Dustin Boston

Breadth-First Search is an algorithm for searching graph data structures. It starts at a specified source node and explores all its immediate neighbors before moving on to the next level of neighbors.

Bubble Sort is a simple comparison-based sorting algorithm that repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. This process is repeated until the list is sorted. Although easy to implement, its worst-case and average-case time complexity of \(O(n^2)\) makes it inefficient on large datasets.

A Chained Hash Table is a data structure that resolves collisions using linked lists. Each slot in the hash table array points to a chain (linked list) that stores all elements hashing to the same index. When inserting, searching, or deleting an element, the algorithm hashes the key to determine the appropriate slot and traverses the corresponding chain to perform the operation.

Cosine similarity is a measure of similarity between two non-zero vectors in a multi-dimensional space, defined as the cosine of the angle between them. It is calculated as the dot product of the vectors divided by the product of their magnitudes. This metric is often used in text analysis, clustering, and information retrieval to determine the similarity of documents or data points, independent of their magnitude.

Counting Sort is a non-comparison-based sorting algorithm that sorts integers by counting the occurrences of each distinct value in a range and using this information to place elements in their correct positions. It works in 𝑂(𝑛+𝑘) time, where 𝑛 is the number of elements and 𝑘 is the range of the input values. Counting Sort is efficient for small ranges of integers but unsuitable for sorting non-integer or large-range data due to its space complexity of 𝑂(𝑘). It is a stable sorting algorithm when implemented correctly.

A finite automata algorithm is a step-by-step method used to check if a sequence of symbols (like letters or numbers) matches a specific pattern. It works by moving through a series of states based on the input, starting from an initial state and possibly ending in a state that signals a match.

The calculateIntersectionWeight function computes the intersection weight between two arrays of strings by determining the ratio of the size of their intersection to the length of the smaller array. It relies on the getIntersection function to find the common elements between the two arrays, ensuring an efficient and straightforward implementation. The getIntersection function uses sets to identify and return a list of unique shared elements, removing duplicates and simplifying comparison. Together, these functions handle edge cases, such as empty arrays, by returning 0 when appropriate to avoid division by zero, making them robust and easy to use for set-based comparisons.

An iterative depth-first search algorithm using a stack, exploring as far along each branch as possible before backtracking, useful for pathfinding and cycle detection.

A recursive depth-first search algorithm that explores branches deeply before backtracking, managing traversal state with recursion.

The Wagner-Fischer algorithm is a dynamic programming method for calculating the Levenshtein distance (edit distance) between two strings. It determines the minimum number of single-character edits (insertions, deletions, or substitutions) needed to transform one string into another by filling a matrix where each cell represents the edit distance between prefixes of the two strings. The algorithm builds the solution incrementally, ensuring optimal substructure and overlapping subproblems for efficient computation.