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