Constrained Optimization
Provides unified access to multiple optimization solvers including Z3, CVXPY, HiGHS, and OR-Tools for solving constraint satisfaction, convex optimization, linear programming, and combinatorial problems like portfolio optimization, production planning, scheduling, and classic puzzles with mathematic
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This constrained optimization MCP server by Rajnish Sharma provides AI agents with unified access to multiple optimization solvers including Z3 for constraint satisfaction, CVXPY for convex optimization, HiGHS for linear programming, and OR-Tools for combinatorial problems. Built with Python and featuring a modular architecture with typed problem definitions, it offers tools for portfolio optimization with Markowitz theory and risk constraints, production planning and resource allocation, scheduling problems like job shop and nurse scheduling, and classic combinatorial puzzles including N-Queens and knapsack variants. The implementation includes comprehensive examples with mathematical formulations, visualization capabilities through matplotlib and seaborn, and Docker deployment support, making it valuable for financial analysts optimizing investment portfolios, operations researchers solving supply chain problems, and developers building AI-powered decision support systems that require mathematical optimization capabilities.
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