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# Introduction to Optimization in Python

Learn to solve real-world optimization problems using Python's SciPy and PuLP, covering everything from basic to constrained and complex optimization.

4 Hours13 Videos42 Exercises

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## Course Description

Optimization problems are ubiquitous in engineering, sciences, and the social sciences. This course will take you from zero optimization knowledge to a hero optimizer. You will use mathematical modeling to translate real-world problems into mathematical ones and solve them in Python using the SciPy and PuLP packages.

## Apply Calculus to Unconstrained Optimization Problems with SymPy

You will start by learning the definition of an optimization problem and its use cases. You will use SymPy to apply calculus to yield analytical solutions to unconstrained optimization. You will not have to calculate derivatives or solve equations; SymPy works seamlessly! Similarly, you will use SciPy to get numerical solutions.

Next, you will learn to solve linear programming problems in SciPy and PuLP. To capture real-world complexity, you will see how to apply PuLP and SciPy to solve constrained convex optimization and mixed integer optimization. By the end of this course, you will have solved real-world optimization problems, including manufacturing, profit and budgeting, resource allocation, and more.

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1. 1

### Introduction to Optimization

Free

This chapter introduces optimization, its core components, and its wide applications across industries and domains. It presents a quick, exhaustive search method for solving an optimization problem. It provides a mathematical primer for the concepts required for this course.

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Introduction to mathematical optimization
50 xp
Understanding mathematical optimization
50 xp
Applying an objective function
100 xp
Exhaustive search method
100 xp
Univariate optimization
50 xp
Finding the derivative
100 xp
Find the second derivative
100 xp
Multivariate optimization
50 xp
Partial derivatives with SymPy
100 xp
Limitations of differentiation
100 xp
2. 2

### Unconstrained and Linear Constrained Optimization

This chapter covers solving unconstrained and constrained optimization problems with differential calculus and SymPy, identifying potential pitfalls. SciPy is also introduced to solve unconstrained optimization problems, in single and multiple dimensions, numerically, with a few lines of code. The chapter goes on to solve linear programming in SciPy and PuLP.

3. 3

### Non-linear Constrained Optimization

Free

This chapter introduces convex-constrained optimization problems with different constraints and looks at mixed integer linear programming problems, essentially linear programming problems where at least one variable is an integer.

4. 4

### Robust Optimization Techniques

This chapter covers finding the global optimum when multiple good solutions exist. We will conduct sensitivity analysis and learn linearization techniques that reduce non-linear problems to easily solvable ones with SciPy or PuLP. In terms of applications, we will solve an HR allocation with training costs problem and capital budgeting with dependent projects.

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Collaborators

Audio Recorded By

Prerequisites

Introduction to NumPy
James Chapman

Curriculum Manager, DataCamp

James is a Curriculum Manager at DataCamp, where he collaborates with experts from industry and academia to create courses on AI, data science, and analytics. He has led nine DataCamp courses on diverse topics in Python, R, AI developer tooling, and Google Sheets. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in high-redshift quasar detection. In his spare time, he enjoys restoring retro toys and electronics.

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Jasmin Ludolf

Data Science Content Developer, DataCamp

Jasmin is a Content Developer at DataCamp. After ten years as a global marketing manager in the music industry, she recently changed careers to follow her curiosity for data. Her passion is value exchange and making data science accessible to all.
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