This repository contains a curated list of JavaScript algorithms, organized by category. These range from simple string manipulation to advanced searching and sorting techniques — perfect for interviews and foundational learning.
Note
Popularity is based on common interview topics, educational materials, and developer community usage.
- Prime Number Check
- Fibonacci Sequence (Recursive)
- Factorial of a Number
- Find the GCD (Greatest Common Divisor)
/**
* Reverses a string
* @param {string} str - The string to reverse
* @returns {string} The reversed string
*/
function reverseString(str) {
if (typeof str !== "string") throw new TypeError("Input must be a string");
return [...str].reverse().join("");
}
console.log(reverseString("hello")); // Output: "olleh"Explanation: Reverses the characters in a string using the spread operator (modern alternative to split) and reverse. Includes type validation.
/**
* Checks if a string is a palindrome (case-insensitive, ignores spaces)
* @param {string} str - The string to check
* @returns {boolean} True if palindrome, false otherwise
*/
function isPalindrome(str) {
if (typeof str !== "string") throw new TypeError("Input must be a string");
const cleaned = str.toLowerCase().replace(/\s+/g, "");
return cleaned === [...cleaned].reverse().join("");
}
console.log(isPalindrome("racecar")); // Output: true
console.log(isPalindrome("A man a plan a canal Panama")); // Output: trueExplanation: Determines if a string reads the same backward as forward. Now handles case-insensitivity and ignores spaces for real-world usage.
/**
* Counts the frequency of each character in a string
* @param {string} str - The string to analyze
* @returns {Object} An object with characters as keys and frequencies as values
*/
function charFrequency(str) {
if (typeof str !== "string") throw new TypeError("Input must be a string");
return [...str].reduce((freq, char) => {
freq[char] = (freq[char] ?? 0) + 1;
return freq;
}, {});
}
console.log(charFrequency("hello")); // Output: { h: 1, e: 1, l: 2, o: 1 }Explanation: Counts how often each character appears in a string using modern reduce() and nullish coalescing (??). More functional approach.
/**
* Determines if two strings are anagrams (ignores case and spaces)
* @param {string} str1 - First string
* @param {string} str2 - Second string
* @returns {boolean} True if anagrams, false otherwise
*/
function isAnagram(str1, str2) {
if (typeof str1 !== "string" || typeof str2 !== "string") {
throw new TypeError("Both inputs must be strings");
}
const normalize = (str) =>
[...str.toLowerCase().replace(/\s+/g, "")].sort().join("");
return normalize(str1) === normalize(str2);
}
console.log(isAnagram("listen", "silent")); // Output: true
console.log(isAnagram("The Eyes", "They See")); // Output: trueExplanation: Determines if two strings are anagrams by normalizing case/spaces and comparing sorted characters. Handles real-world edge cases.
/**
* Checks if a number is prime
* @param {number} num - The number to check
* @returns {boolean} True if prime, false otherwise
*/
function isPrime(num) {
if (!Number.isInteger(num)) throw new TypeError("Input must be an integer");
if (num <= 1) return false;
if (num <= 3) return true;
if (num % 2 === 0 || num % 3 === 0) return false;
for (let i = 5; i * i <= num; i += 6) {
if (num % i === 0 || num % (i + 2) === 0) return false;
}
return true;
}
console.log(isPrime(7)); // Output: trueExplanation: Checks if a number is prime using an optimized approach with integer validation. Eliminates multiples of 2 and 3 for efficiency.
/**
* Generates the nth Fibonacci number using memoization
* @param {number} n - The position in Fibonacci sequence
* @param {Map} memo - Cache for memoization
* @returns {number} The nth Fibonacci number
*/
function fibonacci(n, memo = new Map()) {
if (!Number.isInteger(n) || n < 0) {
throw new TypeError("Input must be a non-negative integer");
}
if (n <= 1) return n;
if (memo.has(n)) return memo.get(n);
const result = fibonacci(n - 1, memo) + fibonacci(n - 2, memo);
memo.set(n, result);
return result;
}
console.log(fibonacci(6)); // Output: 8
console.log(fibonacci(50)); // Output: 12586269025 (efficient with memoization)Explanation: Generates the nth Fibonacci number recursively with memoization for efficiency. Solves the exponential time complexity problem of naive recursion. Time complexity: O(n) instead of O(2^n).
