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Hands-On Data Science with Anaconda

You're reading from   Hands-On Data Science with Anaconda Utilize the right mix of tools to create high-performance data science applications

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Length 364 pages
Edition 1st Edition
Languages
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Authors (2):
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James Yan James Yan
Author Profile Icon James Yan
James Yan
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Toc

Table of Contents (15) Chapters Close

Preface 1. Ecosystem of Anaconda FREE CHAPTER 2. Anaconda Installation 3. Data Basics 4. Data Visualization 5. Statistical Modeling in Anaconda 6. Managing Packages 7. Optimization in Anaconda 8. Unsupervised Learning in Anaconda 9. Supervised Learning in Anaconda 10. Predictive Data Analytics – Modeling and Validation 11. Anaconda Cloud 12. Distributed Computing, Parallel Computing, and HPCC 13. References 14. Other Books You May Enjoy

General issues for optimization problems

There are several issues in optimization. The most important one is how to choose an appropriate objective function. For some cases, the objective function is obvious. Unfortunately, for other cases, it is not that crystal clear. Since choosing a good objective depends on the specific situation, we will discuss it further, but remember that an appropriate objective function might make our task much easier.

In many cases, an inappropriate objective function might cause the following problems:

  • It is difficult to find a feasible solution
  • We might end up with a local solution
  • We might have a corner solution
  • It takes a long time to converge (that is, too much computation time to find a good solution)

Let's look at a convex function; the code and corresponding graph are given here:

x<-seq(-10,10,0.1) 
a<-4 
b<- -2 
c<-10 
y...
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