Data Structures & Algorithms

Author: Muhammad Jahanzaib
www.excellium.online
Interview Preparation &
Essential Concepts for
Data Structures & Algorithms

Data structures and algorithms (DSA) - Topics
● Big O notation
● Arrays
● Linked Lists
● Stacks and Queues
● Trees
● Binary Search Trees
● AVL Trees
● Heap and Priority Queues
● Hash Tables
● Graphs
● Breadth First Search
● Depth First Search
● Dijkstra's Algorithm
● Bellman-Ford Algorithm
● Dynamic Programming

Instructions for Learning and Interview Preparation
➢ Read and understand the theory behind each topic before attempting any exercises or problems related to it.
➢ Practice implementing algorithms and data structures using your preferred programming language.
➢ Familiarize yourself with the time and space complexities of each algorithm and data structure.
➢ Solve as many practice problems as possible to gain a deeper understanding of each topic.
➢ Use the interview questions provided to prepare for technical interviews related to each topic.
➢ Stay organized and keep track of your progress to ensure that you cover all the necessary material.
➢ Test your code thoroughly and look for possible edge cases that may break your code
➢ Make sure to practice coding and implementing these concepts on your own, as that is the best way to
solidify your understanding.
➢ Don't get discouraged if you find some of these concepts challenging at first. Remember, it takes time and
effort to become proficient in any skill, so keep practicing and asking questions.
Remember, DSA is a vast and complex topic that takes time and practice to fully understand. Don't get
discouraged if you encounter difficulties, instead, keep practicing and seeking help when needed.
Good luck!

Data structures
A data structure is a way of organizing and storing data in a computer so that it can be accessed and used
efficiently. It defines a set of rules for how data can be stored, manipulated and retrieved.
Key Points
● Data structures provide a way of organizing and storing data in a computer
● They help in efficient data access and manipulation
● They provide a set of rules for how data can be stored, manipulated, and retrieved
● Choosing the right data structure can make programs more efficient and faster
Definition:

Data structures
● Arrays: Used to store a collection of
elements of the same data type in a
contiguous memory block
● Linked lists: Used to store a collection of
elements in a non-contiguous memory
block
● Trees: Used to represent hierarchical
relationships between data
● Hash tables: Used to store and retrieve data
using a key-value mapping
Real world example: Programming examples:

Data structures and algorithms (DSA)
DSA allows developers to write more efficient
code by optimizing performance and reducing
memory usage. It helps in selecting the best
algorithm for a given problem that performs well
for the input size and processing requirements.
Some of the benefits of DSA include:
By using standard data structures and algorithms,
developers can create reusable code that can be
used in multiple projects. This helps in saving
development time and resources.
DSA makes code easier to read and maintain.
When a developer uses a common data
structure or algorithm, it is easier for other
developers to understand and modify the code,
reducing the likelihood of errors.
DSA enables developers to write code that can
handle large amounts of data and still perform
well. It helps in ensuring that the software can
scale to meet future demands.
Efficiency: Reusability:
Maintainability: Scalability:

Big O notation
Key points:
● Big O notation is used to describe the complexity of an algorithm in terms of the number of
operations it performs
● It provides an upper bound on the growth rate of an algorithm's time or space complexity
● It helps in comparing and selecting the most efficient algorithms for a particular problem
● It can be used to describe the best-case, worst-case, or average-case performance of an
algorithm
Definition:
Big O notation is used to describe the complexity of an algorithm in terms of the number of
operations it performs, as the size of the input grows infinitely large. It gives an upper bound on
the growth rate of an algorithm's time or space complexity.

Big O notation
● Finding the maximum element in an array
using linear search has a time complexity of
O(n)
● Sorting an array using the bubble sort
algorithm has a time complexity of O(n^2)
● Finding the shortest path between two nodes
in a graph using Dijkstra's algorithm has a time
complexity of O(E*logV), where E is the
number of edges and V is the number of
vertices
Real world example: Programming example: C++

Big O notation - Interview Questions
1. What is the difference between O(1) and O(n) time complexity?
2. What is the time complexity of binary search and why is it more efficient than linear
search?
3. What is the worst-case time complexity of quicksort and how can it be improved?
4. What is the difference between O(1) space complexity and O(n) space complexity?
5. How does Big O notation help in analyzing and comparing the efficiency of different
algorithms?

