Problem Description

In this problem, we are working with two key concepts: pairs and sum minimization. We have two arrays, nums1 and nums2, both sorted in non-decreasing order. Our task is to find k pairs, each composed of one element from each array. The objective is to identify the pairs with the smallest possible sums. Specifically, we want to find the k pairs whose sums are the smallest among all possible combinations from nums1 and nums2.

If we were to list all possible pairs and their sums, the list would be quite extensive. However, since the arrays are already sorted, we can infer that the smallest sums will involve the smallest elements from both arrays.

Intuition

The solution employs a min-heap, a binary tree where the parent node is always less than or equal to its children. This is an ideal data structure for efficiently finding the smallest elements.

The algorithm initializes by creating a min-heap and pre-filling it with pairs combining each of the first k elements of nums1 with the first element of nums2.

Here's the step-by-step process to understand the solution:

1. Initialize the min-heap q. We start by forming pairs by taking each element from nums1 (up to the first k elements to avoid unnecessary work) and pairing it with the first element in nums2. The reason for pairing with the first element from nums2 is that we are trying to create pairs with the smallest possible sum to begin with.

2. Heapify q to ensure the smallest sum pair is at the top of the heap.

3. Prepare a list ans to store the resulting k smallest sum pairs.

4. Use a loop to process the min-heap. While the min-heap is not empty and k is greater than 0, we do the following:

   • Pop the smallest sum pair from the heap. This pair is guaranteed to be one of the smallest sum pairs because of the min-heap's properties.

   • Append this pair to the ans list.

   • Decrease k by 1, as we have successfully found one of the k smallest sum pairs.

   • Check if there's a next element in nums2 that can be paired with the same nums1 element. If so, add this new pair to the heap so that it can be considered in the next iteration.

5. Return the ans list containing up to k smallest sum pairs.

By utilizing a min-heap, we ensure that at each step, we pop the minimum sum pair without having to check all combinations. Moreover, since nums1 and nums2 are sorted, we can confidently move to the next element in nums2 for potential pairing without worrying about missing smaller sums.

Learn more about Heap (Priority Queue) patterns.

Solution Approach

The solution we're discussing uses a min-heap to efficiently find the k pairs with the smallest sums. Let's walk through the implementation step by step, taking advantage of algorithms, data structures, and patterns.

1. Min-Heap Initialization and Heapify: The code snippet q = [[u + nums2[0], i, 0] for i, u in enumerate(nums1[:k])] initializes our min-heap q. For each element in nums1 (limited to the first k elements), we create a list with the sum (u + nums2[0]), the index of the nums1 element (i), and the index 0 indicating we've used the first element of nums2. The heapify(q) call then converts the list q into a heap data structure.

2. Processing the Min-Heap: The while loop continues as long as there are elements in the heap (i.e., potential pairs to consider) and k is greater than 0. In each iteration, we use heappop(q) to remove and return the smallest element from the heap, which is the pair with the current smallest sum.

3. Recording the Result: The current smallest sum pair's indices in nums1 and nums2 are stored in the _, i, j variables. We then append this pair [nums1[i], nums2[j]] to our answer list ans.

4. Generating New Pairs: After finding a pair with a small sum, we check if there is a next element in nums2 to pair with the same nums1 element. The if j + 1 < len(nums2) check is used for this purpose, and if true, we form a new pair with the same element from nums1 and the next element in nums2, then push it into the heap using heappush(q, [nums1[i] + nums2[j + 1], i, j + 1]) to make sure it can be considered in subsequent iterations.

5. Finding the k Smallest Sums: The heap keeps track of the current smallest pairs, and the loop ensures that each time we pop from the heap and push a new pair into it, we're considering the next smallest possible pair. This continues until we've found k pairs or there are no more new pairs to consider.

The key algorithms and data structures used here are the heap data structure (a min-heap in particular) for keeping track of the smallest pairs and a list for our result.

