Julia language: a concise tutorial
  • Introduction
  • Language core
    • 1 - Getting started
    • 2 - Data types
    • 3 - Control flow
    • 4 - Functions
    • 5 - Custom structures
    • 6 - Input - Output
    • 7 - Managing run-time errors (exceptions)
    • 8 - Interfacing Julia with other languages
    • 9 - Metaprogramming
    • 10 - Performance (parallelisation, debugging, profiling..)
    • 11 - Developing Julia packages
  • Useful packages
    • Plotting
    • DataFrames
    • JuMP
    • SymPy
    • Weave
    • LAJuliaUtils
    • IndexedTables
    • Pipe
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  1. Useful packages

JuMP

PreviousDataFramesNextSymPy

Last updated 4 years ago

is an algebraic modelling language for mathematical optimisation problems, similar to GAMS, AMPL or Pyomo.

It is solver-independent. It supports also non-linear solvers, providing them with the Gradient and the Hessian.

provides a commented implementation in JuMP of the classical transport problem found in the GAMS tutorial:

Note: The notebook has been updated to the latest JuMP 0.20

While an updated, expanded and revised version of this chapter is available in "Chapter 10 - Mathematical Libraries" of , this tutorial remains in active development.

JuMP
This notebook
Antonello Lobianco (2019), "Julia Quick Syntax Reference", Apress