/**
* Calculates the factorial of a number
* @param {number} n - The number to calculate factorial for
* @returns {number} The factorial of n
*/
function factorial(n) {
if (!Number.isInteger(n)) throw new TypeError("Input must be an integer");
if (n < 0)
throw new RangeError("Factorial is not defined for negative numbers");
if (n === 0 || n === 1) return 1;
return n * factorial(n - 1);
}
console.log(factorial(5)); // Output: 120Explanation: Calculates the factorial of a number recursively with comprehensive input validation using modern type-checking.
/**
* Finds the greatest common divisor using Euclidean algorithm
* @param {number} a - First number
* @param {number} b - Second number
* @returns {number} The GCD of a and b
*/
function gcd(a, b) {
if (!Number.isInteger(a) || !Number.isInteger(b)) {
throw new TypeError("Both inputs must be integers");
}
a = Math.abs(a);
b = Math.abs(b);
return b === 0 ? a : gcd(b, a % b);
}
console.log(gcd(48, 18)); // Output: 6
console.log(gcd(-48, 18)); // Output: 6Explanation: Uses the Euclidean algorithm with support for negative numbers and type validation.
/**
* Finds two indices in array whose values sum to target
* @param {number[]} nums - Array of numbers
* @param {number} target - Target sum
* @returns {number[]} Array of two indices, or empty array if not found
*/
function twoSum(nums, target) {
if (!Array.isArray(nums) || typeof target !== "number") {
throw new TypeError("Input must be an array and a number");
}
const map = new Map();
for (let i = 0; i < nums.length; i++) {
const complement = target - nums[i];
if (map.has(complement)) return [map.get(complement), i];
map.set(nums[i], i);
}
return [];
}
console.log(twoSum([2, 7, 11, 15], 9)); // Output: [0, 1]Explanation: Finds two indices whose values sum to target using a Map (hash map) for O(n) time complexity. Includes input validation.
/**
* Searches for target in a sorted array using binary search
* @param {number[]} arr - Sorted array to search in
* @param {number} target - Value to search for
* @returns {number} Index of target, or -1 if not found
*/
function binarySearch(arr, target) {
if (!Array.isArray(arr) || typeof target !== "number") {
throw new TypeError("Input must be an array and a number");
}
let left = 0,
right = arr.length - 1;
while (left <= right) {
const mid = left + Math.floor((right - left) / 2);
if (arr[mid] === target) return mid;
if (arr[mid] < target) left = mid + 1;
else right = mid - 1;
}
return -1;
}
console.log(binarySearch([1, 2, 3, 4, 5], 4)); // Output: 3Explanation: Searches for target in sorted array using divide-and-conquer. Uses left + Math.floor((right - left) / 2) to avoid overflow issues.
/**
* Sorts an array using bubble sort algorithm with early exit optimization
* @param {number[]} arr - Array to sort
* @returns {number[]} Sorted array
* Time Complexity: O(n²) worst/average case, O(n) best case
* Space Complexity: O(n)
*/
function bubbleSort(arr) {
if (!Array.isArray(arr)) throw new TypeError("Input must be an array");
const sorted = [...arr]; // Create copy to avoid mutating original
for (let i = 0; i < sorted.length; i++) {
let swapped = false; // Optimization: early exit if already sorted
for (let j = 0; j < sorted.length - i - 1; j++) {
if (sorted[j] > sorted[j + 1]) {
[sorted[j], sorted[j + 1]] = [sorted[j + 1], sorted[j]];
swapped = true;
}
}
if (!swapped) break; // Array is already sorted
}
return sorted;
}
console.log(bubbleSort([5, 3, 8, 4, 2])); // Output: [2, 3, 4, 5, 8]
console.log(bubbleSort([1, 2, 3, 4, 5])); // Exits early (already sorted)Explanation: Sorts by repeatedly swapping adjacent elements. Optimized with a swapped flag to exit early when the array is already sorted, improving best-case complexity to O(n).
