![]() Pros: Fairly simple to code, reasonably fast to calculate, but slightly slower than the above methodsĬons: Still can degrade to O(n^2). Easy for someone to construct an array that will cause it to degrade to O(n^2) Pros: Simple to code, fast to calculate, but slightly slower than the above methodsĬons: Still can degrade to O(n^2). What are some techniques to choose a pivot ?Ĭhoose the left most or rightmost element.Ĭons: If the data is sorted or nearly sorted, quick sort will degrade to O(n^2) (A classic case is when the first or last element is chosen as a pivot and the data is already sorted, or nearly sorted) This happens if the pivot is consistently chosen so that all (or too many of) the elements in the array are than the pivot. On average quick sort runs in O(n log n) but if it consistently chooses bad pivots its performance degrades to O(n^2) Pivot selection is an important part of quick sort and there are many techniques, all with pros and cons. A single p and r pair point at the fifth element. The array elements are still ordered as.A p and r pair point at the last element. The first p points at the fifth element, the first q and first r point at the sixth element. The fourth p and a q point at the ninth element, and the fourth r points at the last element. ![]() The third p points at the fifth element, a q and the third r points at the seventh element. The first p and r pair point at the first element, the second p and r pair point at the third element. The array elements are now ordered as.The second p points at the fifth element, the second q points at the eighth element, and the second p points at the final element. The first p points at the first element, the first q points at the second element, the first r points at the third element. The array now has multiple indices named p, q, and r. The array now has an index q pointing at the fourth element containing the value 6. The array starts off with elements, with index p pointing at the first element and index r pointing at the last element.What are the advantages of using the divide and conquer algorithm?Īnswer: The advantages of using the divide and conquer algorithm include: (1) it can be used to solve a wide variety of problems, (2) it can be very efficient for certain types of problems, (3) it is easy to understand and implement, and (4) it lends itself well to parallel processing. In many cases, the time complexity is O(n*log(n)), which is considered very efficient. ![]() ![]() What is the time complexity of the divide and conquer algorithm?Īnswer: The time complexity of the divide and conquer algorithm varies depending on the problem being solved. The divide and conquer algorithm can be used to break the array into smaller sub-arrays, sort them recursively, and then merge them back together to obtain the final sorted array. Give an example of a problem that can be solved using the divide and conquer algorithm.Īnswer: An example of a problem that can be solved using the divide and conquer algorithm is the sorting of a large array of unsorted numbers. What are the three basic steps in the divide and conquer algorithm?Īnswer: The three basic steps in the divide and conquer algorithm are: (1) Divide the problem into smaller sub-problems, (2) Conquer each sub-problem recursively, and (3) Combine the solutions of the sub-problems to obtain the final solution. What is the basic idea behind the divide and conquer algorithm?Īnswer: The divide and conquer algorithm is a problem-solving technique that involves breaking a complex problem into smaller, more manageable sub-problems, solving them individually, and then combining their solutions to obtain the final answer. Give me 5 medium-difficulty questions with answers about Divide and Conquer ![]()
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