Data Structures Tutorial

Data Structures Tutorial Asymptotic Notation Structure and Union Array Data Structure Linked list Data Structure Type of Linked list Advantages and Disadvantages of linked list Queue Data Structure Implementation of Queue Stack Data Structure Implementation of Stack Sorting Insertion sort Quick sort Selection sort Heap sort Merge sort Bucket sort Count sort Radix sort Shell sort Tree Traversal of the binary tree Binary search tree Graph Spanning tree Linear Search Binary Search Hashing Collision Resolution Techniques

Misc Topic:

Priority Queue in Data Structure Deque in Data Structure Difference Between Linear And Non Linear Data Structures Queue Operations In Data Structure About Data Structures Data Structures Algorithms Types of Data Structures Big O Notations Introduction to Arrays Introduction to 1D-Arrays Operations on 1D-Arrays Introduction to 2D-Arrays Operations on 2D-Arrays Strings in Data Structures String Operations Application of 2D array Bubble Sort Insertion Sort Sorting Algorithms What is DFS Algorithm What Is Graph Data Structure What is the difference between Tree and Graph What is the difference between DFS and BFS Bucket Sort Dijkstra’s vs Bellman-Ford Algorithm Linear Queue Data Structure in C Stack Using Array Stack Using Linked List Recursion in Fibonacci Stack vs Array What is Skewed Binary Tree Primitive Data Structure in C Dynamic memory allocation of structure in C Application of Stack in Data Structures Binary Tree in Data Structures Heap Data Structure Recursion - Factorial and Fibonacci What is B tree what is B+ tree Huffman tree in Data Structures Insertion Sort vs Bubble Sort Adding one to the number represented an array of digits Bitwise Operators and their Important Tricks Blowfish algorithm Bubble Sort vs Selection Sort Hashing and its Applications Heap Sort vs Merge Sort Insertion Sort vs Selection Sort Merge Conflicts and ways to handle them Difference between Stack and Queue AVL tree in data structure c++ Bubble sort algorithm using Javascript Buffer overflow attack with examples Find out the area between two concentric circles Lowest common ancestor in a binary search tree Number of visible boxes putting one inside another Program to calculate the area of the circumcircle of an equilateral triangle Red-black Tree in Data Structures Strictly binary tree in Data Structures 2-3 Trees and Basic Operations on them Asynchronous advantage actor-critic (A3C) Algorithm Bubble Sort vs Heap Sort Digital Search Tree in Data Structures Minimum Spanning Tree Permutation Sort or Bogo Sort Quick Sort vs Merge Sort Boruvkas algorithm Bubble Sort vs Quick Sort Common Operations on various Data Structures Detect and Remove Loop in a Linked List How to Start Learning DSA Print kth least significant bit number Why is Binary Heap Preferred over BST for Priority Queue Bin Packing Problem Binary Tree Inorder Traversal Burning binary tree Equal Sum What is a Threaded Binary Tree? What is a full Binary Tree? Bubble Sort vs Merge Sort B+ Tree Program in Q language Deletion Operation from A B Tree Deletion Operation of the binary search tree in C++ language Does Overloading Work with Inheritance Balanced Binary Tree Binary tree deletion Binary tree insertion Cocktail Sort Comb Sort FIFO approach Operations of B Tree in C++ Language Recaman’s Sequence Tim Sort Understanding Data Processing Applications of trees in data structures Binary Tree Implementation Using Arrays Convert a Binary Tree into a Binary Search Tree Create a binary search tree Horizontal and Vertical Scaling Invert binary tree LCA of binary tree Linked List Representation of Binary Tree Optimal binary search tree in DSA Serialize and Deserialize a Binary Tree Tree terminology in Data structures Vertical Order Traversal of Binary Tree What is a Height-Balanced Tree in Data Structure Convert binary tree to a doubly linked list Fundamental of Algorithms Introduction and Implementation of Bloom Filter Optimal binary search tree using dynamic programming Right side view of binary tree Symmetric binary tree Trim a binary search tree What is a Sparse Matrix in Data Structure What is a Tree in Terms of a Graph What is the Use of Segment Trees in Data Structure What Should We Learn First Trees or Graphs in Data Structures All About