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1. A sorting algorithm that does not require extra space for temporary arrays or data structures.
2. A problem-solving strategy that involves breaking a problem into sub-problems, solving each sub-problem independently, and then combining the solutions to solve the original problem.
3. A sort algorithm that looks at the elements of the array to be sorted to determine their relative order.
4. The amount of time an algorithm takes to solve a problem as a function of the size of the input.
5. A mathematical notation used to describe the upper bound of the time complexity of an algorithm as the input size approaches infinity.
6. An informal high-level description of the operating principle of a computer program is a non-specific outline that summarizes how the program functions.
7. A method for solving a problem by dividing it into progressively smaller subproblems.
8. A function or algorithm that calls itself with a smaller version of the problem until a base case is reached.
9. The scenario in which an algorithm takes an average amount of time to solve a given problem.
10. The number of times two elements are compared during the sorting process.
11. Algorithm that divides an array into sub-lists, sorts the sub-lists, and then merges them back together in sorted order.
12. The amount of memory an algorithm requires to solve a problem as a function of the size of the input.
13. The scenario in which an algorithm takes the least amount of time to solve a given problem.
14. A sort algorithm that arranges the elements of an array without needing any extra memory for sorting.
15. A variant of merge sort that does not require extra space for temporary arrays or data structures.
16. The scenario in which an algorithm takes the most amount of time to solve a given problem.
17. The central component of the merge sort algorithm is the function that combines two sorted subarrays. Its primary goal is to merge these arrays.
18. A sorting algorithm that necessitates extra memory in order to arrange the array.