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 Disjoint Set ADT in Data Structure Binary Tree to Segment Tree Binary Tree to List in Order State Space Tree for 4 Queen Problem Hash Function Booth Algorithm Flowchart What is an Algorithm Master Theorem Top-down and Bottom-up Approach in Data Structure Base Address of Array Insertion into a Max Heap in Data Structure Delete a Node From Linked List Find Loop in Linked List How to implement Forward DNS Lookup Cache? How to Implement Reverse DNS Look Up Cache? Primitive vs non primitive data structure Concurrency Algorithms GCD Using Recursion Priority Queue using Linked List Difference between Array and Structure Conversion of Binary to Gray Code

Base Address of Array

The base address refers to the position of the array's first element in memory. The memory address for any component indicates the specific position in memory where the component is stored.

If you are asking about an array's memory address, remember that the memory address and base address are the same. However, if you are looking for the memory value of an element in an array, we can compute it using the base address.

Base Address of Array/>
<!-- /wp:html -->

<!-- wp:paragraph -->
<p>An array is a homogenous data structure, which implies that we may store comparable types of data on the same array. When we talk about an array, we always assume that each element takes up an identical amount of memory space. Every array is determined using the base address for the array. Essentially, the base address aids in determining the addresses of all array items.</p>
<!-- /wp:paragraph -->

<!-- wp:paragraph -->
<p>Imagine an array A having n items. Every component of the array has a size of y bytes. Again, if the array count begins at 0 (as in the C language), the last member of the array is expressed as A[n-1].</p>
<!-- /wp:paragraph -->

<!-- wp:paragraph -->
<p>Now, returning to the storage address of a single element, suppose it is in the array's x location.</p>
<!-- /wp:paragraph -->

<!-- wp:paragraph -->
<p>A[x} equals the array's initial address multiplied by the number of members preceding x. Size of each element.</p>
<!-- /wp:paragraph -->

<!-- wp:paragraph -->
<p>If we have an array A with 50 members, the starting address is 10764H (H stands for hexadecimal), the size of every component is 2 bits, and if we want to get A[21], we should assume that the array count starts at 0. So to achieve A[21], we need the array number to be 22 . Since, we began to count from 0 rather than 1.</p>
<!-- /wp:paragraph -->

<!-- wp:preformatted -->
<pre class=A[21]= 10764+(22*2) =10764+44 = 10808H

If we know the stored location of a specific element, we may jump straight to that position, which is extremely useful when using pointer variables.

An array comprises a collection of arrays. This means that every element of the two-dimensional array is likewise an array, resulting in a matrix-like structure known as an array with two dimensions in C++.

It's a multidimensional array. Two-dimensional arrays are denoted by the array name[n][m], where n is the total number of rows and m is the number of columns.

Finding the address of any component in an Array

Understanding the method of computing an absolute location for every component of an array with two dimensions requires knowledge of how a two-dimensional in-nature array is kept in memory.

Although a two-dimensional array in C++ appears to be significantly different from a one-dimensional array, its memory storage is fairly similar. The two-dimensional layout of a 2-D array is provided for the convenience of the user in the form of a matrix. These arrays of two dimensions are kept in memory the same way as one-dimensional arrays are.

To store an array with two dimensions in recall, we must first map the items into a 1-dimensional array, which is then stored contiguously in memory for ease of access.

This mapping and subsequent storage of memory can be accomplished by two techniques:

  • Row Main Order
  • Column Main Order

Row Main Order

As the name implies, rows are sorted sequentially in memory. This implies that all of the elements from the initial row are kept in memory first, then all of the elements from the subsequent row, and further along.

As a result, we get a one-dimensional array derived from an array of two dimensions with consecutive rows. The address for each component of array_name[i][j], where i signifies the row number and j specifies the column number (0-based index) for that element, if there is an order will be:

The address of arr[i][j] equals the base address plus the number of items spanning arr[i][j] and arr[0][0] in rows in major order. * The size of a single component in an array.