Minimum Cost Spanning Trees in Data Structure Convert Binary Tree into a Threaded Binary Tree Difference between Structured and Object-Oriented Analysis FLEX (Fast Lexical Analyzer Generator) Object-Oriented Analysis and Design Sum of Nodes in a Binary Tree What are the types of Trees in Data Structure What is a 2-3 Tree in Data Structure What is a Spanning Tree in Data Structure What is an AVL Tree in Data Structure Given a Binary Tree, Check if it's balanced B Tree in Data Structure Convert Sorted List to Binary Search Tree Flattening a Linked List Given a Perfect Binary Tree, Reverse Alternate Levels Left View of Binary Tree What are Forest Trees in Data Structure Compare Balanced Binary Tree and Complete Binary Tree Diameter of a Binary Tree Given a Binary Tree Check the Zig Zag Traversal Given a Binary Tree Print the Shortest Path Given a Binary Tree Return All Root To Leaf Paths Given a Binary Tree Swap Nodes at K Height Given a Binary Tree Find Its Minimum Depth Given a Binary Tree Print the Pre Order Traversal in Recursive Given a Generate all Structurally Unique Binary Search Trees Perfect Binary Tree Threaded Binary Trees Function to Create a Copy of Binary Search Tree Function to Delete a Leaf Node from a Binary Tree Function to Insert a Node in a Binary Search Tree Given Two Binary Trees, Check if it is Symmetric A Full Binary Tree with n Nodes Applications of Different Linked Lists in Data Structure B+ Tree in Data Structure Construction of B tree in Data Structure Difference between B-tree and Binary Tree Finding Rank in a Binary Search Tree Finding the Maximum Element in a Binary Tree Finding the Minimum and Maximum Value of a Binary Tree Finding the Sum of All Paths in a Binary Tree Time Complexity of Selection Sort in Data Structure How to get Better in Data Structures and Algorithms Binary Tree Leaf Nodes Classification of Data Structure Difference between Static and Dynamic Data Structure Find the Union and Intersection of the Binary Search Tree Find the Vertical Next in a Binary Tree Finding a Deadlock in a Binary Search Tree Finding all Node of k Distance in a Binary Tree Finding Diagonal Sum in a Binary Tree Finding Diagonal Traversal of The Binary Tree Finding In-Order Successor Binary Tree Finding the gcd of Each Sibling of the Binary Tree Greedy Algorithm in Data Structure How to Calculate Space Complexity in Data Structure How to find missing numbers in an Array Kth Ancestor Node of Binary Tree Minimum Depth Binary Tree Mirror Binary Tree in Data Structure Red-Black Tree Insertion Binary Tree to Mirror Image in Data Structure Calculating the Height of a Binary Search Tree in Data Structure Characteristics of Binary Tree in Data Structure Create a Complete Binary Tree from its Linked List Field in Tree Data Structure Find a Specified Element in a binary Search Tree Find Descendant in Tree Data Structure Find Siblings in a Binary Tree Given as an Array Find the Height of a Node in a Binary Tree Find the Second-Largest Element in a Binary Tree Find the Successor Predecessor of a Binary Search Tree Forest of a Tree in Data Structure In Order Traversal of Threaded Binary Tree Introduction to Huffman Coding Limitations of a Binary Search Tree Link State Routing Algorithm in Data Structure Map Reduce Algorithm for Binary Search Tree in Data Structure Non-Binary Tree in Data Structure Quadratic Probing Example in Hashing Scope and Lifetime of Variables in Data Structure Separate Chaining in Data Structure What is Dynamic Data Structure Separate Chaining vs Open Addressing Time and Space Complexity of Linear Data Structures Abstract Data Types in Data Structures Binary Tree to Single Linked List Count the Number of Nodes in the Binary Tree Count Total No. of Ancestors in a Binary Search Tree Elements of Dynamic Programming in Data Structures Find cost of tree with prims algorithm in data structures Find Preorder Successor in a Threaded Binary Tree Find Prime Nodes Sum Count in Non-Binary Tree Find the Right Sibling of a Binary Tree with Parent Pointers Find the Width of the Binary Search Tree Forest trees in Data Structures Free Tree in Data Structures Frequently asked questions in Tree Data Structures Infix, Postfix and Prefix Conversion Time Complexity of Fibonacci Series What is Weighted Graph in Data Structure

