1.Depth-First Search (DFS): This algorithm explores a graph by traversing as far as possible along each branch before backtracking.
2.Breadth-First Search (BFS): Unlike DFS, BFS explores a graph by systematically visiting all the vertices at the current depth level before moving to the next depth level.
3.Dijkstra's Algorithm: Widely used for finding the shortest path between two vertices in a weighted graph, Dijkstra's algorithm efficiently determines the optimal path.
4.Bellman-Ford Algorithm: Similar to Dijkstra's algorithm, the Bellman-Ford algorithm calculates the shortest path in a graph, even in the presence of negative edge weights.
5.Prim's Algorithm: This algorithm constructs a minimum spanning tree for a weighted undirected graph, ensuring that all vertices are connected with minimum total edge weight.
6.Kruskal's Algorithm: Kruskal's algorithm also finds a minimum spanning tree for a weighted undirected graph, but it does so by adding edges in ascending order of their weights.
7.Floyd-Warshall Algorithm: The Floyd-Warshall algorithm is a highly effective method for determining the shortest path between all pairs of vertices in a weighted network..
8.Topological Sorting: This algorithm arranges the vertices of a directed acyclic graph (DAG) in an order that satisfies all directed edges, ensuring that each edge points from an earlier vertex to a later one.
9.Ford-Fulkerson Algorithm: Specifically designed for finding the maximum flow in a flow network, the Ford-Fulkerson algorithm determines the optimal utilization of resources within a network.
10.A* Algorithm: Combining elements of Dijkstra's algorithm and heuristic search, the A* algorithm effectively determines the shortest path between two vertices by prioritizing the most promising paths.
1.Insertion at the Head: This algorithm inserts a new node at the beginning of the linked list.
2.Insertion at the Tail: Unlike insertion at the head, this algorithm appends a new node at the end of the linked list.
3.Deletion of a Node: This algorithm removes a specific node from the linked list, adjusting the links accordingly.
4.Search: The search algorithm looks for a given value in the linked list and returns true if found, or false if not.
5.Length: This algorithm calculates the length of the linked list, counting the total number of nodes.
6.Reversal: Reversing the order of nodes in the linked list is accomplished by using this algorithm.
7.Merge: The merge algorithm combines two sorted linked lists into a single sorted linked list.
8.Detect Cycle: With this algorithm, one can determine whether there is a cycle (loop) present in the linked list.
9.Remove Duplicates: Removing duplicate elements from a sorted or unsorted linked list can be achieved using this algorithm.
10.Nth Node from the End: This algorithm finds the nth node from the end of the linked list, providing a convenient way to access elements based on their position from the tail.
1.Fibonacci Sequence: The dynamic programming approach to the Fibonacci sequence calculates the sequence efficiently by storing previously computed values to avoid redundant calculations.
2.Longest Common Subsequence (LCS): The LCS algorithm finds the longest subsequence shared between two strings or sequences.
3.Knapsack Problem: This algorithm determines the maximum value that can be obtained by selecting items with given weights and values, without exceeding a specified weight limit.
4.Coin Change: The coin change algorithm calculates the minimum number of coins required to make a given amount of change using a given set of coin denominations.
5.Edit Distance: This algorithm measures the minimum number of operations (insertions, deletions, or substitutions) needed to transform one string into another.
6.Matrix Chain Multiplication: The matrix chain multiplication algorithm identifies the most efficient way to multiply a sequence of matrices, minimizing the total number of scalar multiplications required.
7.Longest Increasing Subsequence (LIS): The LIS algorithm determines the length of the longest increasing subsequence within a given sequence or array.
8.Maximum Subarray Sum: This algorithm finds the maximum sum of a contiguous subarray within a given array.
9.Traveling Salesman Problem (TSP): The dynamic programming approach to the TSP looks for the quickest route that stops in each city precisely once before returning to the beginning location.
10.Subset Sum: The subset sum algorithm determines whether a given set of positive integers has a subset that sums up to a specified target value.
Sorting Algorithms:
1.Bubble Sort: Until the entire array is sorted, this technique examines neighbouring elements frequently and switches them out if they are out of order.
2.Insertion Sort: It builds the final sorted array one item at a time by repeatedly inserting a selected element into its correct position within the sorted portion of the array.
3.Selection Sort: This algorithm divides the array into a sorted and an unsorted portion and repeatedly selects the smallest element from the unsorted portion and places it in its correct position in the sorted portion.
4.Merge Sort: It is a divide-and-conquer algorithm that recursively divides the array into smaller subarrays, sorts them, and then merges them to produce a sorted array.
5.Quick Sort: This algorithm selects a pivot element and partitions the array around it, ensuring that all elements smaller than the pivot are placed to its left and larger elements to its right &The process is repeated recursively on the left and right subarrays.
6.Heap Sort: It creates a binary heap from the array and repeatedly extracts the maximum element from the heap, resulting in a sorted array.
7.Radix Sort: This algorithm sorts elements based on each digit or character position, from the least significant to the most significant, resulting in a fully sorted array.
Searching Algorithms:
8.Linear Search: It sequentially checks each element of the array until the target element is found or the entire array is traversed.
9.Binary Search: This efficient search algorithm is applicable only to sorted arrays and repeatedly divides the search interval in half until the target element is found or determined to be absent.
10.Hashing : It efficiently retrieves and stores elements by mapping their keys to array indices using a hash function.
