Algorithm

Getting Started with AlgorithmWhat is an Algorithm?

Characteristics of Algorithm1 Topic

Analysis Framework

Performance Analysis3 Topics

Mathematical Analysis2 Topics

Sorting AlgorithmSorting Algorithm10 Topics

Searching Algorithm6 Topics

Fundamental of Data StructuresStacks

Queues

Graphs

Trees

Sets

Dictionaries

Divide and ConquerGeneral Method

Binary Search

Recurrence Equation for Divide and Conquer

Finding the Maximum and Minimum

Merge Sort

Quick Sort

Stassen’s Matrix Multiplication

Advantages and Disadvantages of Divide and Conquer

Decrease and ConquerInsertion Sort

Topological Sort

Greedy MethodGeneral Method

Coin Change Problem

Knapsack Problem

Job Sequencing with Deadlines

Minimum Cost Spanning Trees2 Topics

Single Source Shortest Paths1 Topic

Optimal Tree Problem1 Topic

Transform and Conquer Approach1 Topic

Dynamic ProgrammingGeneral Method with Examples

Multistage Graphs

Transitive Closure1 Topic

All Pairs Shortest Paths6 Topics

BacktrackingGeneral Method

NQueens Problem

Sum of Subsets problem

Graph Coloring

Hamiltonian Cycles

Branch and Bound2 Topics

0/1 Knapsack problem2 Topics

NPComplete and NPHard Problems1 Topic
Participants2253
Jump Search
Jump search technique also works for ordered lists. It creates a block and tries to find the element in that block. If the item is not in the block, it shifts the entire block. The block size is based on the size of the list. If the size of the list is n then block size will be √n. After finding a correct block it finds the item using a linear search technique. The jump search lies between linear search and binary search according to its performance.
Lets consider a sorted array A[]
of size n
, with indexing ranging between 0 and n1, and element x that needs to be searched in the array A[]
. For implementing this algorithm, a block of size m is also required, that can be skipped or jumped in every iteration. Thus, the algorithm works as follows:
 Iteration 1: if (
x==A[0]
), then success, else, if (x > A[0]
), then jump to the next block.  Iteration 2: if (
x==A[m]
), then success, else, if (x > A[m]
), then jump to the next block.  Iteration 3: if (
x==A[2m]
), then success, else, if (x > A[2m]
), then jump to the next block.  At any point in time, if (
x < A[km]
), then a linear search is performed from indexA[(k1)m]
toA[km]
What is the optimal block size to be skipped?
In the worst case, we have to do n/m jumps and if the last checked value is greater than the element to be searched for, we perform m1 comparisons more for linear search. Therefore the total number of comparisons in the worst case will be ((n/m) + m1). The value of the function ((n/m) + m1) will be minimum when m = √n. Therefore, the best step size is m = √n.
Jump search algorithm, also called as block search algorithm. Only sorted list of array or table can alone use the Jump search algorithm. In jump search algorithm, it is not at all necessary to scan every element in the list as we do in linear search algorithm. We just check the R element and if it is less than the key element, then we move to the R + R element, where all the elements between R element and R + R element are skipped. This process is continued until R element becomes equal to or greater than key element called boundary value. The value of R is given by R = sqrt(n), where n is the total number of elements in an array. Once the R element attain the boundary value, a linear search is done to find the key value and its position in the array. It must be noted that in Jump search algorithm, a linear search is done in reverse manner that is from boundary value to previous value of R.
Steps for Jump Search Algorithms:
Step 1: Set i=0
and m = √n
.
Step 2: Compare A[i]
with item
. If A[i] != item
and A[i] < item
, then jump to the next block. Also, do the following:
 Set i = m
 Increment
m
by√n
Step 3: Repeat the step 2 till m < n1
Step 4: If A[i] > item
, then move to the beginning of the current block and perform a linear search.
 Set x = i
 Compare
A[x]
withitem
. IfA[x]== item
, then printx
as the valid location else setx++
 Repeat Step 4.1 and 4.2 till x < m
Step 5: Exit
Let us trace the above algorithm using an example:
Consider the following inputs:
A[]
