Introduction

The heapsort algorithm is a sorting algorithm which has time complexity O(n
log n) and does not need additional space. Merge sort has the same time
complexity but usually needs additional space. As opposed to merge sort, the
heap sort algorithm is not stable i.e. it might reverse the order of equal
elements.

The basic program elements of the heapsort algorithm not only work for
sorting. They can be applied to implement priority queues. A priority queue is
a collection of elements where the largest element is accessible very fast.

The basic definitions

Abstract array

The program elements of heapsort work on abstract arrays, i.e. mutable
structures which inherit conformantly from

 deferred class ABSTRACT_ARRAY[G] feature
 content: ghost LIST[G]
 -- List representing the content of the array.
 deferred end
 count: NATURAL
 -- The number of elements in the array
 deferred ensure Result = content.count end
 [] (i:NATURAL): G
 -- The ith element of the array
 require i < count
 deferred ensure Result ~ content[i]
 end
 swap(i,j:NATURAL)
 -- Swap the elements at position `i' and `j'.
 require i<count; j<count deferred ensure
 content = old content.swapped(i,j)
 end
 end

The module

We put all code for heap sorting into a module call “heap_sorter”. The module
defines a class HEAP_SORTER with a formal generic G which has the constraint
that the type G must have an order structure (i.e. a total order).

 -- module: heap_sorter
 immutable class HEAP_SORTER[G:ORDER] end
 ...

The only purpose of the class HEAP_SORTER is to define the formal generic G
and its constraint. With this technique it can be avoided that each function,
procedure and asssertion within the module needs to define its own formal
generic.

Heap structure

The content of the abstract array will be viewed as a binary tree. The root of
the tree is the first element. The element at index position “i” has a left
child at position “2*i+1” and a right child at position “2*i+2”.

The binary tree is depicted in the following drawing.

 l[0]
 / \
 / \
 / \
 l[1] l[2]
 / \ /
 / \ /
 l[3] l[4] l[5] ....
 ...........

Note that each level except the last one of the binary tree is full.

We define some index functions to compute the index of the left and right
child and the index of the parent.

 feature -- Index functions
 left(i:NATURAL): NATURAL ensure Result = 2*i+1 end
 right(i:NATURAL): NATURAL ensure Result = 2*i+2 end
 is_left(i:NATURAL): BOOLEAN ensure Result = (i mod 2 = 1) end
 is_right(i:NATURAL): BOOLEAN ensure Result = (i>0 and i mod 2 = 0) end
 parent(i:NATURAL): NATURAL
 require i>0 ensure Result = (i-1)/2 end
 all(i:NATURAL)
 ensure
 i.is_left => i>0
 i.is_right => i>0
 i.left.is_left
 i.right.is_right
 i.left.parent = i
- i.right.parent = i
 i.is_left => i.parent.left = i
 i.is_right => i.parent.right = i
 end
 end

A sequence has a heap structure if each parent has a higher value than its
both childs. In order to define this property formally we use a ghost
predicate “is_heap”.

 feature
 is_heap(l:LIST[G]): ghost BOOLEAN
 -- Does the list `l' has a heap structure, i.e. each child
 -- is less or equal than its parent?
 ensure
 Result = all(i:NATURAL)
 require 
 0<i
 i<l.count
 ensure
 l[i]<=l[i.parent]
 end
 end
 end

Since the order relation is transitive not only the direct childs of a parent
have lower values but also all the children of the children and so on.

If a sequence has a heap structure then the element with the highest value is
always the first element of the sequence.

Build a heap structure

Initially our array is completely unsorted. Before sorting the array we
rearrange the elements such that the content has a heap structure.

Outline of the algorithm

We need a loop to achieve a heap structure. Therefore we need an invariant
which is easy to establish and to maintain and which together with the exit
condition guarantees the heap structure.

Let us look at the sequence

 [5, 0, 1, 5, 3, 4]

which is represented by the binary tree (2:1 is value 1 at index 2)

 .. 0:5 ..
 / \
 / \
 1:0 2:1
 / \ /
 / \ /
 3:5 4:3 5:4

The leave nodes at index 3, 4 and 5 have already a heap structure because they
have no children. In order to describe this situation we define the predicate
“is_heap_from”.

 feature
 is_heap_from(l:LIST[G], i:NATURAL): ghost BOOLEAN
 -- Are all elements of the list `l' from index `i' on valid
 -- parents of a heap structure?
 ensure
 Result = all(j:NATURAL)
 require
 0 < j
 j < l.count
 i <= j.parent
 ensure
 l[j]<=l[j.parent]
 end
 end
 all(l:LIST[G], c:NATURAL)
 ensure
 (c/2).left >= c
 l.is_heap_from(l.count/2)
 l.is_heap_from(0) => l.is_heap
 l.is_heap_from(i) => l.is_heap_from(i+1)
 end
 end

