Problem Description

The problem asks for all ways a given string s can be split such that each substring in the partition is a palindrome. A palindrome is a string that reads the same forwards and backwards, such as 'racecar'.

For example, if the input string is "aab", then there are two ways to partition it into substrings that are all palindromes: ["aa", "b"] and ["a", "a", "b"].

Flowchart Walkthrough

Let's analyze the problem using the Algorithm Flowchart and identify the appropriate strategies to solve LeetCode problem 131, "Palindrome Partitioning". Here's a step-by-step walkthrough based on the flowchart structure:

1. Is it a graph?

   • No: The problem does not involve navigating or exploring nodes and connections typically found in graph problems.

2. Need to solve for kth smallest/largest?

   • No: The task is not about sorting or finding specific rank-based elements.

3. Involves Linked Lists?

   • No: The problem is based on strings and does not involve linear data manipulation typical in linked-list scenarios.

4. Does the problem have small constraints?

   • Yes: The problem can be solved within reasonably small constraints since it involves generating partitions of a string, which are limited by the string’s length.

5. Brute force / Backtracking?

   • Yes: The problem requires generating possible solutions and checking constraints (palindromic condition), which is a typical scenario where backtracking is appropriate.

Conclusion: The flowchart indicates that a backtracking approach is appropriate for solving the problem of partitioning a string into all possible subsets where each subset is a palindrome. This involves exploring different partitioning strategies and validating palindromic sequences, aligning perfectly with the backtracking algorithm pattern.

Intuition

The core idea behind the solution is to use depth-first search (DFS). We want to explore all possible partitions and whenever we encounter a partition where all substrings are palindromes, we add that partition to our answer.

Here, DFS is used to recursively build partitions. Starting from the beginning of the string, we check every possible end index for a substring that could be a palindrome. If a substring from the current start index to the current end index is a palindrome, then we add it to the current partition list and invoke DFS from the next index. If it's not, we skip the current index and move to the next.

To optimize the identification of palindromes, we use dynamic programming. A 2D table f is maintained where f[i][j] is True if the substring from index i to j is a palindrome. The elements in this table are filled in by checking if the two ends of the substring are equal and if the inside substring (from i+1 to j-1) is also a palindrome.

The recursive DFS approach combined with dynamic programming allows us to efficiently compute all the ways to partition the string into substrings that are palindromes.

Learn more about Dynamic Programming and Backtracking patterns.

Solution Approach

The solution uses a DFS algorithm combined with dynamic programming to efficiently find all possible palindrome partitioning of the string s. Let's walk through the key parts of the implementation.

Firstly, the dynamic programming table f is prepared, which is a square matrix, where f[i][j] indicates whether the substring from i to j (inclusive) is a palindrome. This precomputation avoids redundant checks and optimizes the process of verifying palindromes during the DFS traversal.

Here's how the table f is populated:

• Initialize f as an n x n matrix filled with True, as every single character is a palindrome by itself.
• The table is then filled in a bottom-up manner. For substrings longer than one character (from n-1 to 0 for i and from i+1 to n for j), we check if the two ends are the same, s[i] == s[j], and if the inside substring f[i+1][j-1] is a palindrome.

Once the table is prepared, we use the DFS approach to build the partitions:

• A recursive function dfs takes an index i as a parameter, indicating the current starting point of the substring being considered.
• The recursive function works as follows:
  1. If i equals the length of the string n, it means we reached the end of the string and thus, have a valid partition we can add to the answer list ans.
  2. For other cases, we check all possible end indices j (from i to n-1) and if f[i][j] is True, indicating a palindrome, we append this substring to the current partition list t.
  3. We then recursively call dfs(j + 1), which will attempt to find palindrome partitions for the remaining substring.
  4. Afterwards, we must backtrack by removing the last substring added to t before moving on to the next index.

The DFS is initiated by calling dfs(0), meaning it starts with an empty partition and it looks at the string from the very start. The result is accumulated in the list ans, which eventually contains all the possible palindrome partitionings.

Note that ans is a list of lists, where each sublist represents a distinct palindrome partitioning of s. The variable t represents the current state of the partition list under consideration at each step of the DFS.