Comb Sort

Brush sort is a fairly direct orchestrating computation at first arranged by Wlodzimierz Dobosiewicz and Artur Borowy in 1980, later rediscovered (and given the name "Combsort") by Stephen Lacey and Richard Box in 1991.

Comb sort enhances bubble sort similarly that Shellsort enhances inclusion sort.'s "decreasing augmentation sort" definition specifies the term 'brush sort' as picturing iterative passes of the information, "where the teeth of a brush contact;" the previous term is connected to Don Knuth.


The fundamental thought is to wipe out turtles, or little qualities close to the furthest limit of the rundown, since in an air pocket sort these sluggish the arranging down immensely.

Bunnies, huge qualities around the start of the rundown, don't represent an issue in bubble sort.

In bubble sort, when any two components are looked at, they generally have a hole (distance from one another) of 1.

 The fundamental thought of brush sort is that the hole can be considerably more than 1.

The inward circle of air pocket sort, which does the real trade, is changed to such an extent that the hole between traded components goes down (for every emphasis of external circle) in strides of a "shrivel factor" k: [ n/k, n/k2, n/k3, ..., 1 ].

The hole begins as the length of the rundown n being arranged partitioned by the psychologist factor k (by and large 1.3; see beneath) and one pass of the previously mentioned changed bubble sort is applied with that hole.

 Then the hole is separated by the psychologist factor once more, the rundown is arranged with this new hole, and the cycle rehashes until the hole is 1.

 As of now, brush sort keeps utilizing a hole of 1 until the rundown is completely arranged. The last phase of the sort is consequently comparable to an air pocket sort, yet at this point most turtles have been managed, so an air pocket sort will be proficient.

The psychologist factor significantly affects the proficiency of brush sort. k = 1.3 has been recommended as an ideal psychologist factor by the writers of the first article after observational testing on north of 200,000 irregular records. A worth too little dials the calculation back by making superfluously numerous correlations, while a worth too huge neglects to really manage turtles, causing it to require many passes with 1 hole size.

The example of continued arranging passes with diminishing holes is like Shellsort, yet in Shellsort the exhibit is arranged totally each give prior to going to the following littlest hole.

 Brush sort's passes don't totally sort the components.

 This is the explanation that Shellsort hole groupings have a bigger ideal psychologist variable of around 2.2.


function combsort(array input) is
       hole := input.size//Initialize hole size
    contract := 1.3//Set the hole shrivel factor
    arranged := misleading

    circle while arranged = misleading
        // Update the hole an incentive for a next brush
        hole := floor(gap/recoil)
        on the off chance that hole ≤ 1,
            hole := 1
            arranged := valid//If there are no trades this pass, we are finished
        end if

        // A solitary "brush" over the info list
        I := 0
        circle while I + hole < input.size//See Shell sort for a comparable thought
            in the event that input[i] > input[i+gap],
                swap(input[i], input[i+gap])
                arranged := false
                // Assuming this task never occurs inside the circle,
                // then, at that point, there have been no trades and the rundown is arranged.
             end if

