jeantimex/javascript-problems-and-solutions

Features

• Data Structures and Algorithms - Implements foundational data structures and classical algorithms in JavaScript for computer science education.
• Data Structure Implementations - Implements core data structures including heaps, tries, linked lists, and trees from scratch.
• Mathematical Computing Libraries - Computes prime numbers, number sequences, bitwise operations, and geometric calculations using programmatic logic.
• Algorithm Practice Collections - Provides a comprehensive collection of algorithmic problems and solutions implemented in plain JavaScript for study and practice.
• Depth-First Search Implementations - Explores deep into graph and tree branches recursively to visit all reachable nodes in order.
• Divide And Conquer Algorithms - Breaks complex problems into smaller subproblems, solves them independently, and combines their results efficiently.
• Breadth-First Search - Explores graph and grid nodes level by level to find shortest paths and connected components.
• Graph Problem Solving - Provides solved programming problems and algorithms covering arrays, strings, matrices, trees, graphs, and dynamic programming.
• Algorithmic Problem Solving - Provides a comprehensive curriculum of solved programming challenges for technical interview preparation.
• Binary Search Algorithms - Locates target values within sorted datasets efficiently by repeatedly dividing the search space in half.
• Dynamic Programming - Breaks complex optimization challenges into overlapping subproblems and caches results to find optimal solutions.
• Array Processing - Provides efficient array processing algorithms for searching, sorting, and transforming sequential numerical data.
• Sorting and Searching Implementations - Demonstrates standard sorting routines and binary search variations for studying computer science.
• Backtracking Algorithms - Explores decision spaces recursively and prunes invalid paths to find all possible solution combinations.
• Sorting Algorithms - Orders elements in collections using fundamental sorting techniques like merge sort, quicksort, and heapsort.
• Coding Interview Preparation - Offers structured programming challenges and study materials designed to prepare developers for technical coding interviews.
• Custom Data Structure Implementations - Implements specialized data collections with custom constraints and performance guarantees.
• String Processing Algorithms - Implements standard pattern matching and text manipulation algorithms to solve character sequence challenges.
• Greedy Algorithms - Finds global optimum solutions by making locally optimal choices at each stage of a problem.
• String Number Adders - Multiplies large numbers represented as strings by processing individual digits without direct numerical conversion.
• Tries - Stores and retrieves string keys efficiently by sharing common prefixes in a tree format.
• Tree Data Structures - Navigates and searches hierarchical binary tree structures efficiently.
• Binary Indexed Trees - Performs efficient range sum queries and element updates in logarithmic time using Fenwick tree structures.
• Binary Search on Answer Space - Applies binary search techniques over numerical ranges to locate optimal solution thresholds efficiently.
• Area and Perimeter Calculations - Computes the total boundary length of a single landmass inside a two-dimensional grid by evaluating connected land cells.
• Geometric Skyline Algorithms - Calculates the combined outer contour silhouette of multiple overlapping rectangular buildings using their given coordinates and heights.
• Graph Analysis Algorithms - Resolves connectivity, shortest path, and cycle detection problems across directed and undirected network graphs efficiently.
• Matrix Pattern Traversal Algorithms - Performs matrix rotations, traversals, and pathfinding operations across grid-based data structures efficiently.
• Greedy - Makes locally optimal choices at each algorithmic stage to achieve global optimum solutions.
• Geometric Algorithms - Calculates the maximum possible area formed by any combination of three coordinate points from a given set on a plane.
• Recursive Tree Traversal Algorithms - Systematically navigates hierarchical node structures using recursive function calls.
• Trapped Water Calculations - Calculates the total volume of water retained inside a two-dimensional elevation map represented by a matrix of cell heights.
• Backtracking Solutions - Explores solution spaces recursively and prunes invalid states to generate combinations and permutations.
• Dynamic Programming Techniques - Solves optimization problems by breaking them into overlapping subproblems and caching intermediate values.
• Linked List Reversals - Performs pointer manipulation operations including node insertions, deletions, reversals, and cycle detections.
• Priority Heaps - Maintains priority-ordered collections using complete binary tree heap structures.
• Priority Queues - Maintains dynamically ordered data streams to retrieve extreme priority elements efficiently.
• Random Sampling - Pulls uniform random samples from data streams or arrays of unknown or large sizes efficiently.

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