Data structure cheat sheet: Difference between revisions
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<syntaxhighlight lang='cpp'> | <syntaxhighlight lang='cpp'> | ||
void | void max_heapify(Node* A, int size, int start) { | ||
auto largest = start; | auto largest = start; | ||
if(start * 2 < size && A[largest] < A[start * 2]) | if(start * 2 < size && A[largest] < A[start * 2]) | ||
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maxheapify(A, size, largest); | maxheapify(A, size, largest); | ||
} | } | ||
} | |||
</syntaxhighlight> | |||
==== Build a heap ==== | |||
To build a heap, call <code>heapify()</code> n/2 times for all non-leaf nodes. | |||
<syntaxhighlight lang='cpp'> | |||
void build_max_heap(Node* A, int size) { | |||
for(int i = size / 2; i >= 0; --i) { | |||
max_heapify(A, size, i); | |||
} | |||
} | } | ||
</syntaxhighlight> | </syntaxhighlight> | ||
Revision as of 16:48, 25 October 2020
A quick cheat sheet on common algorithms and data structures:
Linear Structures
Linked Lists
Basic Implementation
String
Related Algorithms
Arrays
Sorting
Tree-based Structures
Binary Tree
Property
Traversal
Heap
A (binary) heap is a complete binary tree that keeps a sepcific condition of its nodes: For a max-heap, , for a min-heap, . A heap can be used to maintain a priority queue. A heap is often stored as a continous vector.
Heapify
Heapify is a fundamental operation to keep the heap property when there is a new node inserted into the root.
Complexity: . You can prove that using recusion master theroem.
void max_heapify(Node* A, int size, int start) {
auto largest = start;
if(start * 2 < size && A[largest] < A[start * 2])
largest = start * 2;
if(start * 2 + 1 < size && A[largest] < A[start * 2 + 1])
largest = start * 2 + 1;
if(largest != start) {
swap(A[start], A[largest]);
maxheapify(A, size, largest);
}
}
Build a heap
To build a heap, call heapify() n/2 times for all non-leaf nodes.
void build_max_heap(Node* A, int size) {
for(int i = size / 2; i >= 0; --i) {
max_heapify(A, size, i);
}
}