Expanded Library

# Understanding and Applying Numerical Optimization Techniques

Optimization is all about smart trade-offs given difficult choices. This course focuses on three specific aspects of numerical optimization: correctly setting up optimization problems, linear programming, and integer programming.

## What you'll learn

Many optimization problems are conceptually similar to software design patterns - they are generally usable techniques that help with commonly recurring problems. In this course, Understanding and Applying Numerical Optimization Techniques, you'll first learn about framing the optimization problem correctly. Correctly framing the problem is the key to finding the right solution, and is also a powerful general tool in business, data analysis, and modeling. Next, you'll explore linear programming. Linear programming is a specific type of optimization used when the problem can be framed purely in terms of linear (straight line) relationships. Finally, you'll wrap up this course learning about integer programming. Integer programming is similar to linear programming, but it involves adding conditions that our variables be integers. This occurs very often in the real world, but the math of solving these problems is quite a bit more involved. By the end of this course, you will have a good understanding of how numerical optimization techniques can be used in data modeling, and how those models can be implemented in Excel, Python, and R.

## Table of contents

Course Overview
2mins
Introducing Numerical Optimization
30mins
Understanding Linear Programming
44mins
Implementing Linear Programming in Excel
34mins
Implementing Linear Programming in R
29mins
Implementing Linear Programming in Python
24mins
Understanding Integer Programming
38mins
Implementing Integer Programming in Excel
16mins
Implementing Integer Programming in R
7mins
Implementing Integer Programming in Python
15mins

## About the author

Vitthal has spent a lot of his life studying - he holds Masters Degrees in Math and Electrical Engineering from Stanford, an MBA from INSEAD, and a Bachelors Degree in Computer Engineering from Mumbai. He has also spent a lot of his life working - as a derivatives quant at Credit Suisse in New York, then as a quant trader, first with a hedge fund in Greenwich and then on his own, and finally at Google in Singapore and Flipkart in Bangalore. In all these roles, he has written a lot of code, and b... more

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