\n\n# Define multiple goals with priorities\ngoals = [\n LXGoal(\n name=\"profit\",\n target=1000,\n priority=1,\n weight=1.0,\n is_minimization=False,\n ),\n LXGoal(\n name=\"quality\",\n target=95,\n priority=2,\n weight=0.8,\n ),\n]\n\n# Solve with goal programming\nsolution = solve_goal_programming(\n model,\n goals,\n mode=LXGoalMode.SEQUENTIAL,\n solver=\"gurobi\",\n)\n```\n\n## \ud83d\udcd6 Documentation\n\n- **[Installation Guide](docs/source/getting-started/installation.rst)** \u2014 Install LumiX and solvers\n- **[Quick Start](docs/source/getting-started/quickstart.rst)** \u2014 Build your first model\n- **[Solver Guide](docs/source/getting-started/solvers.rst)** \u2014 Choose the right solver\n- **[Examples](examples/)** \u2014 11 comprehensive examples\n\n### Examples\n\nThe repository includes 11 examples demonstrating various features:\n\n1. **Production Planning** \u2014 Single-model indexing, data-driven modeling\n2. **Driver Scheduling** \u2014 Multi-dimensional indexing, scheduling\n3. **Facility Location** \u2014 Binary variables, fixed costs\n4. **Basic LP** \u2014 Simple linear programming\n5. **CP-SAT Assignment** \u2014 Constraint programming solver\n6. **McCormick Bilinear** \u2014 Bilinear term linearization\n7. **Piecewise Functions** \u2014 Piecewise-linear approximations\n8. **Scenario Analysis** \u2014 Multiple scenario comparison\n9. **Sensitivity Analysis** \u2014 Parameter sensitivity\n10. **What-If Analysis** \u2014 Decision support\n11. **Goal Programming** \u2014 Multi-objective optimization\n\n## \ud83c\udfaf Why LumiX?\n\n### Before (Traditional Approach)\n\n```python\n# Manual indexing, no type safety\nx = {}\nfor i in range(len(products)):\n x[i] = model.addVar(name=f\"x_{i}\")\n\n# String-based error-prone expressions\nmodel.addConstr(\n sum(x[i] * data[i] for i in range(len(products))) <= capacity\n)\n```\n\n### After (LumiX)\n\n```python\n# Type-safe, data-driven, IDE-friendly\nproduction = (\n LXVariable[Product, float](\"production\")\n .continuous()\n .indexed_by(lambda p: p.id)\n .from_data(products)\n)\n\nmodel.add_constraint(\n LXConstraint(\"capacity\")\n .expression(\n LXLinearExpression()\n .add_term(production, lambda p: p.usage)\n )\n .le()\n .rhs(capacity)\n)\n```\n\n**Benefits:**\n- \u2713 Full IDE autocomplete\n- \u2713 Type checking catches errors early\n- \u2713 No manual indexing\n- \u2713 Data-driven coefficients\n- \u2713 Readable, maintainable code\n- \u2713 Easy to refactor\n\n## \ud83d\udee0\ufe0f Development\n\n### Setup Development Environment\n\n```bash\ngit clone https://github.com/tdelphi1981/LumiX.git\ncd LumiX\npip install -e .[dev]\n```\n\n### Run Tests\n\n```bash\npytest\n```\n\n### Type Checking\n\n```bash\nmypy src/lumix\n```\n\n### Code Formatting\n\n```bash\nblack src/lumix\nruff check src/lumix\n```\n\n## \ud83d\udce6 Project Structure\n\n```\nlumix/\n\u251c\u2500\u2500 src/lumix/\n\u2502 \u251c\u2500\u2500 core/ # Core model building (variables, constraints, expressions)\n\u2502 \u251c\u2500\u2500 solvers/ # Solver interfaces (OR-Tools, Gurobi, CPLEX, GLPK, CP-SAT)\n\u2502 \u251c\u2500\u2500 analysis/ # Analysis tools (sensitivity, scenario, what-if)\n\u2502 \u251c\u2500\u2500 linearization/ # Automatic linearization engine\n\u2502 \u251c\u2500\u2500 goal_programming/ # Goal programming support\n\u2502 \u251c\u2500\u2500 indexing/ # Multi-dimensional indexing\n\u2502 \u251c\u2500\u2500 nonlinear/ # Non-linear terms\n\u2502 \u251c\u2500\u2500 solution/ # Solution handling and mapping\n\u2502 \u2514\u2500\u2500 utils/ # Utilities (logger, ORM, rational converter)\n\u251c\u2500\u2500 examples/ # 11 comprehensive examples\n\u251c\u2500\u2500 tests/ # Test suite\n\u2514\u2500\u2500 docs/ # Sphinx documentation\n```\n\n## \ud83e\udd1d Contributing\n\nContributions are welcome! Please:\n\n1. Open an issue to discuss your idea\n2. Fork the repository\n3. Create a feature branch\n4. Add tests for new functionality\n5. Ensure all tests pass\n6. Submit a pull request\n\n## \ud83d\udcc4 License\n\nLumiX is licensed under the [Academic Free License v3.0](LICENSE).\n\nThis is a permissive open-source license that:\n- \u2713 Allows commercial use\n- \u2713 Allows modification and distribution\n- \u2713 Provides patent protection\n- \u2713 Is OSI-approved\n\n## \ud83d\ude4f Acknowledgments\n\nLumiX builds upon the excellent work of:\n\n- [OR-Tools](https://developers.google.com/optimization) by Google\n- [Gurobi](https://www.gurobi.com/) Optimization\n- [CPLEX](https://www.ibm.com/products/ilog-cplex-optimization-studio) by IBM\n- [GLPK](https://www.gnu.org/software/glpk/) by GNU\n\n## \ud83d\udcde Support\n\n- **Documentation**: https://lumix.readthedocs.io\n- **Issues**: https://github.com/tdelphi1981/LumiX/issues\n- **Discussions**: https://github.com/tdelphi1981/LumiX/discussions\n\n## \ud83d\uddfa\ufe0f Roadmap\n\n- [ ] Additional solver support (HiGHS, SCIP)\n- [ ] Jupyter notebook integration\n- [ ] Interactive visualization tools\n- [ ] Cloud solver integration\n- [ ] Extended ORM support (SQLAlchemy, Django)\n- [ ] Advanced constraint programming features\n- [ ] Parallel scenario evaluation\n- [ ] Model versioning and serialization\n\n## Authors\n\nThis project is maintained by:\n\n- **Tolga BERBER** - tolga.berber@fen.ktu.edu.tr\n- **Beyzanur S\u0130YAH** - beyzanursiyah@ktu.edu.tr\n\nFor a complete list of contributors, see [AUTHORS](AUTHORS).\n\n---\n\n**Made with \u2764\ufe0f by the LumiX Contributors**\n",
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