358. Rearrange String k Distance Apart

Problem Description

The given problem involves taking a string s and an integer k. The task is to rearrange the characters of the string in such a way that the same characters in the new string are separated by at least k distances from each other. If it is not possible to perform such a rearrangement that satisfies the condition, the output should be an empty string "".

For example, If we have the string s = "aabbcc" and k = 3, one possible rearrangement could be "abcabc", as any same characters are at least k = 3 units apart. However, if k was larger, say 7, it would be impossible to rearrange because we don't have enough unique characters to place between repeats to satisfy the distance requirement.

Intuition

To find a solution to this problem, we need a way to ensure that whenever we add characters to the new string (let's call it ans), we respect the minimum distance k between the same characters.

One intuitive approach is to keep track of the frequency of each character and always pick the character with the highest remaining frequency that doesn't violate the k distance rule. To efficiently select the highest frequency characters, we can use a max-heap. In Python, we use a min-heap with negative frequencies to simulate a max-heap.

However, simply using a max-heap isn't enough, because we must wait until k different characters have been added to ans before we're allowed to use the same character again. For this, we can use a queue to temporarily store characters and their adjusted frequencies after they've been used. By the time a character circles back from the end of the queue to the front, k other characters would have been placed in ans, allowing us to either discard the character if its frequency has dropped to zero or push it back into the heap with an adjusted (decremented) frequency to potentially use it again.

The process continues until the heap is empty, meaning all characters have been used up. If at the end of this process the length of ans is not equal to the length of s, that would mean it was not possible to rearrange the string to satisfy the condition, and therefore we return an empty string. Otherwise, we concatenate the characters in ans and return the rearranged string.

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

Solution Approach

The solution uses a combination of a max-heap (implemented as a min-heap with negated values in Python) and a queue for efficient character selection and distancing. Here's a step-by-step breakdown:

1. Frequency Counting: We start by counting the frequency of each character in s using the Counter from the collections module. This gives us a dictionary of characters along with their respective frequencies.

2. Heap Initialization: We then convert this dictionary into a list of tuples, where each tuple contains the negated frequency of the character and the character itself ((-v, c)). This list is then turned into a heap using heapify, which organizes it so that the character with the highest frequency (lowest negative number) is at the top of the heap.

3. Queue Initialization: A queue is initialized using deque to store characters after they are popped from the heap, along with their adjusted (decremented) frequency.

4. String Construction: We then enter a loop to construct the answer string:

   • Pop the character with the highest frequency from the heap (or the least negative number since we are using a min-heap).
   • Negate its frequency back to positive and add the character to the answer list ans.
   • Add the character and its decremented frequency (frequency - 1) to the queue to make it wait until we've placed k characters between its repetitions.

5. Maintaining the Distance k: For every character added to ans, we check if the queue has reached length k. If it has, we pop from the queue's left (the front), which would be the character that was put there k steps ago.

   • If this character's frequency is still more than 0 (meaning it needs to be placed again), we push it back into the heap with its frequency negated to maintain the max-heap property.

6. Final Check and Result: Once the heap is empty, we check if the length of the ans list is the same as the input string s. If they're not equal, it indicates that it was impossible to arrange s according to the given condition, and we return an empty string "". Otherwise, we join the list ans into a string and return it as the final answer.

By using a heap, we ensure that we always pick the character with the highest frequency that isn't currently "on hold" in the queue, which maximizes our chances of finding a valid character placement. The queue, on the other hand, ensures that each character is placed far enough apart to meet the condition specified by k.

Here's the key code component breakdown:

• Counter(s).items() gives us character frequencies.
• heapify(h) turns our list h into a heap.
• heappop(h) retrieves and removes the highest frequency character from the heap.
• q.append((v - 1, c)) adds characters to the queue.
• q.popleft() removes and returns the leftmost character from the queue.
• heappush(h, (-w, c)) pushes a character back into the heap with its updated frequency.

This algorithm ensures that we can efficiently arrange characters k apart in the optimal way, or determine that such an arrangement is not possible.

