Course
Introduction to Optimization in Python
IntermediateSkill Level
Updated 06/2025PythonProgramming4 hr13 videos42 Exercises3,250 XP5,017Statement of Accomplishment
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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.Tackle Complex Problems Head-On
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.Prerequisites
Introduction to NumPy1
Introduction to Optimization
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.
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
Non-linear Constrained Optimization
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
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.
Introduction to Optimization in Python
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FAQs
Which Python libraries are used for optimization in this course?
You will use SymPy for analytical solutions, SciPy for numerical optimization, and PuLP for linear and mixed integer programming problems.
What types of optimization problems are covered?
The course covers unconstrained optimization, linear programming, constrained convex optimization, and mixed integer linear programming, progressing from simple to complex problems.
What real-world applications are used as examples?
You will solve problems in manufacturing, profit maximization, resource allocation, HR allocation with training costs, and capital budgeting with dependent projects.
Do I need a strong math background for this course?
Basic calculus concepts are helpful, but Chapter 1 includes a mathematical primer covering the foundations needed. You should know intermediate Python and NumPy.
What is sensitivity analysis and is it covered?
Sensitivity analysis examines how changes in problem parameters affect the optimal solution. Chapter 4 covers it alongside linearization techniques for simplifying complex problems.
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