Some Important Linear Data Structures- at a glance
Queue is a specialized data storage structure (Abstract data type). Unlike, arrays access of elements in a Queue is restricted. It has two main operations enqueue and dequeue. Insertion in a queue is done using enqueue function and removal from a queue is done using dequeue function. An item can be inserted at the end (‘rear’) of the queue and removed from the front (‘front’) of the queue. It is therefore, also called First-In-First-Out (FIFO) list. Queue has five properties - capacity stands for the maximum number of elements Queue can hold, size stands for the current size of the Queue, elements is the array of elements, front is the index of first element (the index at which we remove the element) and rear is the index of last element (the index at which we insert the element).
Queue structure is defined with fields capacity, size, *elements (pointer to the array of elements), front and rear.
1. createQueue function– This function takes the maximum number of elements
(maxElements) the Queue can hold as an argument, creates a Queue according to it
and returns a pointer to the Queue.
2. enqueue function - This function takes the pointer to the top of the queue Q and the item
(element) to be inserted as arguments. Check for the emptiness of queue
3. dequeue function - This function takes the pointer to the top of the stack S as an
argument and will then dequeue an element.
4. front function – This function takes the pointer to the top of the queue Q as an argument and returns the front element of the queue Q.
1. Each function runs in O(1) time.
2. It has two basic implementations
Array-based implementation – It’s simple and efficient but the maximum size of
the queue is fixed.
Singly Linked List-based implementation – It’s complicated but there is no limit
on the queue size, it is subjected to the available memory.
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These are the standard sources of the knowledge expected from candidates interviewing at Google, Microsoft, Facebook, Amazon and other startups and top-tier technology companies. The books by Cormen or Sedgewick (a standard part of the undergraduate curriculum) are sufficient for this part of the preparation. Google specially, loves to focus on algorithmic questions.
Tutorials on Sorting- at a glance
Basic Data Structures and Algorithms
Stacks Last In First Out data structures ( LIFO ). Like a stack of cards from which you pick up the one on the top ( which is the last one to be placed on top of the stack ). Documentation of the various operations and the stages a stack passes through when elements are inserted or deleted. C program to help you get an idea of how a stack is implemented in code.
Queues First in First Out data structure (FIFO). Like people waiting to buy tickets in a queue - the first one to stand in the queue, gets the ticket first and gets to leave the queue first. Documentation of the various operations and the stages a queue passes through as elements are inserted or deleted. C Program source code to help you get an idea of how a queue is implemented in code.
Single Linked List A self referential data structure. A list of elements, with a head and a tail; each element points to another of its own kind.
Double Linked List- A self referential data structure. A list of elements, with a head and a tail; each element points to another of its own kind in front of it, as well as another of its own kind, which happens to be behind it in the sequence.
Circular Linked List Linked list with no head and tail - elements point to each other in a circular fashion.
Binary Search Trees A basic form of tree data structures. Inserting and deleting elements in them. Different kind of binary tree traversal algorithms.
Heaps - A tree like data structure where every element is lesser (or greater) than the one above it. Heap formation, sorting using heaps in O(n log n) time.
Height Balanced Trees - Ensuring that trees remain balanced to optimize complexity of operations which are performed on them.
Depth First Search - Traversing through a graph using Depth First Search in which unvisited neighbors of the current vertex are pushed into a stack and visited in that order.
Breadth First Search - Traversing through a graph using Breadth First Search in which unvisited neighbors of the current vertex are pushed into a queue and then visited in that order.
Minimum Spanning Trees: Kruskal Algorithm- Finding the Minimum Spanning Tree using the Kruskal Algorithm which is a greedy technique. Introducing the concept of Union Find.
Minumum Spanning Trees: Prim's Algorithm- Finding the Minimum Spanning Tree using the Prim's Algorithm.
Dijkstra Algorithm for Shortest Paths- Popular algorithm for finding shortest paths : Dijkstra Algorithm.
Floyd Warshall Algorithm for Shortest Paths- All the all shortest path algorithm: Floyd Warshall Algorithm
Bellman Ford Algorithm - Another common shortest path algorithm : Bellman Ford Algorithm.
Dynamic Programming A technique used to solve optimization problems, based on identifying and solving sub-parts of a problem first.
Integer Knapsack problemAn elementary problem, often used to introduce the concept of dynamic programming.
Matrix Chain Multiplication Given a long chain of matrices of various sizes, how do you parenthesize them for the purpose of multiplication - how do you chose which ones to start multiplying first?
Longest Common Subsequence Given two strings, find the longest common sub sequence between them.
Elementary cases : Fractional Knapsack Problem, Task Scheduling - Elementary problems in Greedy algorithms - Fractional Knapsack, Task Scheduling. Along with C Program source code.
Data Compression using Huffman TreesCompression using Huffman Trees. A greedy technique for encoding information.