Problem Description

The given problem presents us with a scenario where we need to find the k(^{th}) largest element in a provided integer array nums. Unlike finding the maximum or minimum element, this task requires us to identify the value that would be placed at the k(^{th}) largest position if the array were sorted. However, the twist in this problem is that duplicates are allowed, and each instance counts for the position. For example, if nums = [3,2,3,1,2,4,5,5,6] and k = 4, the fourth largest element is 4.

Moreover, the problem poses an additional challenge: it hints at the possibility of finding the solution without sorting the entire array, which might suggest that there are more optimal ways to solve this especially when we consider time complexity.

Intuition

To solve this problem within the constraint of not fully sorting the array, we can draw on strategies from the concept of Quick Select, which is a selection algorithm based on the partitioning logic of Quick Sort.

Quick Sort operates by selecting a pivot element from the array and partitioning the other elements into two sub-arrays, according to whether they are less than or greater than the pivot. The pivotal insight here is that once we position a pivot during partitioning, its final sorted position is fixed in the array.

We use this characteristic of Quick Sort to our advantage. By performing partitioning, if we position a pivot at index p, and p is the n-k position in a 0-indexed array (the position where our kth largest element would be if the array were sorted in ascending order), then we have our answer: the element at index p. If p is less than n-k, it implies the kth largest element is to the right, so we only need to partition the sub-array to the right of p. Conversely, if p is larger than n-k, we only need to partition to the left of p.

Using this approach, the algorithm short-circuits the sorting process, only partially sorting the elements around the k(^{th}) largest value until it is positioned correctly. In the best case, this results in linear time complexity, although in the worst case (which can be mitigated by good pivot selection), it can reach quadratic complexity—it's still generally more optimal than fully sorting the array.

Learn more about Divide and Conquer, Sorting and Heap (Priority Queue) patterns.

Solution Approach

To implement the solution based on the intuition described earlier, the Reference Solution Approach uses the Quick Select algorithm, which is inspired by the partition process of Quick Sort.

Here's a step-by-step explanation of the algorithm in the given solution:

1. Define a helper function quick_sort which will handle the partitioning and the recursive search.
2. Within quick_sort, calculate a pivot value x as the element in the middle index of nums—this is computed as nums[(left + right) >> 1] where >> 1 is a bit-shift operation equivalent to integer division by 2. This pivot is arbitrary, and different selection methods can be used to optimize the algorithm.
3. Set two pointers, i starting from the left bound left - 1 and j from the right bound right + 1. Then increment i and decrement j until an element greater than or equal to x is found from the left and an element less than or equal to x is found from the right.
4. If i < j which means elements are on the wrong side of the pivot, swap them. Repeat this process until i is not less than j.
5. After the loop, j will be at the partition point. If j is less than the index we're interested in (k), we know our kth largest element is on the right of j hence call quick_sort recursively for the right sub-array.
6. If j is greater or equal to k, the kth largest element is on the left side or at j.
7. Invoke this helper function with the entirety of the input array's bounds and n - k as the target position, starting the process.

The algorithm's success hinges on the partitioning step, which ensures that after each pass, at least one element (the pivot) is in its final sorted position. By targeting the n - k index, we aim to put the kth largest element (which is the kth from the last in sorted order) in its correct position.

To visualize this algorithm with an example, let's consider nums = [3,2,1,5,6,4] and k = 2. The steps would be:

• Pick a pivot (let's say it's 3).
• After one partitioning step, the array could look like [2, 1, 3, 5, 6, 4].
• 3 is now in its correct sorted position (index 2). We're looking for the index 4 (n - k), which is to the right of our pivot. Therefore, we'll now focus on the sub-array [5, 6, 4].
• The algorithm will then repeat these steps on the sub-array until the element at the n-k place is positioned correctly, at which point it will return as the kth largest element.

By performing this targeted partitioning, we avoid the need to sort the entire array, which gives us a quicker path to the desired kth largest element.

Example Walkthrough

Let's illustrate the solution approach with a small example. We will use the array nums = [3, 2, 1, 5, 6, 4] and k = 2, meaning we want to find the 2nd largest element.

1. We need to find the n-k th element in the sorted order, which translates to 6 - 2 = 4 in 1-indexed or 3 in 0-indexed notation in our array.
2. We pick a pivot, let's use the median value of 3 as an example. The quick_sort function is then called with the entire array along with indices of the bounds 0 and 5.
3. Partition the array around the pivot 3. One possible way the array could be partitioned is [2, 1, 3, 5, 6, 4]. After partitioning, the pivot 3 is positioned at its correct sorted index 2.
4. Since our target index 3 is greater than the pivot's index 2, we ignore the left part of the array including our pivot, and focus on the right part: [5, 6, 4].
5. We repeat the process on this sub-array. We pick a new pivot, say 5. Partitioning this sub-array might result in [4, 5, 6].
6. The pivot 5 lands at index 1 of the sub-array (4 in the original array, due to 0-indexing). Now the position of 5 is the same as our target index 4 (n-k), which means it's the kth largest element.

The second-largest element in this array is 5, and thus is the output of our algorithm using Quick Select. This process saved time by not having to sort the whole array and instead focused on finding the specific kth element directly.

Solution Implementation

Time and Space Complexity

The implemented function findKthLargest uses the QuickSelect algorithm, which is a variation of the QuickSort algorithm, to find the kth largest element in the list.

Time Complexity

The average time complexity of the QuickSelect algorithm is O(n), where n is the number of elements in the list. This is because it generally only needs to recursively partition one side of the pivot, effectively reducing the problem size by about half each time.

However, in the worst-case scenario, the time complexity degrades to O(n^2). This happens when the pivot selected is always the smallest or largest element in each recursion, causing the algorithm to partition around each element one by one. In practice, with a good pivot selection strategy (such as using the median-of-medians algorithm), the worst-case complexity can be improved, but this code uses a simple middle element pivot, so it remains vulnerable to the worst-case behavior.

Space Complexity

The space complexity of the QuickSelect algorithm used here is O(log n) on average due to the recursion stack. It requires additional space proportional to the depth of the recursion tree which, on average, has a depth of log n.

In the worst case, where the smallest or largest element is always chosen as the pivot, the recursive calls can go up to n levels deep resulting in a space complexity of O(n).

Learn more about how to find time and space complexity quickly using problem constraints.