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.
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.
Course SyllabusCourse Overview- 1m 40s
—Course Overview 1m 40sIntroducing Numerical Optimization- 30m 43s
—Choices, Trade-offs, and Optimization 6m 24s
—Objectives, Constraints, and Decision Variables 5m 16s
—Optimality and Feasibility 5m 8s
—Applications of Optimization 6m 55s
—An Optimization Case Study 6m 59sUnderstanding Linear Programming- 42m 52sImplementing Linear Programming in Excel- 33m 27sImplementing Linear Programming in R- 29m 35sImplementing Linear Programming in Python- 24m 23sUnderstanding Integer Programming- 38m 34sImplementing Integer Programming in Excel- 16m 43sImplementing Integer Programming in R- 6m 42sImplementing Integer Programming in Python- 14m 42s