Module 1 Introduction_AOA

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Across
  1. 2. The process of evaluating an algorithm based on its resource requirements.
  2. 5. An asymptotic notation representing the lower bound of an algorithm's growth rate.
  3. 7. An asymptotic notation representing the upper bound of an algorithm's growth rate.
  4. 9. The expected time taken by an algorithm over all possible inputs.
  5. 12. A method used to solve recurrence relations of divide-and-conquer algorithms.
  6. 14. The amount of time an algorithm takes as the input size increases.
  7. 15. An asymptotic notation representing the tight bound of an algorithm's growth rate.
  8. 17. The number of elements or amount of data given to an algorithm.
  9. 19. The minimum time taken by an algorithm for a given input size.
  10. 20. The operation of checking two elements to determine their order.
Down
  1. 1. The maximum time taken by an algorithm for any input of a given size.
  2. 3. The use of mathematical concepts to analyze algorithm efficiency.
  3. 4. A sorting algorithm that builds the sorted list one element at a time.
  4. 6. A finite sequence of steps used to solve a problem.
  5. 8. The amount of memory required by an algorithm during execution.
  6. 10. The analysis of algorithm efficiency for large input sizes.
  7. 11. The rate at which a function increases as the input size increases.
  8. 13. An equation that defines a problem in terms of smaller instances of the same problem.
  9. 16. A sorting algorithm that repeatedly selects the smallest element and places it in its correct position.
  10. 18. The operation of exchanging the positions of two elements.