Constrained Optimization MCP Server

Solve portfolio optimization problems using modern portfolio theory. This tool implements Markowitz mean-variance optimization to find optimal asset allocations that maximize expected return while constraining risk. Args: assets: List of asset names expected_returns: List of expected returns for each asset risk_factors: List of risk factors (standard deviations) for each asset correlation_matrix: Correlation matrix between assets max_allocations: Optional maximum allocation limits for each asset risk_budget: Optional maximum portfolio risk (variance) description: Optional problem description Returns: Optimal portfolio weights and performance metrics Example: assets = ["Bonds", "Stocks", "RealEstate", "Commodities"] expected_returns = [0.08, 0.12, 0.10, 0.15] risk_factors = [0.02, 0.15, 0.08, 0.20] correlation_matrix = [[1.0, 0.2, 0.3, 0.1], [0.2, 1.0, 0.6, 0.7], ...] max_allocations = [0.4, 0.6, 0.3, 0.2] risk_budget = 0.01