= {0, 1, 1, 2, 3, 5, 8, 13, 21, 55, 77, 89, 101, 201, 256, 780}item
= 77
Step 1: m = √n = 4 (Block Size)
Step 2: Compare A[0]
with item
. Since A[0] != item and A[0]<item, skip to the next block
Figure 2: Comparing A[0] and item
Step 3: Compare A[3] with item. Since A[3] != item and A[3]<item, skip to the next block
Figure 3: Comparing A[3] and item
Step 4: Compare A[6] with item. Since A[6] != itemand A[6]<item, skip to the next block
Figure 4: Comparing A[6] and item
Step 5: Compare A[9] with item. Since A[9] != itemand A[9]<item, skip to the next block
Figure 5: Comparing A[9] and item
Step 6: Compare A[12] with item. Since A[12] != item and A[12] >item, skip to A[9] (beginning of the current block) and perform a linear search.
Figure 6: Comparing A[12] and item
Figure 7: Comparing A[9] and item (Linear Search)
 Compare A[9] with item. Since A[9] != item, scan the next element
 Compare A[10] with item. Since A[10] == item, index 10 is printed as the valid location and the algorithm will terminate
Figure 8: Comparing A[10] and item (Linear Search)
Example – Jump Search
In 16 elements of array, we need to find our key element 7 using jump search algorithm.
step 1: Find the value of R. here R = sqrt (16) i.e) R = 4.
step 2: Skip the first three elements(1, 2, 3) in the array and check whether fourth(4) value is equal to or greater than key value(7).
step 3: If not skip next three elements(5, 6, 7) in the array and check whether eighth(8) value is equal to or greater than key value(7). In this case it is greater than Key value.
step 4: Now by using linear search algorithm, move reverse from value 8(boundary value) to value 4(previous value) to find the key value(7).
step 5: Thus using linear search algorithm the Key value is calculated and resulted in position array[6].
The complexity of Jump Search Technique
 Time Complexity: O(√n)
 Space Complexity: O(1)
Input and Output
Input: A sorted list of data: 10 13 15 26 28 50 56 88 94 127 159 356 480 567 689 699 780 850 956 995 The search key 356 Output: Item found at location: 11
Algorithm
jumpSearch(array, size, key)
Input: An sorted array, size of the array and the search key
Output: location of the key (if found), otherwise wrong location.
Begin
blockSize := √size
start := 0
end := blockSize
while array[end] <= key AND end < size do
start := end
end := end + blockSize
if end > size – 1 then
end := size
done
for i := start to end 1 do
if array[i] = key then
return i
done
return invalid location
End
Jump Search Program in C
#include<iostream>
#include<cmath>
using namespace std;
int jumpSearch(int array[], int size, int key) {
int start = 0;
int end = sqrt(size); //the square root of array length
while(array[end] <= key && end < size) {
start = end; //it it is not correct block then shift block
end += sqrt(size);
if(end > size  1)
end = size; //if right exceeds then bound the range
}
for(int i = start; i<end; i++) { //perform linear search in selected block
if(array[i] == key)
return i; //the correct position of the key
}
return 1;
}
int main() {
int n, searchKey, loc;
cout << "Enter number of items: ";
cin >> n;
int arr[n]; //create an array of size n
cout << "Enter items: " << endl;
for(int i = 0; i< n; i++) {
cin >> arr[i];
}
cout << "Enter search key to search in the list: ";
cin >> searchKey;
if((loc = jumpSearch(arr, n, searchKey)) >= 0)
cout << "Item found at location: " << loc << endl;
else
cout << "Item is not found in the list." << endl;
}
Output :
Enter number of items: 20
Enter items:
10 13 15 26 28 50 56 88 94 127 159 356 480 567 689 699 780 850 956 995
Enter search key to search in the list: 356
Item found at location: 11
Advantages – Jump Search
 Jump search algorithm is more efficient in case of finding a element 600 out of 625 elements in an array.
 Jump search algorithm takes 25 iteration to find a element 600 out of 625 elements in an array.
 Whereas Linear search algorithm takes 600 iteration to find a element 600 out of 625 elements in an array.
 Whereas Binary search algorithm takes 19 iteration to find a element 600 out of 625 elements in an array but complexity in calculation is very tough as compared to jump search algorithm.
Disadvantages – Jump Search
 Jump search algorithm is not preferable for unsorted list or array.
 Executing time of Binary search algorithm is 0 (sqrt (n)).