/**
* Sorts an array using in-place quick sort algorithm
* @param {number[]} arr - Array to sort
* @returns {number[]} Sorted array
* Time Complexity: O(n log n) average, O(n²) worst case
* Space Complexity: O(log n) due to recursion stack
*/
function quickSort(arr) {
if (!Array.isArray(arr)) throw new TypeError("Input must be an array");
const sorted = [...arr]; // Work on copy to avoid mutation
quickSortInPlace(sorted, 0, sorted.length - 1);
return sorted;
}
function quickSortInPlace(arr, low, high) {
if (low < high) {
const pi = partition(arr, low, high);
quickSortInPlace(arr, low, pi - 1);
quickSortInPlace(arr, pi + 1, high);
}
}
function partition(arr, low, high) {
const pivot = arr[high];
let i = low - 1;
for (let j = low; j < high; j++) {
if (arr[j] < pivot) {
i++;
[arr[i], arr[j]] = [arr[j], arr[i]];
}
}
[arr[i + 1], arr[high]] = [arr[high], arr[i + 1]];
return i + 1;
}
console.log(quickSort([3, 6, 8, 10, 1, 2, 1])); // Output: [1, 1, 2, 3, 6, 8, 10]Explanation: Divide-and-conquer in-place sorting with Hoare partition scheme. Uses O(log n) space instead of O(n), making it more memory-efficient. Better for interview discussions about space optimization.
/**
* Merges two sorted arrays into one sorted array
* @param {number[]} arr1 - First sorted array
* @param {number[]} arr2 - Second sorted array
* @returns {number[]} Merged sorted array
*/
function mergeSortedArrays(arr1, arr2) {
if (!Array.isArray(arr1) || !Array.isArray(arr2)) {
throw new TypeError("Both inputs must be arrays");
}
const merged = [];
let i = 0,
j = 0;
while (i < arr1.length && j < arr2.length) {
if (arr1[i] < arr2[j]) {
merged.push(arr1[i++]);
} else {
merged.push(arr2[j++]);
}
}
return [...merged, ...arr1.slice(i), ...arr2.slice(j)];
}
console.log(mergeSortedArrays([1, 3, 5], [2, 4, 6])); // Output: [1, 2, 3, 4, 5, 6]Explanation: Merges two sorted arrays efficiently in O(n + m) time. Modern syntax with spread operator.
/**
* Finds the maximum value in an array
* @param {number[]} arr - Array to search
* @returns {number} The maximum value
*/
function findMax(arr) {
if (!Array.isArray(arr) || arr.length === 0) {
throw new TypeError("Input must be a non-empty array");
}
return Math.max(...arr);
}
// Alternative for very large arrays (avoids stack overflow):
function findMaxAlternative(arr) {
if (!Array.isArray(arr) || arr.length === 0) {
throw new TypeError("Input must be a non-empty array");
}
return arr.reduce((max, current) => (current > max ? current : max));
}
console.log(findMax([1, 2, 3, 4, 5])); // Output: 5Explanation: Finds the largest number. Now includes validation and an alternative using reduce() for very large arrays to avoid stack overflow from spread operator.
/**
* Creates a debounced function that delays execution
* @param {Function} fn - Function to debounce
* @param {number} delay - Delay in milliseconds
* @returns {Function} Debounced function
*/
function debounce(fn, delay) {
if (typeof fn !== "function" || typeof delay !== "number") {
throw new TypeError("First argument must be a function, second a number");
}
let timerId;
return function (...args) {
clearTimeout(timerId);
timerId = setTimeout(() => fn.apply(this, args), delay);
};
}
// Modern async version with promises
async function debounceAsync(fn, delay) {
if (typeof fn !== "function" || typeof delay !== "number") {
throw new TypeError("First argument must be a function, second a number");
}
let timerId;
return function (...args) {
return new Promise((resolve) => {
clearTimeout(timerId);
timerId = setTimeout(() => {
resolve(fn.apply(this, args));
}, delay);
});
};
}
const log = debounce(() => console.log("Debounced!"), 300);
log();
log();
log(); // Logs once after 300ms of inactivityExplanation: Limits rate at which a function fires. Classic callback version and modern async/Promise version for modern use cases. Includes parameter validation.
