Prescriptive Analytics: 03 Bibliotecas para fazer Otimização em Python

O processo de otimização tem muita relação com Machine Learning e sistemas de analytics. Todas trabalham com equações, buscando formas de explicar fenômenos e gerar novos insights. Para facilitar esse processo, existem algumas ferramentas em Python para Otimização.

Python:

1. Scipy

Optimization (scipy.optimize)

The scipy.optimize package provides several commonly used optimization algorithms. A detailed listing is available: scipy.optimize (can also be found by help(scipy.optimize)).
The module contains:

1. Unconstrained and constrained minimization of multivariate scalar functions (minimize) using a variety of algorithms (e.g. BFGS, Nelder-Mead simplex, Newton Conjugate Gradient, COBYLA or SLSQP)
2. Global (brute-force) optimization routines (e.g. basinhopping, differential_evolution)
3. Least-squares minimization (least_squares) and curve fitting (curve_fit) algorithms
4. Scalar univariate functions minimizers (minimize_scalar) and root finders (root_scalar)
5. Multivariate equation system solvers (root) using a variety of algorithms (e.g. hybrid Powell, Levenberg-Marquardt or large-scale methods such as Newton-Krylov).

2. pyOpt

3. Gekko Optimization Suite