Example Walkthrough

Let's walk through a small example to illustrate the solution approach using the string s = "aaabc" and k = 2.

1. Frequency Counting: Count the frequency of each character.

   1'a': 3
   2'b': 1
   3'c': 1

2. Heap Initialization: Initialize the heap with negated frequencies to simulate max-heap.

   1Heap: [(-3, 'a'), (-1, 'b'), (-1, 'c')]
   2(This is turned into a heap in-place, so it might not look ordered here.)

3. Queue Initialization: Initialize an empty queue with deque.

   1Queue: []

4. String Construction: Let's start constructing the string. The heap contains the following elements (negated frequencies shown):

   1Step 1: Pop 'a' (real frequency 3) and add to answer -> ans = ['a'], Heap now: [(-1, 'b'), (-1, 'c')]
   2Step 2: Pop 'b' (real frequency 1) and add to answer -> ans = ['a', 'b'], Heap now: [(-1, 'c')]
   3Step 3: We cannot pop 'a' again since it's only 1 character away, but we can pop 'c'.
   4         Add 'c' to answer -> ans = ['a', 'b', 'c'], Heap now: []

   At this point, the heap is temporarily empty, but we still have characters to reinsert. Our queue keeps track of the characters we've used.

5. Maintaining the Distance k: After each character addition to ans, we push it into the queue with a decremented frequency if it's greater than 0.

   1Step 1: Push ('a', 2) into the queue
   2Step 2: Push ('b', 0) into the queue
   3Step 3: Since 'b' frequency is now 0, we don't push it again, but we push ('c', 0)

   Once 'a' has been in the queue for k = 2 steps, we pop it from the queue. Its adjusted frequency is larger than 0, so we reinsert ('a', 2) into the heap.

6. Final Check and Result: We continue this process, and our ans looks like this at the end: ['a', 'b', 'c', 'a', 'a']. We compare the length of ans with s, and they're not equal, which means a valid arrangement was not possible. Thus, we return an empty string "".

However, if at some step a different character had a frequency sufficient to complete the process, such as 'd', we might have arranged 'd' in between and the process could return a valid string. For this example, since it's not possible to meet the requirement of placing the same characters k = 2 distances apart due to high frequency of 'a', we return an empty string.

This concludes the example walkthrough, illustrating how the combination of a max-heap and a queue helps to solve the problem of arranging characters at least k distance apart or determining that it's not feasible.

Solution Implementation

Time and Space Complexity

Time Complexity

The time complexity of this function is predominantly determined by the operations associated with the heap and the deque.

• The construction of the heap occurs once using heapify after converting the Counter(s).items() into a list of tuples, which takes O(n) time, where n is the number of distinct characters in the string s.
• The main while-loop runs as long as there are elements in the heap, which means it can execute up to len(s) times because in the worst case each letter is appended to the answer string ans.
• Inside the loop, the heappop operations happen once per loop iteration, which has a complexity of O(log n), as each heappop operation is on a heap of at most n distinct characters.
• Adding an element back into the heap has the same O(log n) complexity for the same reasons.
• The operations associated with the deque, such as append and popleft, both operate in O(1) time.

Since each character passes through the heap and the deque at most once, the heappop and the heappush operations dominate. Thus, the overall time complexity of the main loop is O(len(s) * log n), where len(s) is the length of the input string and n is the number of distinct characters.

Space Complexity

The space complexity of this function is dominated by the heap, the deque, and the output list.

• The heap can contain at most n elements, where n is the number of distinct characters in s.
• The deque can contain at most k elements since we insert one element per loop iteration and remove one when the deque size exceeds k.
• The output list ans will ultimately contain len(s) elements.

Combining these factors, the space complexity can be considered O(n + k + len(s)). However, since n and k are bounded by the length of s, this can be simplified to O(len(s)), which is the overhead required to store the answer separate from the input.

In conclusion:

• Time Complexity: O(len(s) * log n)
• Space Complexity: O(len(s))

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