Arrays
An array is a collection of elements of the same data
type, stored in contiguous memory locations. Each
element is identified by an index or a subscript, which
starts from 0.
Key points:
● An array is a collection of elements of the same data type
● The elements are stored in contiguous memory locations
● The elements can be accessed using an index or a subscript, which starts from 0
● Arrays can be one-dimensional, two-dimensional, or multi-dimensional
● Arrays in most programming languages are fixed-size, but some languages
support dynamic arrays that can grow or shrink during runtime
Definition:
C++

Arrays
● Storing the temperatures of each day of the
week in an array
● Storing the scores of a student in each subject
in an array
● Storing the pixels of an image in an array
Real world example: Programming example: Javascript

Arrays - Interview Questions
1. What is an array, and how is it different from a linked list?
2. What is the time complexity for accessing an element in an array?
3. What is the time complexity for inserting or deleting an element in the middle of an array?
4. How can you find the maximum (or minimum) element in an array?
5. How can you remove duplicates from an array?
6. Can you explain how to implement binary search on a sorted array?
7. How can you reverse the order of elements in an array?
8. Can you explain how to rotate an array by a given number of positions?
9. How can you find the kth largest (or smallest) element in an unsorted array?
10. Can you explain how to implement a dynamic array?

Linked Lists
Definition:
A linked list is a data structure that consists of a sequence of nodes, where each node contains a data
element and a reference or a pointer to the next node in the sequence. The last node points to null or a
sentinel node to indicate the end of the list.
Key points:
● A linked list is a sequence of nodes, where each node contains a data element and a reference to
the next node
● Each node has a pointer to the next node in the sequence, except for the last node, which points to
null or a sentinel node
● Linked lists can be singly-linked, doubly-linked, or circular-linked
● Insertion and deletion of elements in a linked list can be done efficiently, but random access to
elements is slow
● Linked lists are useful when the size of the data is not known in advance or when efficient insertion
and deletion of elements is required

Linked Lists
● Storing the browser history in a linked list
● Implementing a stack or a queue using a linked list
● Storing a large database of customer information in a
linked list
Real world example:
Deep dive
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Linked Lists - Interview Questions
1. What is a Linked List, and how does it differ from an array?
2. What are the advantages and disadvantages of a Linked List compared to an array?
3. How do you traverse a Linked List?
4. How do you add an element to the beginning of a Linked List?
5. How do you add an element to the end of a Linked List?
6. How do you delete a node from a Linked List?
7. What is the difference between a singly Linked List and a doubly Linked List?
8. How do you reverse a Linked List?
9. What is the time complexity to traverse a Linked List, add or delete a node at the
beginning or end, and search for an element in a Linked List?
10. Can you explain how a Linked List can be used to implement a stack or a queue?

Stacks and Queues
A stack is a data structure that follows the Last-In-First-Out (LIFO) principle, where elements are inserted
and removed from one end called the top. A queue is a data structure that follows the First-In-First-Out
(FIFO) principle, where elements are inserted at one end called the rear and removed from the other end
called the front.
Key points:
● A stack is a Last-In-First-Out (LIFO) data structure
● A queue is a First-In-First-Out (FIFO) data structure
● Stacks and queues can be implemented using arrays or linked lists
● Stacks and queues have a wide range of applications, such as in function calls, expression
evaluation, and job scheduling
● Some common operations performed on stacks are push, pop, and peek, while common operations
performed on queues are enqueue, dequeue, and peek
Definition:

Stacks and Queues
● Storing the web pages visited in a web browser using
a stack
● Implementing a call stack in programming languages
to keep track of function calls
● Implementing a queue for handling incoming
customer service requests
Deep dive
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Stacks and Queues - Interview Questions
1. What is a stack and how does it work?
2. What is the difference between a stack and a queue?
3. Can you implement a stack using a queue? If so, how?
4. Can you implement a queue using a stack? If so, how?
5. What is the time complexity of various operations in a stack and a queue?
6. What is a circular queue and how does it differ from a regular queue?
7. Can you explain the concept of stack overflow and how it can be prevented?
8. Can you explain the concept of queue underflow and how it can be prevented?
9. Can you provide an example of a real-world situation where a stack or a queue might
be used?
10. Can you explain the difference between a priority queue and a regular queue?
11. Can you implement a priority queue using a heap data structure?
12. How do you implement a stack or a queue using a linked list?

