Constrained Optimization MCP Server

# Changelog All notable changes to the **General Purpose MCP Server for Constrained Optimization** will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [Unreleased] ### Added - Comprehensive mathematical reference documentation - Enhanced Jupyter notebook with interactive examples - GitHub Actions workflow for automated PyPI publishing - Build and test automation scripts ## [1.0.0] - 2025-01-13 ### Added - **Initial release** of General Purpose MCP Server for Constrained Optimization - **Model Context Protocol (MCP)** server implementation - **Multiple solver support**: - Z3 SMT solver for constraint satisfaction problems - CVXPY for convex optimization - HiGHS for linear programming - OR-Tools for constraint programming - **Portfolio optimization** capabilities: - Markowitz mean-variance optimization - Black-Litterman model with investor views - Risk parity optimization - ESG-constrained optimization - Multi-asset portfolio management - **Scheduling and operations** research: - Job shop scheduling with Gantt chart visualization - Nurse scheduling with fairness constraints - Resource allocation and capacity planning - **Combinatorial optimization**: - N-Queens problem solving - Knapsack problems (0/1 and multiple variants) - Assignment and allocation problems - **Economic production planning**: - Multi-period production planning - Inventory management and demand forecasting - Supply chain optimization - Cost minimization strategies - **Comprehensive examples**: - Interactive Jupyter notebook with 6+ example categories - Mathematical formulations and theory - Visualizations and performance analysis - Real-time optimization demonstrations - **Documentation**: - Complete API reference - Mathematical reference with 70+ formulas - Academic-style PDF documentation - Professional package guide - **AI agent integration**: - Unified API for different optimization types - Real-time optimization for AI agents - Easy integration with MCP protocol - **Mathematical rigor**: - Complete KKT conditions and duality theory - Complexity analysis (P vs NP-Complete) - Solution methods (exact, heuristic, decomposition) - Performance metrics and optimization criteria ### Technical Details - **Python 3.10+** support - **Modular architecture** with extensible design - **Comprehensive error handling** and validation - **High performance** optimization for large-scale problems - **Professional packaging** with proper metadata - **Cross-platform** compatibility ### Dependencies - z3-solver>=4.14.1.0 - pydantic>=2.0.0 - returns>=0.20.0 - fastmcp>=0.1.0 - cvxpy>=1.6.0 - highs>=1.11.0 - ortools<9.15.0 - numpy>=1.24.0 - pandas>=2.0.0 - matplotlib>=3.7.0 - seaborn>=0.12.0 - scipy>=1.10.0 - jupyter>=1.0.0 - ipywidgets>=8.0.0 ### Examples Included - `examples/nqueens.py` - N-Queens problem with chessboard visualization - `examples/knapsack.py` - Knapsack variants with performance analysis - `examples/job_shop_scheduling.py` - Production scheduling with Gantt charts - `examples/nurse_scheduling.py` - Workforce scheduling with constraints - `examples/portfolio_optimization.py` - Advanced financial strategies - `examples/economic_production_planning.py` - Multi-period planning - `examples/constrained_optimization_demo.ipynb` - Interactive notebook ### Documentation - `README.md` - Main project documentation - `docs/README.md` - API reference - `docs/mathematical_reference.md` - Complete mathematical guide - `docs/constrained_optimization_package.pdf` - Professional package guide - `docs/constrained_optimization_journal.pdf` - Academic research paper - `PUBLISHING.md` - Publishing and deployment guide --- ## Version History - **1.0.0** (2025-01-13): Initial release with comprehensive optimization capabilities - **0.1.0** (Development): Early development and testing phase ## Future Roadmap ### Planned Features - [ ] Additional optimization solvers (Gurobi, CPLEX) - [ ] Machine learning integration for optimization - [ ] Real-time optimization dashboard - [ ] Cloud deployment support - [ ] Advanced portfolio strategies (Black-Litterman, Risk Parity) - [ ] Multi-objective optimization - [ ] Stochastic optimization - [ ] Robust optimization ### Research Areas - [ ] Quantum optimization algorithms - [ ] Federated optimization - [ ] Explainable AI for optimization - [ ] Automated problem formulation - [ ] Optimization as a service (OaaS) --- **For more information, visit**: https://github.com/your-username/constrained-opt-mcp **PyPI Package**: https://pypi.org/project/constrained-opt-mcp/ **Documentation**: https://github.com/your-username/constrained-opt-mcp/tree/main/docs