Optimization with Python: all you need for LP-MILP-NLP-MINLP, Learn how to solve optimization problems using CPLEX, Gurobi, A.I., and more (also called operational research)
Created by Rafael Silva Pinto
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Operational planning and long term planning for companies are more complex in recent years. Information change fast, and the decision making is a hard task. Therefore, optimization algorithms are used to find optimal solutions for these problems. Professionals in this field are the most valued ones.
In this course you will learn what is necessary to solve problems applying:
Linear Programming (LP)
Mixed-Integer Linear Programming (MILP)
NonLinear Programming (NLP)
Mixed-Integer Linear Programming (MINLP)
Genetic Algorithm (GA)
Particle Swarm (PSO)
Constraint Programming (CP)
The following solvers and frameworks will be explored:
Solvers: CPLEX – Gurobi – GLPK – CBC – IPOPT – Couenne – SCIP
Frameworks: Pyomo – Or-Tools – PuLP
Same Packages and tools: Geneticalgorithm – Pyswarm – Numpy – Pandas – MatplotLib – Spyder – Jupyter Notebook
In addition to the classes and exercises, the following problems will be solved step by step:
Optimization on how to install a fence in a garden
Route optimization problem
Maximize the revenue in a rental car store
Optimal Power Flow: Electrical Systems
The classes use examples that are created step by step, so we will create the algorithms together.
Besides this course is more concerned with mathematical approaches, you will also learn how to solve problems using artificial intelligence (AI), genetic algorithm, and particle swarm.
Don't worry if you do not know Python or how to code, I will teach you everything you need to start with optimization, from the installation of Python and its basics, to complex optimization problems.
I hope this course can help you in your carrier. Yet, you will receive a certification from Udemy.
See you in the classes