Trees
A tree is a non-linear data structure that consists of a set of nodes, where each node has a value or data,
and zero or more child nodes. The nodes are connected by edges, which represent the relationships
between them. The topmost node of a tree is called the root, while the nodes with no children are called
leaves.
Key points:
● A tree is a non-linear data structure that consists of nodes and edges
● Each node has a value or data, and zero or more child nodes
● The topmost node of a tree is called the root, while the nodes with no children are called leaves
● Trees can be binary, ternary, or n-ary, depending on the number of child nodes a node can have
● Trees can be used to represent hierarchical relationships between data, such as file systems,
organization charts, and family trees
Definition:

Trees
● Representing the file system of a computer using a
tree structure
● Storing and organizing the information of a
company's employees in a tree structure
● Representing the hierarchical structure of a website
using a tree structure
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Trees - Interview Questions
1. What is a tree, and what are its essential properties?
2. What is the difference between a binary tree and a binary search tree?
3. Can you explain the process of inserting a node into a binary search tree?
4. How can you determine if a given binary tree is a binary search tree?
5. Can you describe the pre-order, in-order, and post-order traversal methods for a
binary tree?
6. What is the time complexity of finding an element in a binary search tree?
7. Can you explain the concept of balanced trees, and why are they important?
8. What are some examples of balanced trees, and how do they work?
9. How do you perform a level-order traversal of a binary tree?
10. Can you discuss some common applications of trees in computer science and
software development?

Binary Search Trees
A binary search tree (BST) is a binary tree data structure where each node has a value, and the left subtree
of a node contains only values less than the node's value, while the right subtree contains only values
greater than the node's value.
Key points:
● A binary search tree (BST) is a binary tree where each node has a value, and the left subtree
contains only values less than the node's value, while the right subtree contains only values greater
than the node's value
● BSTs can be used to implement efficient searching and sorting algorithms
● The height of a BST determines the time complexity of search, insertion, and deletion operations
● Balanced BSTs, such as AVL trees and Red-Black trees, are designed to ensure that the height of
the tree is minimized, resulting in optimal performance for search, insertion, and deletion operations
Definition:

Binary Search Trees
● Implementing a spell checker using a BST to store
the dictionary
● Implementing a phone book using a BST to store the
names and phone numbers
● Implementing a stock market analysis tool using a
BST to store the stock prices
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Binary Search Trees - Interview Questions
1. What is the time complexity of searching for an element in a binary search tree?
2. What is the difference between a binary search tree and a binary tree?
3. What is the height of a binary search tree?
4. How can you find the minimum and maximum elements in a binary search tree?
5. Can a binary search tree have duplicate elements?
6. How do you delete a node from a binary search tree?
7. What is the time complexity of inserting a node in a binary search tree?
8. What is the difference between in-order, pre-order, and post-order traversal of a
binary search tree?
9. What is the advantage of using a binary search tree over a simple array for searching
elements?
10. What are the applications of binary search trees?

AVL Trees
An AVL tree is a self-balancing binary search tree where the difference between the heights of the left and
right subtrees of any node is at most 1.
Key points:
● An AVL tree is a self-balancing binary search tree where the height difference between the left and
right subtrees of any node is at most 1
● AVL trees are designed to ensure that the height of the tree is minimized, resulting in optimal
performance for search, insertion, and deletion operations
● AVL trees achieve this balance through a process called rotation, where a node and its subtrees are
rotated to maintain balance
● The time complexity of search, insertion, and deletion operations in an AVL tree is O(log n)
Definition:

AVL Trees
● Implementing a compiler using an AVL tree to store
the symbol table
● Implementing a spell checker using an AVL tree to
store the dictionary
● Implementing a file system using an AVL tree to
store the file directories
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AVL Trees - Interview Questions
1. What is an AVL tree, and how does it differ from a binary search tree?
2. Can you explain how AVL trees maintain balance, and what is the significance of maintaining
balance?
3. What is the time complexity of basic operations such as insertion, deletion, and searching in an
AVL tree?
4. Can you discuss some of the advantages and disadvantages of using AVL trees over other tree
structures?
5. How do you determine if an AVL tree is balanced?
6. Can you describe the rotation operations used to maintain balance in AVL trees?
7. How can AVL trees be used to efficiently store and retrieve large amounts of data?
8. Can you explain the process of rebalancing an AVL tree after an insertion or deletion operation?
9. Can you discuss some of the common pitfalls or mistakes that can occur when implementing AVL
trees?
10. Can you compare and contrast AVL trees with other self-balancing tree structures, such as Red-
Black Trees?