/**
* Node class for LinkedList
*/
class Node {
constructor(data) {
this.data = data;
this.next = null;
}
}
/**
* Singly Linked List implementation
*/
class LinkedList {
constructor() {
this.head = null;
}
/**
* Inserts a value at the beginning
* @param {*} data - Value to insert
* Time Complexity: O(1)
*/
insertAtHead(data) {
const newNode = new Node(data);
newNode.next = this.head;
this.head = newNode;
}
/**
* Searches for a value in the list
* @param {*} data - Value to search for
* @returns {boolean} True if found
* Time Complexity: O(n)
*/
search(data) {
let current = this.head;
while (current) {
if (current.data === data) return true;
current = current.next;
}
return false;
}
/**
* Deletes first occurrence of a value
* @param {*} data - Value to delete
* Time Complexity: O(n)
*/
delete(data) {
if (!this.head) return;
if (this.head.data === data) {
this.head = this.head.next;
return;
}
let current = this.head;
while (current.next) {
if (current.next.data === data) {
current.next = current.next.next;
return;
}
current = current.next;
}
}
/**
* Reverses the linked list in-place
* Time Complexity: O(n)
*/
reverse() {
let prev = null;
let current = this.head;
while (current) {
const next = current.next;
current.next = prev;
prev = current;
current = next;
}
this.head = prev;
}
/**
* Converts list to array for easy viewing
* @returns {Array}
*/
toArray() {
const result = [];
let current = this.head;
while (current) {
result.push(current.data);
current = current.next;
}
return result;
}
}
// Usage
const list = new LinkedList();
list.insertAtHead(3);
list.insertAtHead(2);
list.insertAtHead(1);
console.log(list.toArray()); // Output: [1, 2, 3]
console.log(list.search(2)); // Output: true
list.reverse();
console.log(list.toArray()); // Output: [3, 2, 1]Explanation: Fundamental data structure with insert, search, delete, and reverse operations. Essential for interviews to understand pointers and node traversal.
/**
* Stack implementation (Last-In-First-Out)
* Time Complexity: O(1) for push/pop/peek
* Space Complexity: O(n)
*/
class Stack {
constructor() {
this.items = [];
}
/**
* Adds element to top of stack
* @param {*} element - Value to push
*/
push(element) {
this.items.push(element);
}
/**
* Removes and returns element from top
* @returns {*} Removed element or undefined
*/
pop() {
return this.items.length === 0 ? undefined : this.items.pop();
}
/**
* Views top element without removing
* @returns {*} Top element or undefined
*/
peek() {
return this.items.length === 0
? undefined
: this.items[this.items.length - 1];
}
/**
* Checks if stack is empty
* @returns {boolean}
*/
isEmpty() {
return this.items.length === 0;
}
/**
* Returns size of stack
* @returns {number}
*/
size() {
return this.items.length;
}
/**
* Clears the stack
*/
clear() {
this.items = [];
}
/**
* Converts stack to array
* @returns {Array}
*/
toArray() {
return [...this.items];
}
}
// Usage
const stack = new Stack();
stack.push(10);
stack.push(20);
stack.push(30);
console.log(stack.peek()); // Output: 30
console.log(stack.pop()); // Output: 30
console.log(stack.size()); // Output: 2Explanation: LIFO data structure critical for parsing, undo/redo, and function call management. Classic interview topic with applications in parenthesis matching and expression evaluation.
/**
* Queue implementation (First-In-First-Out)
* Time Complexity: O(1) for enqueue/dequeue/peek
* Space Complexity: O(n)
*/
class Queue {
constructor() {
this.items = [];
}
/**
* Adds element to back of queue
* @param {*} element - Value to enqueue
*/
enqueue(element) {
this.items.push(element);
}
/**
* Removes and returns element from front
* @returns {*} Removed element or undefined
*/
dequeue() {
return this.items.length === 0 ? undefined : this.items.shift();
}
/**
* Views front element without removing
* @returns {*} Front element or undefined
*/
peek() {
return this.items.length === 0 ? undefined : this.items[0];
}
/**
* Checks if queue is empty
* @returns {boolean}
*/
isEmpty() {
return this.items.length === 0;
}
/**
* Returns size of queue
* @returns {number}
*/
size() {
return this.items.length;
}
/**
* Clears the queue
*/
clear() {
this.items = [];
}
/**
* Converts queue to array
* @returns {Array}
*/
toArray() {
return [...this.items];
}
}
// Usage
const queue = new Queue();
queue.enqueue(1);
queue.enqueue(2);
queue.enqueue(3);
console.log(queue.peek()); // Output: 1
console.log(queue.dequeue()); // Output: 1
console.log(queue.size()); // Output: 2Explanation: FIFO data structure essential for BFS, task scheduling, and buffering. Note: JavaScript arrays' shift() is O(n), so for production use a circular array or linked-list implementation.
