How to improve your diet and save money with Python
Process optimization in industry has become essential in order to maximize the resources available and reduce energy consumption.
Optimization problems become interesting when dealing with restrictions (linear or nonlinear) and integer variables (modeling the discrete decisions). Python ecosystem presents different libraries to solve optimization problems, some of them are CVXOpt, CVXPy, PulP, OpenOpt, or Pyomo.
Among them, Pyomo results interesting because:
- It can be used for Mathematical modeling in Python similarly to AMPL (and GAMS)
- It communicates with the main solvers used in this field such as GLPK, Gurobi, CPLEX, CBC and PICO
- It’s free and open source Python library (BSD license), being developed by Sandia National Laboratories, USA.
- It supports Python 3 and it is easy to install.
The talk will be divided in three parts:
Introduction to Mathematical Programming/Optimization (15 min): visual introduction to optimization concepts including restrictions and non linearties (linear Programming, Nonlinear Programming, ILP, MIP, MINLP).
Introduction to the Pyomo sintax and a quick note for the installation (20 min): showing how to improve their diet and save money when ordering food in fast food restaurants.
Optimization problems in engineering (10 min): showing more advanced optimization examples that include decision variables.