Convex 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 convex optimization problems efficiently
SKILL.md
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Convex Optimization Solver
Purpose
Provides capabilities for formulating and solving convex optimization problems efficiently and reliably.
Capabilities
- Linear programming (LP)
- Quadratic programming (QP)
- Second-order cone programming (SOCP)
- Semidefinite programming (SDP)
- Conic optimization
- Duality analysis
Usage Guidelines
- Convexity Verification: Confirm problem convexity
- Formulation: Express problem in standard conic form
- Solver Selection: Choose solver based on problem structure
- Duality: Extract dual variables for sensitivity analysis
Tools/Libraries
- CVXPY
- Gurobi
- MOSEK
- SCS
- ECOS