The Bron-Kerbosch algorithm is a backtracking algorithm used to find all maximal cliques in an undirected graph. Known for its efficiency, especially on sparse graphs, it is widely applied in social network analysis, bioinformatics, and computational chemistry. The algorithm can be optimized with pivoting to reduce recursive calls and improve performance.
Learn about function memoization, a technique to optimize performance by caching results of expensive function calls.
Greedy algorithms are a class of algorithms that make the optimal choice at each step with the hope of finding the global optimum
Backtracking is a systematic algorithmic method for solving problems where you need to explore all possible configurations (solution candidates) and discard those that fail to satisfy the given constraints. It is widely used to solve constraint satisfaction problems such as puzzles, combinatorial optimization problems, and decision problems.
Memoization is an essentail part for optimization technique used to improve the efficiency of recursive algorithms by storing previously computed results.
Overview and applications of Approximation Algorithms in computational problems.
Prim's algorithm is a greedy algorithm that finds the Minimum Spanning Tree (MST)
Understanding how recursion depth affects algorithm performance.
Stochastic Gradient Descent (SGD) is an optimization algorithm used to minimize the loss function in machine learning and deep learning models.