/**
* Depth-First Search traversal
* @param {Object} graph - Adjacency list representation
* @param {string|number} start - Starting vertex
* @returns {Array} Order of visited vertices
* Time Complexity: O(V + E) where V = vertices, E = edges
* Space Complexity: O(V) for recursion stack
*/
function dfs(graph, start) {
if (!graph || !(start in graph)) {
throw new TypeError("Invalid graph or start vertex");
}
const visited = new Set();
const result = [];
function explore(vertex) {
if (visited.has(vertex)) return;
visited.add(vertex);
result.push(vertex);
for (const neighbor of graph[vertex]) {
explore(neighbor);
}
}
explore(start);
return result;
}
// Iterative DFS with explicit stack
function dfsIterative(graph, start) {
if (!graph || !(start in graph)) {
throw new TypeError("Invalid graph or start vertex");
}
const visited = new Set();
const result = [];
const stack = [start];
while (stack.length > 0) {
const vertex = stack.pop();
if (!visited.has(vertex)) {
visited.add(vertex);
result.push(vertex);
// Add neighbors in reverse for left-to-right traversal
for (let i = graph[vertex].length - 1; i >= 0; i--) {
if (!visited.has(graph[vertex][i])) {
stack.push(graph[vertex][i]);
}
}
}
}
return result;
}
// Usage
const graph = {
A: ["B", "C"],
B: ["A", "D"],
C: ["A", "D"],
D: ["B", "C", "E"],
E: ["D"],
};
console.log(dfs(graph, "A")); // Output: ["A", "B", "D", "C", "E"]
console.log(dfsIterative(graph, "A")); // Output: ["A", "C", "D", "E", "B"]Explanation: Explores graph deeply before backtracking. Recursive version is elegant; iterative version avoids stack overflow on large graphs. Used for cycle detection, topological sorting, and connected components.
/**
* Breadth-First Search traversal
* @param {Object} graph - Adjacency list representation
* @param {string|number} start - Starting vertex
* @returns {Array} Order of visited vertices (level-by-level)
* Time Complexity: O(V + E) where V = vertices, E = edges
* Space Complexity: O(V) for queue
*/
function bfs(graph, start) {
if (!graph || !(start in graph)) {
throw new TypeError("Invalid graph or start vertex");
}
const visited = new Set([start]);
const queue = [start];
const result = [];
while (queue.length > 0) {
const vertex = queue.shift();
result.push(vertex);
for (const neighbor of graph[vertex]) {
if (!visited.has(neighbor)) {
visited.add(neighbor);
queue.push(neighbor);
}
}
}
return result;
}
// BFS to find shortest path
function bfsShortestPath(graph, start, end) {
if (!graph || !(start in graph) || !(end in graph)) {
throw new TypeError("Invalid graph or vertices");
}
if (start === end) return [start];
const visited = new Set([start]);
const queue = [[start]];
while (queue.length > 0) {
const path = queue.shift();
const vertex = path[path.length - 1];
for (const neighbor of graph[vertex]) {
if (neighbor === end) {
return [...path, neighbor];
}
if (!visited.has(neighbor)) {
visited.add(neighbor);
queue.push([...path, neighbor]);
}
}
}
return null; // No path found
}
// Usage
const graph = {
A: ["B", "C"],
B: ["A", "D"],
C: ["A", "D"],
D: ["B", "C", "E"],
E: ["D"],
};
console.log(bfs(graph, "A")); // Output: ["A", "B", "C", "D", "E"]
console.log(bfsShortestPath(graph, "A", "E")); // Output: ["A", "C", "D", "E"]Explanation: Explores graph level-by-level, ideal for finding shortest paths and closest elements. Uses queue (FIFO). Less common than DFS for most problems but essential for shortest-path and connectivity queries.