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.
Course info
Rating
(14)
Level
Intermediate
Updated
May 4, 2017
Duration
3h 59m
Table of contents
Course Overview
Introducing Numerical Optimization
Understanding Linear Programming
Implementing Linear Programming in Excel
Implementing Linear Programming in R
Implementing Linear Programming in Python
Understanding Integer Programming
Implementing Integer Programming in Excel
Implementing Integer Programming in R
Implementing Integer Programming in Python
Description
Course info
Rating
(14)
Level
Intermediate
Updated
May 4, 2017
Duration
3h 59m
Description

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.

About the author
About the author

An engineer and tinkerer, Vitthal has worked at Google, Credit Suisse, and Flipkart and studied at Stanford and INSEAD. He has worn many hats, each of which has involved writing code and building models. He is passionately devoted to his hobby of laughing at his own jokes.

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