Fibonacci Heap is wildly adopted to implement the famous Dijkstra's algorithm due to its high performance on the decrease_key function. In the Dijkstra's algorithm, V times of insertion, V times of extracting minimum and E times of decreasing key are required, where E is the number of edges and V is the number of vertices. Binary Heap as another priority queue requires a time complexity of O(ElogV+VlogV). Fibonacci Heap reduces the time complexity to O(E+V*logV) as the decrease_key function only costs O(1) amortized time. The following picture shows the time complexity of Fibonacci Heap compared with other priority queues. [1]
Fibonacci Heap is similar to a Binomial Heap. The difference is that Fibonacci Heap adopts the method of lazy-merge and lazy-insert, which saves potential, (a term used in Amortized Analysis). Those saved potentials reduce the time complexity of decrease_key and extract_min in future computations. The detailed analysis will be presented in Chapter 4.
This chapter talks about the structure of a Fibonacci Heap. A Fibonacci Heap is a collection of heap-ordered trees as same as a Binomial Heap. All of the roots of a Fibonacci Heap are in a circular linked list instead of an array. Non-root nodes will also be placed in a circular linked list with all of its siblings. The following two pictures visualize this data structure. [1] (a) shows what the heap looks like and (b) shows how it is implemented using pointers and circular linked list.
This section shows how a Fibonacci Heap is implemented. The heap is abstracted as class FibHeap and its node is abstracted as struct FibHeapNode.
/*File: FibHeap.h*/
struct FibHeapNode
{
int key; // assume the element is int
FibHeapNode* left;
FibHeapNode* right;
FibHeapNode* parent;
FibHeapNode* child;
int degree;
bool mark;
};
class FibHeap {
public:
FibHeapNode* m_minNode;
int m_numOfNodes;
FibHeap(){ // initialize a new and empty Fib Heap
m_minNode = nullptr;
m_numOfNodes = 0;
}
~FibHeap() {
_clear(m_minNode);
}
/* Insert a node with key value new_key
and return the inserted node*/
FibHeapNode* insert(int newKey);
/* Merge current heap with another*/
void merge(FibHeap &another);
/* Return the key of the minimum node*/
int extract_min();
/* Decrease the key of node x to newKey*/
void decrease_key(FibHeapNode* x, int newKey);
/*Delete a specified node*/
void delete_node(FibHeapNode* x);
private:
/*omitted, can be checked in source file*/
};
As shown in the code, a Fibonacci Heap has a pointer to its minimum node, and an integer recording the amount of nodes. Each node has pointers to its right and left sibling(s) as well as its child and parent. A single node may have multiple children, but we only need to point to one of the them since they are all stored in a circular linked list. This is similar to the implementation of a Binomial Heap. Each node also has a degree showing the number of children it has. The mark of the node enables Fibonacci Heap to have a perfect decrease_key function, which costs only O(1) amortized time.
This method insert() is simply completed by creating a new node and adding it into the root linked list. This method accepts one input argument newKey, the key of the new node, and returns the pointer to the newly created node.
The method of insert() is a lazy operation to save potential for decrease_key and extract_min.
The method merge() takes the reference of another FibHeap as the only argument and merges it with the current heap.
When merging H1 and H2, we only need to merge the two root lists and refresh the new minimum node. merge is also an lazy operation.
extract_min() takes no arguments and returns the integer value of the minimum key.
This is the most computationally intensive function in a Fibonacci Heap. In insert() and merge(), nodes are placed in the root list unordered. The task of arrangement is deferred until extract_min() is called. In this method, the minimum node will be deleted from the root list, and its children will then be put into the root list. After this procedure, all nodes in the root list will be traversed and the nodes with the same degree will be merged. This operation will continue until all nodes in the root list have different degrees. The following 3 pictures show how extract_min() works.[1]
The following pseudo-code shows the detailed traversal process in extract_min() method.
/**********************************************************************************
* It can be proved that the node of Fibonacci Heap can have at most [logN] children
* L is an array of pointer which point to trees
* And L(R) is the pointer which points to a tree that has R children(child).
**********************************************************************************/
for ( R = 0; R <= [logN]; R++)
while |LR| >= 2
{
Remove two trees from L(R);
Merge the two trees;
Add the new tree into L(R+1);
}
This method takes a pointer to a FibHeapNode and an integer as input arguments. The integer is used as the new key of the node.
This method changes the key of the input node, cuts the node from its parent and adds the node into the root linked list. The mark of its parent needs to be evaluated. The mark being true indicates the parent node has lost one child before. In this case, the parent node should be cut by the subroutine cascading_cut() and moved to the root list. This process will keep running until a node with a mark being false appears. The number of nodes being cut is 1+c, where c is the number execution of cascading_cut().
This methods takes a pointer to a FibHeapNode as the single input argument and then removes the input node from the heap.
Analyze the time complexity of Fibonacci Heap with an Amortized Analysis method.
[1] The pictures in this article are from https://www.cs.princeton.edu/~wayne/teaching/fibonacci-heap.pdf.