Big(O) Complexity Classes in Programming

What is a ScrollSet? Read the one page paper.
Note: these ScrollSets were generated by LLMs without extensive human review.

Concepts

id classDescription commonAlgorithms
O(1) Execution time remains constant regardless of input size. Finding array element by index, adding a node to the head of a linked list
O(log n) Execution time grows logarithmically in proportion to the input size. Binary search
O(n) Execution time grows linearly with the input size. Linear search, traversing an array
O(n log n) Execution time grows linearly and logarithmically with the input size. Quick sort, merge sort
O(n^2) Execution time grows quadratically with the input size. Bubble sort, selection sort, insertion sort
O(n^3) Execution time grows cubically with the input size. Naive matrix multiplication
O(2^n) Execution time grows exponentially based on the input size. Brute force solutions for the traveling salesman problem, recursive calculation of Fibonacci numbers
O(n!) Execution time grows factorially based on the input size. Solving the traveling salesman problem via brute force, generating all permutations of a set

Measures

Name Values Coverage Question Example Type Source SortIndex IsComputed IsRequired IsConceptDelimiter Crux
id 8 100% What is the ID of this concept? O(1) string 1 false true true
classDescription 8 100% A brief explanation of the complexity class, often describing how the time or space requirements grow with the size of the input. Execution time remains constant regardless of input size. string 1.9 false
commonAlgorithms 8 100% Examples of algorithms or operations that typically exhibit this level of complexity. Finding array element by index, adding a node to the head of a linked list string 1.9 false