Because the items are organized in row-major order, and the total number of components spanning arr[i][j] and the initial component of the array are going to be equal to the number of rows preceding the row where this element sits * the number of items in one row + the index of the component in its row.

This is just (i * m + j) for the component arr[i][j], where m equals the total number of columns within the array.

Consequently, the absolute location of any component in row-major order becomes:

  • Address of arr[i][j] = Base address + (i * m + j) * the dimension of one array element.
  • If we want to determine the absolute memory location of an[1][2] in row-major order.
  • In this situation, i=1, j=2, and m=4. Applying anything to the formula yields,
  • The numerical address of a[1][2] equals the base address plus (1*4 + 2)*size.
  • Address of a[1][2] = base address + 6*size.
  • Component a[1][2] appears after 6 items when it is turned into an array with one dimension utilizing row-major ordering.

Column Main Order

Columns are sorted sequentially in memory. This indicates that all the items of the initial column are saved in recall, then every component of the subsequent column, and continuing.

As a result, we may create an array with one dimension from an array with two dimensions by storing columns consecutively. The address for every element array_name[i][j], when i signifies the row number and j specifies the column number (0-based index) for that element, in such an order will be:

  • Address many arr[i][j] = base address plus (number of items spanning arr[i][j] to arr[0][0] in column-major order). * The size of a single component in an array
  • As the elements are arranged in column-major peace, the number of elements among arr[i][j] and the initial component of the set will be equivalent to the "a number for columns before that column in which this component resides * several components in one column + the row number of an element" (because this will be the number of elements kept in this column before this component in column-major order).
  • This is just (j * n + i) every component arr[i][j] when n is the total amount of columns in the array.
  • Consequently, the absolute location of any component in row-major order becomes:
  • Address for arr[i][j] = Base location + (j*n + i) * the dimension of one member in the array
  • If we need to get the absolute memory location of a[1][2] using column-major order.
  • In this situation, i=1, j=2, and n=3. Applying anything to the formula yields,
  • Address of a[1][2] = Base address + (2 * 3 + 1)*size.
  • Address of a[1][2] = base address + 7*size.
  • Component a[1][2] appears after 7 items when it is turned into an array with one dimension using column-major ordering.

Calculating the address for any element in the 1-D Array:

The one-dimensional array is a form of linear array. Accessing its elements requires a single subscript, which might be a row or column index.

Address of A[I] = B + W * (I-LB)
  • I = Subset of the element whose address is to be discovered.
  • B is the base address, W is the storage capacity of a single element in an array (in bytes), and LB is the lower limit or bound of the subscript (if not supplied, assume zero).

Example

Determine the address space of A[1700] if the base address of A[1300 ………… 1900] is 1020 and every component is 2 bytes in memory. 

Solution

Given:

  • Base address: B = 1040
  • The lower limit or lower bound of the subscript LB is 1500
  • A single element can be stored in an array. W = 2 Bytes.
  • A subset of elements whose addresses must be discovered. I = 1900
  • The formula employed is Address of A[I] = B + W * (I - LB).
Address of A[1700] = 1040 + 2 * (1900 - 1500) = 1040 + 2 * (400) = 1040 + 800.

Address of A[1900] = 1840.

Conclusion

  • An array with two dimensions is an array of arrays with each element being an array, resulting in a matrix-like structure.
  • A two-dimensional array's base address serves as a starting point for calculating the addresses of all array items.
  • To store an array with two dimensions in memory, we must first map its elements to a limited array. Then they are contiguously stored in memory for convenient access.

There are two methods for converting the fundamental address of an array with two dimensions into a one-dimensional array for storage:

  • Row Main Order: Rows are memorized in a continuous sequence.
  • Column Main Order: Columns are sequentially sorted in memory.

We may alternatively supply the data set as an array that specifies the row and column sizes.