# Understanding and Applying Numerical Optimization Techniques

##### By Vitthal Srinivasan
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
(12)
Level
Intermediate Updated
May 4, 2017 Duration
3h 59m 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
(12)
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.