2.Maximum Path Sum in a Binary Tree: The maximum path sum algorithm finds the maximum sum that can be obtained by traversing from any node to any node in a binary tree.
3.Check if a given array can represent the Preorder Traversal of a Binary Search Tree: This algorithm checks if a given array of numbers can be the preorder traversal of a binary search tree.
4.Check whether a binary tree is a full binary tree or not: The full binary tree check algorithm determines whether a binary tree is a full binary tree, which means each node has either One or two children.
5.Bottom View of a Binary Tree: The bottom view algorithm prints the bottom-most nodes of a binary tree when viewed from the bottom.
6.Print Nodes in the Top View of a Binary Tree: This algorithm prints the top-most nodes of a binary tree when viewed from the top.
7.Remove nodes on root-to-leaf paths of length less than K: The remove nodes algorithm removes nodes on root-to-leaf paths whose length is less than a given K value.
8.Lowest Common Ancestor in a Binary Search Tree: this approach locates the lowest common ancestor (LCA) of two nodes.
9.Check if a binary tree is a subtree of another binary tree: The subtree check algorithm determines whether a given binary tree is a subtree of another binary tree.
10.Reverse alternate levels of a perfect binary tree: This algorithm reverses the nodes at alternate levels of a perfect binary tree, starting from level 1.
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1.Modular Exponentiation: This algorithm efficiently calculates the exponentiation of a number modulo another number, often used in cryptography and number theory.
2.Modular Multiplicative Inverse: This algorithm finds the modular multiplicative inverse of a number modulo another number, which is useful in solving modular equations.
3.Primality Test using Fermat's Method: This algorithm determines whether a given number is prime or composite using Fermat's theorem.
4.Euler's Totient Function: This algorithm calculates Euler's totient function, which counts the number of positive integers less than or equal to a given number that are coprime to it.
5.Sieve of Eratosthenes: This approach effectively locates all prime numbers up to a specified limit by repeatedly designating prime multiples as composite.
6.Convex Hull: The convex hull algorithm finds the smallest convex polygon that encloses a given set of points in the plane.
7.Basic and Extended Euclidean Algorithms: These algorithms find the greatest common divisor (GCD) of two numbers and compute the coefficients of Bézout's identity, which are useful in solving linear Diophantine equations.
8.Segmented Sieve: The segmented sieve algorithm extends the Sieve of Eratosthenes to find prime numbers in a specified range efficiently.
9.Chinese Remainder Theorem: This algorithm solves a system of congruences with pairwise coprime moduli, finding a unique solution modulo the product of the moduli.
10.Lucas Theorem: Lucas' theorem is used to efficiently calculate binomial coefficients modulo a prime number using properties of Lucas sequences.
1.Maximum Subarray XOR: This algorithm finds the maximum XOR value of a subarray within an array of integers.
2.Magic Number: The magic number algorithm determines whether a given number is a magic number, which is a positive integer that results in 1 when a specific set of operations is repeatedly applied.
3.Sum of Bit Differences among all Pairs: This algorithm calculates the sum of bit differences between all possible pairs of integers in an array.
4.Swap All Odds and Even Bits: This algorithm swaps all odd-positioned bits with their adjacent even-positioned bits in a given number.
5.Find the Element that Appears Once: This algorithm finds the element that appears only once in an array where all other elements appear twice.
6.Binary Representation of a Given Number: This algorithm converts a given decimal number into its binary representation.
7.Count Total Set Bits in All Numbers from 1 to N: The algorithm counts the total number of set bits (1s) in the binary representation of all numbers from 1 to a given number N.
8.Rotate Bits of a Number: This algorithm rotates the bits of a given number by a specified number of positions.
9.Count Number of Bits to be Flipped to Convert A to B: The algorithm counts the number of bits that need to be flipped in order to convert one integer A to another integer B.
10.Find Next Sparse Number: This algorithm finds the next sparse number, which is a number that does not have consecutive set bits in its binary representation.
1.Reverse an Array without Affecting Special Characters: This algorithm reverses an array of characters while keeping the special characters in their original positions.
2.All Possible Palindromic Partitions: The algorithm finds all possible partitions of a string such that each partition is a palindrome.
3.Count Triplets with Sum Smaller than a Given Value: This algorithm counts the number of triplets in an array whose sum is smaller than a given value.
4.Convert Array into Zig-Zag Fashion: The algorithm rearranges the elements of an array in a zig-zag pattern, such that the sequence of elements alternates between increasing and decreasing order.
5.Generate All Possible Sorted Arrays from Alternate Elements of Two Given Sorted Arrays: This algorithm generates all possible sorted arrays by taking alternate elements from two given sorted arrays.
6.Pythagorean Triplet in an Array: The algorithm finds a Pythagorean triplet (three numbers that satisfy the Pythagorean theorem) in an array.
7.Length of the Largest Subarray with Contiguous Elements: This algorithm finds the length of the largest subarray in an array where the elements are contiguous.
8.Find the Smallest Positive Integer Value that Cannot be Represented as the Sum of any Subset of a Given Array: The algorithm finds the smallest positive integer that cannot be obtained as the sum of any subset of elements from a given array.
9.Smallest Subarray with Sum Greater than a Given Value: This algorithm finds the length of the smallest subarray in an array whose sum is greater than a given value.
10.Stock Buy Sell to Maximize Profit: The algorithm determines the maximum profit that can be obtained by buying and selling stocks from an array of stock prices.