Heap and Priority Queues
A heap is a complete binary tree data structure where every parent node is either greater than or equal to
its child nodes (max heap), or every parent node is less than or equal to its child nodes (min heap). A priority
queue is an abstract data type that stores elements and retrieves them in order of priority.
Key points:
● A heap is a complete binary tree where every parent node is either greater than or equal to its child
nodes (max heap), or every parent node is less than or equal to its child nodes (min heap)
● Heaps can be used to implement priority queues, where the elements are stored and retrieved in
order of priority
● The time complexity of inserting an element into a heap is O(log n), while the time complexity of
retrieving the highest or lowest priority element is O(1)
● Heapsort is a sorting algorithm that uses a heap data structure to sort elements in O(n log n) time
complexity
Definition:

Heap and Priority Queues
● Implementing a task scheduler using a priority queue
to schedule tasks based on their priority
● Implementing a network router using a heap to store
and retrieve packets based on their priority
● Implementing a messaging system using a heap to
store and retrieve messages based on their
timestamp
Real world example:
Programming example: C++

Heap and Priority Queues - Interview Questions
1. What is a heap? How is it different from a normal binary tree?
2. What is the time complexity of inserting an element in a heap?
3. What is the time complexity of finding the maximum (or minimum) element in a heap?
4. How can you implement a priority queue using a heap?
5. What is the difference between a min-heap and a max-heap?
6. How can you implement heap sort using a heap?
7. How can you remove an element from a heap?
8. What is the time complexity of removing an element from a heap?
9. Can you explain how a heap can be used to efficiently implement Dijkstra's algorithm?
10. Can you explain the concept of a heapify operation?

Hash Tables
A hash table is a data structure that uses a hash function to map keys to their corresponding values in a
table. The hash function takes the key as input and returns a unique index in the table where the value is
stored.
Key points:
● A hash table is a data structure that uses a hash function to map keys to their corresponding values
in a table
● Hash tables can provide O(1) time complexity for search, insertion, and deletion operations in the
average case, making them very efficient
● A good hash function should produce a uniform distribution of indices to avoid collisions, where two
or more keys are mapped to the same index
● Collisions can be resolved through various techniques, such as separate chaining or open
addressing
● Hash tables are commonly used in database indexing, caching, and implementing associative arrays
Definition:

Hash Tables
● Implementing a spell checker using a hash table to
store the dictionary
● Implementing a web cache using a hash table to
store frequently accessed web pages
● Implementing a user authentication system using a
hash table to store the user credentials
Real world example:
Programming example: C++
Deep dive
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Hash Tables - Interview Questions
1. What is a hash table, and how does it work?
2. What is the time complexity of insertions, deletions, and searches in a hash table?
3. What is a hash function, and how is it used in a hash table?
4. How do collisions occur in hash tables, and how are they handled?
5. What are the advantages of using a hash table over other data structures such as arrays and
linked lists?
6. Can a hash table be used to implement a priority queue? If so, how?
7. How can you efficiently resize a hash table to accommodate more elements?
8. Can you explain the concept of load factor in hash tables, and how does it affect performance?
9. What are some common applications of hash tables?
10. Can you discuss some techniques for designing a good hash function?

Graphs
A graph is a data structure that consists of a set of vertices (nodes) and a set of edges that connect the
vertices. Each edge represents a relationship or connection between two vertices.
Key points:
● A graph is a data structure that consists of a set of vertices (nodes) and a set of edges that connect
the vertices
● Graphs can be directed (edges have a direction) or undirected (edges have no direction)
● Graphs can be weighted (edges have a weight) or unweighted (edges have no weight)
● Graphs can be represented using an adjacency matrix or an adjacency list
● Common algorithms for graph traversal and manipulation include breadth-first search, depth-first
search, Dijkstra's algorithm, and Kruskal's algorithm
Definition:

Graphs
● Representing social networks using a graph, where the
vertices represent people and the edges represent
friendships or relationships
● Representing road networks using a graph, where the
vertices represent intersections or landmarks and the
edges represent roads or pathways
● Representing the internet using a graph, where the vertices
represent websites and the edges represent hyperlinks
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Graphs - Interview Questions
1. What is a graph, and what are the basic components of a graph?
2. Can you explain the difference between a directed graph and an undirected graph?
3. Can you explain the difference between a weighted and an unweighted graph?
4. What is a cycle in a graph? How can you detect cycles in a graph?
5. Can you explain the difference between DFS and BFS traversal algorithms in a graph?
6. Can you describe how you would find the shortest path between two nodes in a graph using
Dijkstra's Algorithm?
7. What is a minimum spanning tree? How can you find the minimum spanning tree of a graph?
8. Can you explain the concept of a topological sort of a graph?
9. How can you detect if a graph is bipartite?
10. Can you describe some applications of graph theory in real-life problems?

