A Python-embedded modeling language for convex optimization problems. It allows you to express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers.
CVXPY
- Python
- IA y aprendizaje automático, Análisis de datos, Matemáticas, Control de procesos
- Inteligencia Artificial (IA), Optimización del diseño, Aprendizaje automático, Software, Análisis estadístico
Características:
- Allows specifying optimization problems using a natural mathematical syntax, supports various types of convex optimization (LP, QP, SOCP, SDP, MILP), interfaces with multiple open-source and commercial solvers (e.g., ECOS, SCS, MOSEK, Gurobi), disciplined convex programming (DCP) analysis
Precios:
- Gratis
- User-friendly syntax for defining optimization problems, automatically transforms problems to solver-compatible forms, powerful for a wide range of applications, good documentation and examples.
- Limited to convex (and mixed-integer convex) optimization problems, performance depends on the underlying solver, DCP rules can be restrictive for new users.
Ideal para:
- Researchers and engineers who need to model and solve convex optimization problems in various fields like control, finance, machine learning, and operations research.