             I := I + 1
         end circle
     end circle
end function


//python code
from math import floor
def combsort_inplace(data):
    length = len(data)
    shrink = 1.3
    hole = length
    arranged = False
    while not arranged:
        hole = floor(gap/shrivel)
        if hole <= 1:
            arranged = True
            hole = 1
        # comparable to 'I = 0; while (I + hole) < length: ...{loop body}... I += 1'
        for I in range(length - hole):
            sm = hole + i
            in the event that data[i] > data[sm]:
                # since Python is exceptionally great, this achieves the trade
                data[i], data[sm] = data[sm], data[i]
                arranged = False

def combsort(data):
    length = len(data)
    shrink = 1.3
    hole = length
    out = list(data)
    is_sorted = False
    while not is_sorted:
        hole = floor(gap/shrivel)
        in the event that hole <= 1:
            is_sorted = True
            hole = 1
        for i in range(length - hole):
            sm = hole + i
            if out[i] > out[sm]:
                out[i], out[sm] = out[sm], out[i]
                is_sorted = False
    return out
  • class: Sorting calculation
  • Information structure:  Array
  • Most pessimistic scenario performance:           O(n^{2})}O(n^{2})[1]
  • Best-case performance :{\displaystyle \Theta (n\log n)}\Theta(n \log n)
  • Normal performance:    {\displaystyle \Omega           (n^{2}/2^{p})}\Omega(n^2/2^p), where p is the quantity of increments[1]
  • Most pessimistic scenario space complexity: O(1)}O(1)

Steps for combo sort Algorithm:

Instate variable hole as the size of the cluster and variable traded as evident.

1.Proclaim the consistent variable SHRINK_FACTOR as 1.3.

2.While the hole isn't 1 or traded is set to true do the accompanying:

3.Set traded as false.

4.Set hole as (int)gap/SHRINK_FACTOR.

5.For each component in the reach 0 to n - hole do the accompanying - if A[i] > A[i+gap], swap(A[i], A[i+gap]) and set traded to valid.

Complexity of Combo Sort Algorithm:

Normal Case

The time intricacy is of the request for [Big Theta]: O(n2/2p) where p is the quantity of additions.

Worst case:

The most pessimistic scenario time intricacy is [Big O]: O(n2).

Best Case

The best-case happens when the cluster is arranged or almost arranged. The best-case time intricacy is [Big Omega]: O(nlogn). It is a critical improvement over the best-case time intricacy of air pocket sort.

Space Complexity

Space Complexity for this calculation is O(n) in light of the fact that the brush sort calculation requires no extra space other than brief factors.


// Java program for implementation of Comb Sort 
class CombSorting
    // To find gap between elements 
    int getNextGap(int gap1) 
        // Shrink gap by Shrink factor 
        Gap1 = (gap1*10)/13; 
        if (gap1 < 1) 
            return 1; 
        return gap1; 
    // Function to sort arr[] using Comb Sort 
    void sort(int arr[]) 
        int n1 = arr.length; 
        // initialize gap 
        int gap = n1; 
        // Initialize swapped as true to make sure that 
        // loop runs 
        boolean swapped = true; 
        // Keep running while gap is more than 1 and last 
        // iteration caused a swap 
        while (gap1 != 1 || swapped == true) 
            // Find next gap 
            Gap1 = getNextGap(gap1); 
            // Initialize swapped as false so that we can 
            // check if swap happened or not 
            swapped = false; 
            // Compare all elements with current gap 
            for (int i=0; i<n-gap; i++) 
                if (arr[i] > arr[i+gap]) 
                    // Swap arr[i] and arr[i+gap] 
                    int temp = arr[i]; 
                    arr[i] = arr[i+gap]; 
                    arr[i+gap] = temp; 
                    // Set swapped 
                    swapped = true; 
    // Driver method 
    public static void main(String args[]) 
        CombSort ob = new CombSort(); 
        int arr[] = {enter list of elements}; 
        System.out.println("sorted array"); 
        for (int i=0; i<arr.length; ++i) 
            System.out.print(arr[i] + " ");