Breadth First Search
Breadth-first search (BFS) is an algorithm for traversing or searching a graph or tree data structure. Starting
from a root node, BFS visits all the nodes at the current depth before moving on to nodes at the next
depth.
Key points:
● Breadth-first search is an algorithm for traversing or searching a graph or tree data structure
● BFS visits all the nodes at the current depth before moving on to nodes at the next depth
● BFS can be used to find the shortest path between two nodes in an unweighted graph or tree
● BFS can be implemented using a queue data structure to store the nodes to be visited
● The time complexity of BFS is O(V + E), where V is the number of vertices and E is the number of
edges in the graph
Definition:

Breadth First Search
● Finding the shortest path between two locations on a
map
● Navigating a maze by exploring all the possible paths
in breadth-first order
● Traversing a website by exploring all the pages in
breadth-first order
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Breadth First Search - Interview Questions
1. What is Breadth First Search, and how does it work?
2. What is the time complexity of Breadth First Search?
3. How is Breadth First Search different from Depth First Search?
4. How do you implement Breadth First Search in a graph?
5. Can Breadth First Search be used to find the shortest path in a graph?
6. Can Breadth First Search be used to detect cycles in a graph?
7. What is the significance of using a queue in Breadth First Search?
8. What is the difference between Breadth First Search and Dijkstra's Algorithm?
9. Can you explain how Breadth First Search can be used in finding the connected
components of a graph?
10. Can you discuss a real-world scenario where Breadth First Search can be applied?

Depth First Search
Depth-first search (DFS) is an algorithm for traversing or searching a graph or tree data structure. Starting
from a root node, DFS explores as far as possible along each branch before backtracking.
Key points:
● Depth-first search is an algorithm for traversing or searching a graph or tree data structure
● DFS explores as far as possible along each branch before backtracking
● DFS can be used to find the shortest path between two nodes in a weighted graph or tree
● DFS can be implemented using recursion or a stack data structure to store the nodes to be visited
● The time complexity of DFS is O(V + E), where V is the number of vertices and E is the number of
edges in the graph
Definition:

Depth First Search
● Solving a maze by exploring all the possible paths in
depth-first order
● Finding the exit path in a labyrinth by exploring all the
possible routes in depth-first order
● Searching for a file on a computer by exploring all the
folders in depth-first order
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Depth First Search - Interview Questions
1. What are the advantages and disadvantages of using recursion to implement depth-
first search?
2. How can depth-first search be used to detect cycles in a graph?
3. How can depth-first search be used to find the strongly connected components of a
directed graph?
4. What are the advantages of DFS over BFS?
5. What are the possible applications of DFS in real-world scenarios?
6. Can you explain the time complexity of DFS and how it can be optimized?
7. How does DFS handle cycles in a graph?
8. Can you differentiate between recursive and iterative approaches in implementing
DFS?
9. How do you determine the order of traversal in DFS?
10. What is the difference between pre-order, in-order, and post-order traversal in DFS?
11. Can you explain the concept of backtracking in DFS?
12. How can DFS be used to find the strongly connected components in a graph?

Dijkstra's Algorithm
Dijkstra's algorithm is a shortest path algorithm that computes the shortest path between a source vertex
and all other vertices in a weighted graph with non-negative edge weights. It maintains a priority queue of
vertices, where the vertex with the smallest distance from the source is dequeued and processed first.
Key points:
● Works only on graphs with non-negative edge weights.
● Maintains a set of visited vertices and a priority queue of vertices yet to be visited.
● Updates the distance and path of each adjacent vertex if a shorter path to that vertex is found.
● Continues until the destination vertex is dequeued from the priority queue.
Definition:

