Book Image

Learning Julia

By : Anshul Joshi, Rahul Lakhanpal
Book Image

Learning Julia

By: Anshul Joshi, Rahul Lakhanpal

Overview of this book

Julia is a highly appropriate language for scientific computing, but it comes with all the required capabilities of a general-purpose language. It allows us to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language such as Python. It is perfect for building high-performance and concurrent applications. From the basics of its syntax to learning built-in object types, this book covers it all. This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set. The book presents the fundamentals of programming in Julia and in-depth informative examples, using a step-by-step approach. You will be taken through concepts and examples such as doing simple mathematical operations, creating loops, metaprogramming, functions, collections, multiple dispatch, and so on. By the end of the book, you will be able to apply your skills in Julia to create and explore applications of any domain.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
8
Data Visualization and Graphics

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...