In this section, we describe the MeldableHeap, a priority Queue implementation in which the underlying structure is also a heap-ordered binary tree. However, unlike a BinaryHeap in which the underlying binary tree is completely defined by the number of elements, there are no restrictions on the shape of the binary tree that underlies a MeldableHeap; anything goes.
The and operations in a MeldableHeap are implemented in terms of the operation. This operation takes two heap nodes and and merges them, returning a heap node that is the root of a heap that contains all elements in the subtree rooted at and all elements in the subtree rooted at .
The nice thing about a operation is that it can be defined recursively. See Figure 10.4. If either or is , then we are merging with an empty set, so we return or , respectively. Otherwise, assume since, if , then we can reverse the roles of and . Then we know that the root of the merged heap will contain , and we can recursively merge with or , as we wish. This is where randomization comes in, and we toss a coin to decide whether to merge with or :
Node<T> merge(Node<T> h1, Node<T> h2) { if (h1 == nil) return h2; if (h2 == nil) return h1; if (compare(h2.x, h1.x) < 0) return merge(h2, h1); // now we know h1.x <= h2.x if (rand.nextBoolean()) { h1.left = merge(h1.left, h2); h1.left.parent = h1; } else { h1.right = merge(h1.right, h2); h1.right.parent = h1; } return h1; }
In the next section, we show that runs in expected time, where is the total number of elements in and .
With access to a operation, the operation is easy. We create a new node containing and then merge with the root of our heap:
boolean add(T x) { Node<T> u = newNode(); u.x = x; r = merge(u, r); r.parent = nil; n++; return true; }This takes expected time.
The operation is similarly easy. The node we want to remove is the root, so we just merge its two children and make the result the root:
T remove() { T x = r.x; r = merge(r.left, r.right); if (r != nil) r.parent = nil; n--; return x; }Again, this takes expected time.
Additionally, a MeldableHeap can implement many other operations in expected time, including:
The analysis of is based on the analysis of a random walk in a binary tree. A random walk in a binary tree starts at the root of the tree. At each step in the random walk, a coin is tossed and, depending on the result of this coin toss, the walk proceeds to the left or to the right child of the current node. The walk ends when it falls off the tree (the current node becomes ).
The following lemma is somewhat remarkable because it does not depend at all on the shape of the binary tree:
Let denote the size of the root's left subtree, so that is the size of the root's right subtree. Starting at the root, the walk takes one step and then continues in a subtree of size or . By our inductive hypothesis, the expected length of the walk is then
We make a quick digression to note that, for readers who know a little about information theory, the proof of Lemma 10.1 can be stated in terms of entropy.
With this result on random walks, we can now easily prove that the running time of the operation is .
The following theorem summarizes the performance of a MeldableHeap:
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