The predicate “l.is_heap_from(i)” is a good canditate for a loop invariant since
it is valid for “i=l.count/2” and the condition “i=0” implies the goal that
the list has a heap structure. I.e. the procedure “make_heap” has the outline

 make_heap(a:ABSTRACT_ARRAY[G])
 local
 i: NATURAL
 do
 from
 i := a.count/2
 invariant
 i <= a.count
 a.content.is_heap_from(i)
 a.content.is_permutation(old a.content)
 variant
 i
 until
 i = 0
 loop
 i := i-1 -- decrease the variant
 a.sift_down(i,a.count) -- reestablish invariant (see below)
 end
 ensure
 a.content.is_heap
 a.content.is_permutation(old a.content)
 end

Reestablish the invariant: “sift_down”

If the invariant “l.is_heap_from(i)” is valid and we decrease the index “i” we
might temporarily violate the invariant because we add a root element to the
partial heap which is not yet a valid parent for its children because its
value might be less or equal than the left or the right child.

E.g. in the above tree

 .. 0:5 ..
 / \
 / \
 1:0 2:1
 / \ /
 / \ /
 3:5 4:3 5:4

we have “l.is_heap_from(3)” because the elements at index 3, 4 and 5 are valid
parents. However the condition “l.is_heap_from(2) is not valid, because the
value at position 2 is less than the value of its left child at position 5
(the node at position 2 does not have a right child in that case).

This defect can be corrected easily. Just identify the child which has a
greater value than the root and swap the two values. This pushes the defect
one level down the tree. I.e. we have to continue until the node is a valid
parent or until it has no children. This can be easily done with a loop.

In order to describe the invariant we have to find a predicate which describes
a partial heap with one defect position.

 feature -- partial heap with one defect position
 is_nearly_heap_from(l:LIST[G], i,k:NATURAL)
 -- Are all elements of the list `l' from index `i' on except at
 -- position `k' valid parents of a heap structure?
 ensure
 Result = all(j:NATURAL)
 require
 0 < j
 j < l.count
 i <= j.parent
 k /= j.parent
 ensure
 l[j] <= l[j.parent]
 end
 end
 is_valid_parent(p:NATURAL, l:LIST[G]: ghost BOOLEAN
 -- Is the element at position `p' within the list `l' a valid
 -- parent of a heap structure (i.e. left and right child have
 -- lower values)?
 ensure
 Result = ((p.left<l.count => l[p.left]<=l[p])
 and
 (p.right<l.count => l[p.right]<=l[p]))
 end
 all(l:LIST[G], i,k:NATURAL)
 require
 l.is_nearly_heap_from(i,k)
 l.count<=k.left or k.is_valid_parent(l)
 ensure
 l.is_heap_from(i)
 end
 all(l:LIST[G], i:NATURAL)
 ensure
 l.is_heap_from(i+1) => l.is_nearly_heap_from(i,i)
 end
 end

With this definition and assertion we can formulate the procedure “sift_down”.

 feature
 sift_down(a:ABSTRACT_ARRAY[G], i,cnt:NATURAL)
 -- Sift down a defect at position `i' of a partial heap of the
 -- array `a'. Only consider the elements below `cnt'.
 require
 cnt <= a.count
 i < cnt
 a.content.take(cnt).is_heap_from(i+1)
 local
 parent,child: NATURAL
 good_parent: BOOLEAN
 do
 from
 parent,good_parent := i,False
 invariant
 parent < cnt
 a.content.take(cnt).is_nearly_heap_from(i,parent)
 a.content.is_permutation(old a.content)
 good_parent => parent.is_valid_parent(a.content.take(cnt))
 variant
 cnt - parent
 until
 parent.left>=cnt or good_parent
 loop
 child := parent.left
 if child+1<a.count and a[child]<a[child+1]
 -- find the child with the highest value
 then
 child := child+1
 end
 if a[child] > a[parent] then
 a.swap(parent,child) -- Swap if necessary
 else
 good_parent := True
 end
 parent := child
 end
 ensure
 a.content.take(cnt).is_heap_from(i)
 a.content.is_permutation(old a.content)
 end
 end

Sort the heap

If the array has already a heap structure (i.e. all parents have larger values
than their children) it is easy to sort the array.