The pattern is utilizing a greedy-like approach: in each step, we're always taking the smallest available option (the pair with the smallest sum) and then exploring slightly larger options by considering the next possible element from nums2.

This approach results in a time-efficient solution because we avoid generating all possible pairs, which would have resulted in a much higher time complexity.

Example Walkthrough

Let's walk through a small example using the provided solution approach to understand how it works.

Consider the following input:

• nums1 = [1, 7, 11]
• nums2 = [2, 4, 6]
• k = 3

Following the steps of the solution approach:

1. Min-Heap Initialization and Heapify: We initialize the min-heap with elements paired as follows: (nums1[i] + nums2[0], i, 0). Since we're only interested in the first k pairs, and k is 3, we consider only the first three elements from nums1. Therefore, our min-heap q starts with the following elements (assuming we include all three elements of nums1 and k limits other factors):

   q = [
     (1 + 2, 0, 0),   # sum = 3, index in nums1 = 0, index in nums2 = 0
     (7 + 2, 1, 0),   # sum = 9, index in nums1 = 1, index in nums2 = 0
     (11 + 2, 2, 0),  # sum = 13, index in nums1 = 2, index in nums2 = 0
   ]

   After heapification, q ensures that the pair with the smallest sum is at the top of the heap.

2. Processing the Min-Heap: We start processing the heap by popping elements while k > 0. The smallest sum pair is at the top, so we pop (3, 0, 0) first.

3. Recording the Result: We extract the indices 0 from nums1 and 0 from nums2 and append the pair [1, 2] to our result list ans. Now, ans = [[1, 2]].

4. Generating New Pairs: Since nums1[0] was paired with nums2[0], we now pair nums1[0] with the next element in nums2, which is nums2[1] (value 4), and add this new pair to the heap. Now, the heap q looks like:

   q = [
     (1 + 4, 0, 1),   # sum = 5, new pair
     (7 + 2, 1, 0),
     (11 + 2, 2, 0)
   ]

   Heapification ensures the smallest pair stays at the top, which is now (5, 0, 1).

5. Finding the k Smallest Sums: We continue the process, now popping (5, 0, 1) from the heap and appending [1, 4] to ans. Subsequently, we form a new pair with nums1[0] and nums2[2], and the heap q gets updated after heapification. We repeat this process until k becomes 0.

After these steps, our result list ans is [[1, 2], [1, 4], [1, 6]], which are the k smallest sum pairs from nums1 and nums2.

By following this strategy, we avoid generating all possible pairs and efficiently find the k smallest sums using a min-heap to guide our pair selections.

Solution Implementation

Time and Space Complexity

The given Python code implements a heap to find the k smallest pairs of sums from two integer arrays nums1 and nums2. Here we will discuss its time complexity and space complexity.

Time Complexity

The time complexity of the algorithm is as follows:

1. Heap Initialization: The code creates a min-heap and initializes it with the sums of elements from nums1 and the first element of nums2. Since the heap is initialized with at most k elements (and not more than the length of nums1), the complexity of this step is O(k) since each heap insertion is O(log k) and we do it at most k times.

2. Heap Operations: Then, in each iteration, the algorithm pops an element from the heap and potentially pushes a new element into the heap. Since we perform k iterations (bounded by k and the length of the output), and both heappop and heappush operations have a time complexity of O(log k), the complexity for this part is O(k log k).

Overall, the time complexity is O(k) + O(k log k), which simplifies to O(k log k) because as k grows, the k log k term dominates.

Space Complexity

The space complexity of the algorithm is as follows:

1. Heap Space: The heap size is at most k, as it stores pairs of indices and their corresponding sum, giving us O(k).

2. Output List: The list ans to store the answer pairs. In the worst case, it will contain k pairs, leading to O(k) space complexity.

The overall space complexity combines the heap space and the output list, but since both are O(k), the total space complexity remains O(k).

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