Congratulations!

1. Congratulations!

Congratulations! You've made it to the end of the course. Let's recap what you learned.

2. Chapter 1

In chapter 1 you were introduced to mathematical optimization and univariate and multivariate optimization.

3. Chapter 2

Chapter 2 took a deeper dive into unconstrained optimization and introduced bounded and linear constraints as well as a look at linear programming.

4. Chapter 3

In chapter three you saw convex constrained optimization and solved mixed integer linear programming problems.

5. Chapter 4

Finally, in chapter four your learned how to transform non-linear problems, and perform global optimization and sensitivity analysis.

6. The real-world

Throughout the course, you applied these skills to real world scenarios including manufacturing, profit and budgeting, resource allocation and more.

7. Congratulations!

Congratulations again! And best of luck as you continue your learning journey.

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

This exercise is part of the course

Introduction to Optimization in Python

IntermediateSkill Level

4.8+

Start Course for 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.

Exercise 1: Introduction to mathematical optimization Exercise 2: Understanding mathematical optimization Exercise 3: Applying an objective function Exercise 4: Exhaustive search method Exercise 5: Univariate optimization Exercise 6: Finding the derivative Exercise 7: Find the second derivative Exercise 8: Multivariate optimization Exercise 9: Partial derivatives with SymPy Exercise 10: Limitations of differentiation

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.

Exercise 1: Unconstrained optimization Exercise 2: Finding the maxima Exercise 3: Multivariate optimization Exercise 4: Bound-constrained optimization Exercise 5: Working with Bounds Exercise 6: Handling hard inequalities Exercise 7: Linear programming Exercise 8: What is linear programming?Exercise 9: PuLP for linear optimization Exercise 10: Handling multiple elements

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.

Exercise 1: Convex constrained-optimization Exercise 2: Interpreting indifference curves Exercise 3: Visualize the indifference curve Exercise 4: Utility maximization Exercise 5: Convex-constrained optimization with inequality constraints Exercise 6: Interior or corner?Exercise 7: Linear constrained biscuits Exercise 8: Nonlinear constrained biscuits Exercise 9: Mixed integer linear programming (MILP)Exercise 10: Adjusting MILP Exercise 11: Understanding integrality

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.

Exercise 1: Transforming non-linear problems Exercise 2: Solving the capital budgeting problem Exercise 3: Allocating resources Exercise 4: Global optimization in SciPy Exercise 5: Find the global optimum Exercise 6: Using callback Exercise 7: Sensitivity analysis in PuLP Exercise 8: Slack and shadow price Exercise 9: Shadow prices with juice Exercise 10: The diet problem revisited Exercise 11: Congratulations!

Current Exercise