| Average Case |
The expected amount of time or space resources required to solve a problem. |
| Average-Case Time Complexity |
The expected amount of time an algorithm takes to run for inputs of size n, considering all possible inputs. |
| Best Case |
The minimum amount of time or space resources required to solve a problem. |
| Best-Case Time Complexity |
The minimum amount of time an algorithm takes to run for a specific input of size n. |
| Big O Notation |
A way of describing the space or time complexity of an algorithm. |
| O(1) |
An operation whose execution time remains unchanged regardless of input size. |
| O(Log N) |
A time or space complexity where the amount of time or memory used increases logarithmically with the size of the input. |
| O(N) |
A time or space complexity where the amount of time or memory used increases linearly with the size of the input |
| O(N^2) |
A time or space complexity where the amount of time or memory used grows quadratically with the size of the input. |
| Space Complexity |
The amount of memory or storage space required by an algorithm to solve a problem. |
| Time Complexity |
The amount of time an algorithm takes to run as a function of the input size. |
| Worst Case |
The maximum amount of resources (such as time or space) required to solve a problem, considering all possible inputs. |
| Worst-Case Time Complexity |
The maximum amount of time an algorithm takes to run for any input of size n. |
