leetcode_215

Find the kth largest element in an unsorted array. Note that it is the kth largest element in the sorted order, not the kth distinct element.

Example 1:

Input: [3,2,1,5,6,4] and k = 2
Output: 5

Example 2:

Input: [3,2,3,1,2,4,5,5,6] and k = 4
Output: 4

Note:

You may assume k is always valid, 1 ≤ k ≤ array's length.

Solutions

1. brute force O(nlog(n)) S(1)

2. sorting

class Solution {
public:
    int findKthLargest(vector<int>& nums, int k) {
        sort(nums.begin(), nums.end());
        return nums[nums.size() - k];
    }
};

1. quick sort based partition O(n) S(1)

2. time complexity: O(n) + O(n/2) + O(n/4)... worst case: O(n2)

#include <experimental/random>
class Solution {
public:
    int findKthLargest(vector<int>& nums, int k) {
        // k largest equals to size - k smallest
        k = nums.size() - k;
        int lo = 0, hi = nums.size() - 1;
        while (lo < hi) {
            // use random to prevent deterioration of complexity to O(n2)
            swap(nums[lo], nums[std::experimental::randint(lo, hi)]);
            // be carefull, the pivot is not nums[0]
            int pivot = nums[lo];
            int i = lo, j = hi;
            while (i < j) {
                while (i < j && nums[j] >= pivot) j--;
                nums[i] = nums[j];
                while (i < j && nums[i] <= pivot) i++;
                nums[j] = nums[i];
            }
            nums[i] = pivot;
            // you can also check if i equals k
            if (i <= k) lo = i + 1;
            if (i >= k) hi = i - 1;
        }

        return nums[k];
    }
};

1. heap O(n) + nlog(k) S(k)

2. Maintaing a min heap with k largest elements.

3. Other heap based selection algorithm also works.

class Solution {
public:
    int findKthLargest(vector<int>& nums, int k) {
        priority_queue<int, vector<int>, greater<int>>  heap;
        for (int i = 0; i < k; i++)
            heap.push(nums[i]);
        for (int i = k; i < nums.size(); i++) {
            heap.push(nums[i]);
            heap.pop();
        }

        return heap.top();
    }
};

Or a manually built heap.

1. bucket sort O(n) S(max - min + 1)

2. reference: https://leetcode-cn.com/problems/kth-largest-element-in-an-array/comments/105522

class Solution {
public:
    int findKthLargest(vector<int>& nums, int k) {
        auto min_max = minmax_element(nums.begin(), nums.end());
        int min = *min_max.first, max = *min_max.second;
        vector<int> bucket(max - min + 1, 0);
        for (auto & num : nums)
            bucket[num - min]++;
        int count = 0;
        for (int i = max - min; i >= 0; i--) {
            count += bucket[i];
            if (count >= k)
                return min + i;
        }
        return -1;
    }
};

1. Iteratively remove the smallest element O(kn) S(k)

2. reference: https://www.geeksforgeeks.org/k-largestor-smallest-elements-in-an-array/
3. The complexity equals to iteratively find the largest element k times.

class Solution {
public:
    int findKthLargest(vector<int>& nums, int k) {
        vector<int> store(nums.begin(), nums.begin() + k);
        auto pmin =  min_element(store.begin(), store.end());

        for (int i = k; i < nums.size(); i++) {
            if (nums[i] > *pmin) {
                *pmin = nums[i];
                pmin = min_element(store.begin(), store.end());
            }
        }
        return *pmin;
    }
};

1. introselect

2. binary search

3. Speculate the target number in the range of 32bit interger by counting the number of elements slower than it. For the target k'th larget number, numbers in the array <= it should be at least k.

class Solution {
public:
    int findKthLargest(vector<int>& nums, int k) {
        if (k > nums.size()) return -1;
        k = nums.size() - k + 1;
        long lo = INT_MIN, hi = INT_MAX;
        while (lo < hi) {
            long mid = lo + ((hi - lo) >> 1);
            int less = 0;
            for (auto n : nums) less += n <= mid;
            // for the target, numbers less(<=) should be at least k(may have duplicates).
            // if not satisfy, we can ensure the target is at least mid + 1
            if (less < k)
                lo = mid + 1;
            else
                hi = mid;
        }
        return lo;
    }
};