Editing Algorithms Complexity (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Algorithm''' — A finite set of instructions to solve a specific problem.
* '''Computational Complexity''' — A measure of the resources (time and space) needed to run an algorithm.
* '''Big O Notation''' — A mathematical notation that describes the limiting behavior of a function (the "worst-case" scenario).
* '''Time Complexity''' — How the execution time of an algorithm grows with the input size (n).
* '''Space Complexity''' — How the memory usage of an algorithm grows with the input size (n).
* '''P (Complexity Class)''' — Problems that can be solved in "polynomial time" (efficiently).
* '''NP (Complexity Class)''' — Problems where a solution can be ''verified'' in polynomial time, but not necessarily solved.
* '''NP-Complete''' — The "hardest" problems in NP; if one is solved efficiently, they all are.
* '''Recursion''' — A method where a function calls itself to solve smaller instances of the same problem.
* '''Greedy Algorithm''' — An algorithm that makes the locally optimal choice at each step.
* '''Dynamic Programming''' — An optimization technique that solves complex problems by breaking them into overlapping subproblems.
* '''Divided and Conquer''' — An algorithm design pattern that breaks a problem into two or more sub-problems of the same or related type.
* '''Heuristic''' — A technique designed for solving a problem more quickly when classic methods are too slow.
* '''Sorting''' — The process of arranging data in a specific order (e.g., QuickSort, MergeSort).
* '''Searching''' — The process of finding a specific item in a dataset (e.g., Binary Search).
</div>

<div style="background-color: #006400; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">