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1. The process of improving the performance of an algorithm by reducing its time or space complexity.
2. It is a traversing method for binary search trees where the root is visited first, followed by the left and right subtrees recursively.
3. A method for solving a problem by dividing it into progressively smaller subproblems.
4. The scenario in which an algorithm takes an average amount of time to solve a given problem.
5. A step-by-step process for solving a problem or achieving a specific goal.
6. A list that is not sorted according to any specific criterion.
7. A sorting algorithm that does not require extra space for temporary arrays or data structures.
8. A sort algorithm that arranges the elements of an array without needing any extra memory for sorting.
9. A problem-solving strategy that involves breaking a problem into smaller sub-problems, solving them independently and combining their solutions.
10. The time or space complexity of an algorithm when it receives average inputs.
11. The speed and effectiveness with which a task is completed, measured in terms of time and resources used.
12. The time or space complexity of an algorithm when it receives the best input possible.
13. A variant of merge sort that does not require extra space for temporary arrays or data structures.
14. A mathematical notation used to describe the upper bound of the time complexity of an algorithm as the input size approaches infinity.
15. A collection of elements of the same data type, stored in contiguous memory locations.
16. The process of finding a specific value within an array or list.
17. The scenario in which an algorithm takes the least amount of time to solve a given problem.
18. Algorithm that searches a list or array one item at a time in a linear fashion.
19. The scenario in which an algorithm takes the longest time to complete.