Nonlinear optimization solver
Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration
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Solve general nonlinear optimization problems
SKILL.md
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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
- Starting Point: Provide good initial guesses
- Gradient Information: Supply gradients when available
- Global vs Local: Choose global methods for multimodal problems
- Constraint Handling: Use appropriate constraint formulations
Tools/Libraries
- IPOPT
- KNITRO
- NLopt
- scipy.optimize