pulp.tar.gz

pulp

We make optimization decisions every day. To improve the performance of your applications, computers need to do the same. This course will help you optimize computing functions through the open-source Python library PuLP.

In this course, you’ll learn about linear programming, an optimization approach that maximizes or minimizes functions through defined algebra, and the PuLP library along with its various optimizers available via APIs. You’ll identify the variables and constraints of computing functions and define the optimization goals. You’ll then learn and practice common linear programming algorithms. Finally, you’ll be introduced to potential applications in the real world.

By the end of the course, you will be able to optimize computing functions using Python and PuLP.

Master Linear Programming in Python with PuLP

# Introduction to Python

Python is among the most commonly used general-purpose programming languages these days. It was first released in 1991 to increase code readability using significant indentation. It is an interpreted language, so during execution, each line is interpreted into the machine’s language on the go. In contrast to C++, where the code is compiled first and then executed, Python programs can be directly executed without compilation.

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# Code readability 

We can see the immense use of Python in scientific and engineering applications, thanks to its plain English-like syntax. This makes it easy for naive programmers to quickly master it. Not only this but its concise syntax support the rapid development of large-scale applications.

# Applications of Python

**Python** can be used in any area of the computing paradigm. However, it is mainly used in web development. Python-based frameworks like Django and Flask make it easy to create back-end logic. It is also heavily used in data science for machine learning, analysis, and visualization. Scikit-learn and TensorFlow are two of the most popular libraries for this. For data analysis or visualization, Matplotlib is an excellent library. We can also develop desktop-based GUI applications like Tkinter, wxWidgetM, Kivy, or PyQt applications. For scientific and numeric computing, several options such as SciPy, Scikit-learn, NumPy, pandas, and Matplotlib are available. If we want to develop audio- or video-based applications, then Gstreamer, Pyglet, and QT Phonon are good options.

# Introduction to PuLP

**PuLP** is a free, open-source library that solves optimization problems computationally. To use PuLP for this purpose, we first need to create a mathematical model of an optimization problem that includes the optimization variables and constraints. Then, we use an optimizer to solve the linear programming problems. You may sometimes hear this called **LP optimization**. Any of the external linear programming solvers like CBC, GLPK, CPLEX, Gurobi, and others can be used in this way. The solver updates the optimization variables to their optimal value.


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Learn about the various benefits of programming in Python and how the PuLP library works.


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Introduction to Python and PuLP

Introduction to Python

Code readability