We maintain the condition that the first part of the array has a heap
structure and the second part of the array is sorted. We describe sortedness
by the following predicate.

 feature
 is_sorted(l:LIST[G]): ghost BOOLEAN
 ensure
 Result = all(a,b:G) [a,b] in l.tail_zipped => a<=b
 end
 end

Then conditions

 a.content.take(i).is_heap
 a.content.drop(i).is_sorted
 all(e,f:G) e in a.content.take(i) => f in a.content.drop(i) => e<=f

are easy to establish with “i=a.count”. The condition “i=0” guarantees that
the whole array is sorted.

If we decrement “i” we violate the second part of the invariant. The condition
can be reestablished by swapping the i-th element with the first (the first is
the largest in the heap and all elements in the heap are smaller than the
elements of the already sorted part).

But this violates the first condition of the invariant because we put an
element at the top of the tree which has usually a too small value to be the
top of the tree. With a call to the procedure “sift_down” it is possible to
reestablish this condition as well.

 feature
 sort_heap(a:ABSTRACT_ARRAY[G])
 require
 a.content.is_heap
 local
 i:NATURAL
 do
 from
 i := a.count
 invariant
 i_1: i <= a.count
 i_2: a.content.take(i).is_heap
 i_3: a.content.drop(i).is_sorted
 i_4: all(e,f:G) e in a.content.take(i) 
 => f in a.content.drop(i) 
 => e<=f
 i_5: a.content.is_permutation(old a.content)
 variant
 i
 until
 i = 0
 loop
 i := i-1 -- decrease variant
 a.swap(0,i) -- reestablish i_3
 a.sift_down(0,i) -- reestablish i_2
 -- i_1, i_4 and i_5 are never violated
 end
 ensure
 a.content.is_sorted
 end
 end

The complete heap sort

 feature
 sort(a:ABSTRACT_ARRAY[G])
 do
 a.make_heap
 a.sort_heap
 ensure
 a.content.is_sorted
 a.content.is_permutation(old a.content)
 end
 end

Priority queue

A priority queue is a sequence of elements which is maintained to have a heap
structure. With this condition it is possible to access the largest element
very fast since it is always the first element.

An empty sequence satisfies the condition of being a heap. Since arrayed
containers allow to insert elements rapidly at the end an insertion violates
the heap condition in a different manner as in the above algorithm
“make_heap”. We do not introduce a defect at the top of the tree, we introduce
a defect at the bottom of the tree.

A single defect in a heap

In order to reestablish the heap condition we need to sift the element up
in the tree until it reaches its appropriate position. The following predicate
describes a singe defect in a heap structure.

 feature
 is_heap_except_child(l:LIST[G], i:NATURAL) ghost BOOLEAN
 -- Does `l' have a heap structure except for the child at `i'?
 ensure
 Result = all(j:NATURAL)
 require
 0 < j
 j < l.count
 j/= i
 ensure
 l[j]<=l[j.parent]
 end
 end
 all(l:LIST[G], e:G)
 ensure
 l.is_heap_except_child(0) => l.is_heap
 l.is_heap => (l+e).is_heap_except_child(l.count)
 end
 all(l:LIST[G], i:NATURAL)
 require
 0 < i
 i < l.count
 l.is_heap_except_child(i)
 l[i] <= l[i.parent]
 ensure
 l.is_heap
 end
 end

The procedure “sift_up”

The procedure “sift_up” accepts an array which has a heap structure when
neglecting the last element. The algorithm consists of a loop which pushes the
defect one level up until there is no longer any defect. I.e. the algorithm
reestablishes the heap condition for the full array including the last element.

 feature
 sift_up(a:ABSTRACT_ARRAY[G])
 require
 0 < a.count
 a.content.without_last.is_heap
 local
 i:NATURAL
 do
 from
 i := a.count-1
 invariant
 i < a.count
 a.content.is_heap_except_child(i)
 a.content.is_permutation(old a.content)
 variant
 i
 until
 i=0 or a[i]<=a[i.parent]
 loop
 a.swap(i.parent,i)
 i := i-1
 end
 ensure
 a.content.is_heap
 a.content.is_permutation(old a.content)
 end
 end

Inserting into and deleting from a priority queue

With the procedures “sift_up” and “sift_down” it is easy to maintain a
priority queue with a arrayed structure.

– Insert: Insert a new element at the end of a valid heap and call “sift_up”
to put it into its proper position.

– Remove the top element: Swap the first and the last element, remove the last
element and sift the new top element down by a call to “sift_down” to put it
in its proper position.

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