Dijkstra's Algorithm
● Dijkstra's algorithm can be used to find the shortest path
between two points on a map, to optimize travel routes
for delivery vehicles, or to determine the fastest way to
transfer data between nodes in a computer network.
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Dijkstra's Algorithm - Interview Questions
1. What is Dijkstra's Algorithm, and how does it work?
2. What is the time complexity of Dijkstra's Algorithm?
3. What is the difference between Dijkstra's Algorithm and A* Algorithm?
4. What is the advantage of using a priority queue in Dijkstra's Algorithm?
5. Can Dijkstra's Algorithm handle negative weight edges? If not, what is the alternative?
6. How does Dijkstra's Algorithm handle graphs with cycles?
7. What is the difference between Dijkstra's Algorithm and the Bellman-Ford Algorithm?
8. Can you explain how Dijkstra's Algorithm can be used to find the shortest path in a
graph with weighted edges?
9. Can you describe a situation where Dijkstra's Algorithm might not be the best solution
for finding the shortest path?
10. Can you discuss an optimization technique that can be used to improve the
performance of Dijkstra's Algorithm on a large graph?

Bellman-Ford Algorithm
The Bellman-Ford algorithm is a shortest path algorithm that is used to find the shortest path between a
source node and every other node in a weighted graph, even if the graph has negative weight edges. The
algorithm works by repeatedly relaxing all edges in the graph until the shortest path is found.
Key points:
● Works for graphs with negative weight edges
● Can detect negative weight cycles
● Slower than Dijkstra's Algorithm for graphs with non-negative weights
● Time complexity of O(V * E) where V is the number of vertices and E is the number of edges
Definition:

Bellman-Ford Algorithm
● The Bellman-Ford algorithm is used in network routing
protocols such as Open Shortest Path First (OSPF) and
Border Gateway Protocol (BGP) to find the shortest path
between routers in a network.
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Bellman-Ford Algorithm - Interview Questions
1. What is the Bellman-Ford Algorithm, and how does it work?
2. What is the time complexity of the Bellman-Ford Algorithm?
3. How does the Bellman-Ford Algorithm handle negative weight edges?
4. What is the difference between the Bellman-Ford Algorithm and Dijkstra's Algorithm?
5. Can you explain how the Bellman-Ford Algorithm can be used to find the shortest
path in a graph with weighted edges?
6. Can you describe a situation where the Bellman-Ford Algorithm might not be the best
solution for finding the shortest path?
7. How does the Bellman-Ford Algorithm handle graphs with cycles?
8. Can you discuss an optimization technique that can be used to improve the
performance of the Bellman-Ford Algorithm on a large graph?
9. How does the Bellman-Ford Algorithm handle graphs with negative weight cycles?
10. Can you give an example of a problem where the Bellman-Ford Algorithm is useful?

Dynamic Programming
Dynamic Programming is a problem-solving technique used in computer programming to solve complex
problems by breaking them down into smaller sub-problems. The method involves solving each sub-
problem only once and storing its solutions in a table, so that if the same sub-problem occurs again, the
solution is already available.
Key points:
● Dynamic Programming is used to solve problems with overlapping sub-problems and optimal
substructure.
● It involves breaking down a problem into smaller sub-problems, solving each sub-problem once,
and storing the solution in a table for future use.
● The technique reduces the time complexity of the algorithm by avoiding redundant computations
and reusing solutions to sub-problems.
● It can be applied to a wide range of problems, such as optimization, pathfinding, and string
matching.
Definition:

Dynamic Programming
● Finding the shortest path in a graph using Dijkstra's
Algorithm
● Knapsack problem: Given a set of items, each with a
weight and a value, determine the maximum value that
can be obtained while keeping the total weight below a
certain limit.
● Longest common subsequence problem: Given two
strings, find the longest subsequence that is present in
both of them.
● Matrix chain multiplication: Given a sequence of
matrices, find the most efficient way to multiply them.
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Dynamic Programming - Interview Questions
1. What is Dynamic Programming?
2. How is Dynamic Programming different from Divide and Conquer?
3. What is memoization, and how is it used in Dynamic Programming?
4. What is the optimal substructure property, and why is it important for Dynamic
Programming?
5. What is the difference between top-down and bottom-up Dynamic Programming
approaches?
6. What is the time complexity of Dynamic Programming algorithms?
7. How do you decide whether to use Dynamic Programming or another algorithmic
technique to solve a problem?
8. Can you explain the Knapsack problem and how Dynamic Programming can be used
to solve it?
9. How can Dynamic Programming be used to find the longest common subsequence
between two strings?
10. Can you explain the concept of state transition in Dynamic Programming?

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