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Understanding and Applying Numerical Optimization Techniques

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Duration

3h 59m

level

Intermediate

Course Creator

Vitthal Srinivasan

Last Updated

04-May-17

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.

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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.
Author Name: Vitthal Srinivasan
Author Description:
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

Table of Contents

  • Course Overview
    1min
  • Introducing Numerical Optimization
    30mins
  • Understanding Linear Programming
    42mins
  • Implementing Linear Programming in Excel
    33mins
  • 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
    6mins
  • Implementing Integer Programming in Python
    14mins

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Understanding and Applying Numerical Optimization Techniques
Understanding and Applying Numerical Optimization Techniques
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