nonlinear-optimization-solver

# Nonlinear Optimization Solver ## Purpose Provides capabilities for solving general nonlinear optimization problems including constrained and unconstrained formulations. ## Capabilities - Gradient-based methods (BFGS, L-BFGS, CG) - Newton and quasi-Newton methods - Interior point methods - Sequential quadratic programming (SQP) - Global optimization (basin-hopping, differential evolution) - Constraint handling ## Usage Guidelines 1. **Starting Point**: Provide good initial guesses 2. **Gradient Information**: Supply gradients when available 3. **Global vs Local**: Choose global methods for multimodal problems 4. **Constraint Handling**: Use appropriate constraint formulations ## Tools/Libraries - IPOPT - KNITRO - NLopt - scipy.optimize