👉 Effort math, also known as computational effort, is a mathematical discipline that quantifies the computational resources required by algorithms to solve problems. It focuses on analyzing and comparing these resources, typically measured in terms of time complexity (how execution time grows with input size) and space complexity (memory usage). By expressing these complexities using big O notation, effort math helps predict an algorithm's performance on large datasets, guiding developers in choosing efficient solutions. For instance, an O(n) algorithm scales linearly with input size, making it preferable to an O(n²) one for large inputs, as the former requires fewer resources overall. This analysis is crucial for optimizing algorithms and ensuring they remain practical as data sizes increase.
