Optimization with Python: all you need for LP-MILP-NLP-MINLP. Optimization can help reduce the time and resources needed to complete a task. Python is a widely used programming language that makes it easy to write code and execute it. This book covers all you need for LP-Milp-Nlp-Minlp optimization, including algorithms, data structures, and software tools.
This course covers the basics of linear programming, mixed integer linear programming, and multiple objective linear programming. It is designed for students who are new to optimization or who want to brush up on their skills. The course is self-paced, so you can work at your own pace.
Python is a widely used programming language that can be used for a variety of tasks, including optimization. In this article, we will discuss how to optimize LP-, MP-, and MNLP models with Python.
- Solve optimization problems using linear programming, mixed-integer linear programming, nonlinear programming, mixed-integer nonlinear programming,
- LP, MILP, NLP, MINLP
- Main solvers and frameworks, including CPLEX, Gurobi, and Pyomo
- Genetic algorithm, particle swarm, and constraint programming
- From the basic to advanced tools, learn how to install Python and how to use the main packages (Numpy, Pandas, Matplotlib…)
- How to solve problems with arrays and summations
Optimization with Python: all you need for LP-MILP-NLP-MINLP Course Requirements
- Some knowledge in programming logic
- Why and where to use optimization
- It is NOT necessary to know Python
Optimization with Python: all you need for LP-MILP-NLP-MINLP Course Description
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!
Who this course is for:
- Undergrad, graduation, master program, and doctorate students.
- Companies that wish to solve complex problems
- People interested in complex problems and artificial intelligence