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Learning Julia

You're reading from   Learning Julia Build high-performance applications for scientific computing

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781785883279
Length 316 pages
Edition 1st Edition
Languages
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Authors (2):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
 Lakhanpal Lakhanpal
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Lakhanpal
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Table of Contents (17) Chapters Close

Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Understanding Julia's Ecosystem FREE CHAPTER 2. Programming Concepts with Julia 3. Functions in Julia 4. Understanding Types and Dispatch 5. Working with Control Flow 6. Interoperability and Metaprogramming 7. Numerical and Scientific Computation with Julia 8. Data Visualization and Graphics 9. Connecting with Databases 10. Julia’s Internals

Optimization


In mathematics and computer science, an optimization problem is a problem of finding the best solution from all the feasible solutions. They can broadly be divided into two categories depending upon the variables:

  • Continuous (continuous optimization problem)
  • Discrete (combinatorial optimization problem)

Some of the problems that can be categorized as optimization problems are given here:

  • Shortest path
  • Maximum flow through a network
  • Vehicle routing

Julia, in particular, provides a number of optimization packages, the group of which is collectively called as JuliaOpt. The two most notable packages used are:

  • JuMP (Julia for Mathematical Programming)
  • Convex.jl

Both of these are Algebraic modeling languages (AMLs), which sit over MathProgBase.jl.

JuMP

JuMP is an AML implemented in Julia. Readers coming from a Python background may be familiar with PuLP. It currently supports several open source solvers for a wide variety of problem cases. Some of its features include:

  • User friendliness
  • Speed
  • Solver...
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