marcosfede/algorithms

Features

• Algorithmic Problem Sets - Provides a comprehensive collection of implementations for fundamental data structures and algorithms for study and interview preparation.
• Algorithm Solutions - Provides a comprehensive collection of algorithmic solutions for competitive programming and technical interview preparation.
• Binary Search Implementations - Implements binary search algorithms for efficient data retrieval in sorted collections.
• Computer Science Curricula - Serves as a repository of reference solutions for common programming challenges and standard architectural patterns.
• Data Structure Implementations - Provides standardized implementations of fundamental data structures like linked lists, heaps, and queues.
• Sorting Algorithms - Provides educational implementations of standard sorting algorithms for study.
• Computer Science Education - Offers foundational learning materials covering core computer science principles, algorithmic problem solving, and software engineering practices.
• Graph Analysis Algorithms - Implements core graph theory algorithms including pathfinding, cycle detection, and spanning tree generation.
• Sequence DP Solutions - Solves sequence-based dynamic programming challenges like longest common subsequence.
• Architectural Pattern Implementations - Provides reference implementations for architectural patterns used in system design.
• Markov State Transition Models - Simulates Markov chain state transitions using defined probability matrices.
• Mathematical Expression Parsing - Parses mathematical strings into executable logic based on operator precedence.
• Encapsulation Patterns - Organizes information into standardized containers like trees and hash tables to provide efficient access and modification patterns.
• Graph Traversal Strategies - Provides systematic graph traversal strategies for pathfinding, connectivity analysis, and cycle detection.
• Hash Table Implementations - Implements hash tables using hash functions to compute indices for efficient key-value retrieval.
• Sliding Window Maximums - Calculates the maximum value within a sliding window using deque-based optimization.
• Competitive Programming Training - Provides structured practice sets and algorithmic challenges designed to improve performance in coding competitions.
• Randomized Data Structures - Maintains collections of unique elements supporting constant-time insertion, deletion, and random retrieval.
• Divide And Conquer Algorithms - Implements divide and conquer algorithms to decompose complex computational problems into manageable sub-problems.
• Graph Connectivity Analysis - Provides analysis of graph connectivity to determine reachability between vertices in directed structures.
• Shortest Path Algorithms - Implements algorithms for finding the shortest path between nodes in graphs by comparing edge counts.
• Sudoku Board Validation - Checks if a partially filled grid follows standard rules by verifying that no numbers repeat within rows, columns, or sub-grids.
• Software Engineering Fundamentals - Provides foundational concepts and methodologies for building, maintaining, and analyzing robust software systems.
• Technical Interview Preparation - Includes resources and practice materials specifically for preparing for technical assessments and coding interviews.
• Tree Data Structures - Provides standardized implementations of tree-based data structures including binary search trees and tries.
• Bitwise Logic Operations - Implements low-level bitwise logic operations for efficient data manipulation and performance optimization.
• Multi-Language Code Collections - Offers a cross-language library providing efficient solutions for sorting, searching, graph traversal, and dynamic programming.
• Arbitrary Base Conversion - Converts integers between arbitrary bases from 2 to 36.
• Shortest Path Grid Calculators - Computes the shortest path from every empty room in a grid to the nearest gate using search traversal.
• Greatest Common Divisor Algorithms - Provides implementations of the extended Euclidean algorithm for finding greatest common divisors and linear combination coefficients.
• Linked List Cycle Detectors - Identifies whether a linked list contains a cycle by traversing the structure and checking for repeated nodes.
• Iterative Search Strategies - Locates specific elements within datasets by repeatedly narrowing the search range to achieve efficient time complexity.
• Divisor Calculators - Calculates all divisors of an integer by iterating through potential candidates.
• Backtracking Solvers - This project calculates all possible operator combinations between digits of a string to reach a specific target value using backtracking techniques.
• String Abbreviation Generators - Generates all possible abbreviated forms of a string using backtracking.
• Connected Component Identification - Implements algorithms for identifying and counting contiguous regions of identical values in a grid.
• Backtracking Algorithms - Explores potential solution spaces using recursive backtracking to incrementally build and validate candidates.
• Dynamic Programming Techniques - Uses dynamic programming techniques to cache results of overlapping subproblems and avoid redundant computation.
• Classic Problem Implementations - Implements classic algorithmic challenges to demonstrate efficient problem-solving techniques for complex computational tasks.
• Linked List Mid-Node Deletions - Implements the copy-from-next node removal technique for singly linked lists.
• Missing Number Identification - Identifies missing integers in a sequence by comparing against expected ranges.
• Spatial Grid Traversal Algorithms - Provides computational methods for navigating and processing two-dimensional grid structures using coordinate-based logic.
• String and Sequence Algorithm Solutions - Provides string and sequence manipulation algorithms including pattern matching and reversal.
• Anagram Grouping Algorithms - Groups strings into categories based on shared patterns like anagrams.
• Graph - Calculates valid routes between nodes in a graph structure using recursive backtracking to explore potential connections.

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