Julia Language while Loops


  • while cond; body; end
  • break
  • continue


The while loop does not have a value; although it can be used in expression position, its type is Void and the value obtained will be nothing.

Collatz sequence

The while loop runs its body as long as the condition holds. For instance, the following code computes and prints the Collatz sequence from a given number:

function collatz(n)
    while n ≠ 1
        n = iseven(n) ? n ÷ 2 : 3n + 1
    println("1... and 4, 2, 1, 4, 2, 1 and so on")


julia> collatz(10)
1... and 4, 2, 1, 4, 2, 1 and so on

It is possible to write any loop recursively, and for complex while loops, sometimes the recursive variant is more clear. However, in Julia, loops have some distinct advantages over recursion:

  • Julia does not guarantee tail call elimination, so recursion uses additional memory and may cause stack overflow errors.
  • And further, for the same reason, a loop can have decreased overhead and run faster.

Run once before testing condition

Sometimes, one wants to run some initialization code once before testing a condition. In certain other languages, this kind of loop has special do-while syntax. However, this syntax can be replaced with a regular while loop and break statement, so Julia does not have specialized do-while syntax. Instead, one writes:

local name

# continue asking for input until satisfied
while true
    # read user input
    println("Type your name, without lowercase letters:")
    name = readline()

    # if there are no lowercase letters, we have our result!
    !any(islower, name) && break

Note that in some situations, such loops could be more clear with recursion:

function getname()
    println("Type your name, without lowercase letters:")
    name = readline()
    if any(islower, name)
        getname()  # this name is unacceptable; try again
        name       # this name is good, return it

(Although this example is written using syntax introduced in version v0.5, it can work with few modifications on older versions also.)

This implementation of breadth-first search (BFS) on a graph represented with adjacency lists uses while loops and the return statement. The task we will solve is as follows: we have a sequence of people, and a sequence of friendships (friendships are mutual). We want to determine the degree of the connection between two people. That is, if two people are friends, we will return 1; if one is a friend of a friend of the other, we will return 2, and so on.

First, let’s assume we already have an adjacency list: a Dict mapping T to Array{T, 1}, where the keys are people and the values are all the friends of that person. Here we can represent people with whatever type T we choose; in this example, we will use Symbol. In the BFS algorithm, we keep a queue of people to “visit”, and mark their distance from the origin node.

function degree(adjlist, source, dest)
    distances = Dict(source => 0)
    queue = [source]

    # until the queue is empty, get elements and inspect their neighbours
    while !isempty(queue)
        # shift the first element off the queue
        current = shift!(queue)

        # base case: if this is the destination, just return the distance
        if current == dest
            return distances[dest]

        # go through all the neighbours
        for neighbour in adjlist[current]
            # if their distance is not already known...
            if !haskey(distances, neighbour)
                # then set the distance
                distances[neighbour] = distances[current] + 1

                # and put into queue for later inspection
                push!(queue, neighbour)

    # we could not find a valid path
    error("$source and $dest are not connected.")

Now, we will write a function to build an adjacency list given a sequence of people, and a sequence of (person, person) tuples:

function makeadjlist(people, friendships)
    # dictionary comprehension (with generator expression)
    result = Dict(p => eltype(people)[] for p in people)

    # deconstructing for; friendship is mutual
    for (a, b) in friendships
        push!(result[a], b)
        push!(result[b], a)


We can now define the original function:

degree(people, friendships, source, dest) =
    degree(makeadjlist(people, friendships), source, dest)

Now let’s test our function on some data.

const people = [:jean, :javert, :cosette, :gavroche, :éponine, :marius]
const friendships = [
    (:jean, :cosette),
    (:jean, :marius),
    (:cosette, :éponine),
    (:cosette, :marius),
    (:gavroche, :éponine)

Jean is connected to himself in 0 steps:

julia> degree(people, friendships, :jean, :jean)

Jean and Cosette are friends, and so have degree 1:

julia> degree(people, friendships, :jean, :cosette)

Jean and Gavroche are connected indirectly through Cosette and then Marius, so their degree is 3:

julia> degree(people, friendships, :jean, :gavroche)

Javert and Marius are not connected through any chain, so an error is raised:

julia> degree(people, friendships, :javert, :marius)
ERROR: javert and marius are not connected.
 in degree(::Dict{Symbol,Array{Symbol,1}}, ::Symbol, ::Symbol) at ./REPL[28]:27
 in degree(::Array{Symbol,1}, ::Array{Tuple{Symbol,Symbol},1}, ::Symbol, ::Symbol) at ./REPL[30]:1