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