By using a combination of DFS for enumeration, dynamic programming for palindrome checking, and backtracking for generating partitions, the solution efficiently enumerates all palindrome partitions of the string s.

Example Walkthrough

Let's walk through an example to illustrate the solution approach using the string "aab".

Step 1: First, we prepare the dynamic programming table f. The length of the string s is 3, so our table will be a 3x3 matrix. Initially, f[i][i] (all positions where i == j) are set to True since all characters are palindromes by themselves.

f = [
  [True, False, False],
  [False, True, False],
  [False, False, True]
]

Step 2: Now we fill the remaining f[i][j] values where i != j. This involves checking substrings of length 2 and above.

• For i = 1 and j = 2 ("aa"), s[i] is equal to s[j] and the inside substring (which is empty) is "trivially" a palindrome. So, f[1][2] is set to True.
• For i = 0 and j = 1 ("ab"), s[i] is not equal to s[j], so f[0][1] remains False.
• For i = 0 and j = 2 ("aab"), s[i] is not equal to s[j], so f[0][2] remains False.

Our DP table now looks like this:

f = [
  [True, False, False],
  [False, True, True],  // "aa" is a palindrome
  [False, False, True]
]

Step 3: We start a DFS traversal to build palindrome partitions.

• We call dfs(0) and look for all palindromic substrings starting at index 0.
• For i = 0, we consider substring "a" (f[0][0] is True), and so we add ["a"] to our current partition t and call dfs(1).
• Inside dfs(1), we again find a palindromic substring "a" (f[1][1] is True), so we add ["a"] to t and call dfs(2).
• Finally, in dfs(2), we add "b" to t, since f[2][2] is True, and reach the end of the string. We now have ["a", "a", "b"] and add this partition to our answer list ans.
• We backtrack to dfs(1) and continue the loop for j = 2, now "aa" is a palindrome as f[1][2] is True, so we add ["aa"] to t and call dfs(3).
• In dfs(3), we've reached the end of the string again, so we now have ["a", "aa"] which is a valid partition that we add to ans.

The DFS finishes, and our answer ans contains all possible palindrome partitioning for the input string "aab":

ans = [
  ["a", "a", "b"],
  ["aa", "b"]
]

We successfully obtained all partitions where each substring is a palindrome using a combination of DFS, dynamic programming, and backtracking.

Solution Implementation

Time and Space Complexity

Time Complexity

The given code first prepares a 2D boolean array f, which is used to determine if a substring s[i:j] is a palindrome. This preparation takes O(n^2) time because it gradually shrinks the substring window from both sides and each cell in f is filled based on the result of palindrome checks on previous substrings.

The main part of the algorithm uses depth-first search (DFS) to build all possible palindrome partitions of the string. Every recursive call of dfs goes through the string to find palindromes starting from the current index i up to the end of the string. In the worst case scenario, each character could be the start of a palindrome substring, leading to a situation where the number of recursive calls is proportional to the Catalan numbers because each step can involve a choice of different ending indices for the palindrome. The nth Catalan number is given by the formula (2*n)! / ((n+1)!*n!) which is approximately O(4^n / (n^(3/2))). This determines the number of possible partitions in the worst case and thus the recursive calls made.

Given that the length of the input string is n, the time complexity is O(n^2) for the initial palindrome check array preparation plus the time complexity of the DFS traversal, which is O(4^n / (n^(3/2))). Hence, the overall worst-case time complexity is O(n^2 + 4^n / (n^(3/2))).

Space Complexity

The space complexity is influenced by two factors: the space taken by the 2D array f and the space used by the recursion call stack.

• The 2D array f has size n^2, hence it uses O(n^2) space.
• The depth of the recursion stack is at most n (the size of the string s), because that's the deepest it can go (each character can be a separate palindrome). Furthermore, at each level of recursion a string is potentially added to the current partition t. Thus, we need to account for the space taken by the list of current palindromes t, which in the worst case can store up to n strings.

Thus, the space complexity of the recursive calls and the space to store the current partitions is O(n).

However, we also have an ans list that stores all the possible palindrome partitions. In the worst case, there can be exponential (Catalan number) many partitions, and the list of partitions can grow very large, significantly larger than the stack depth.

Combining these factors, the space complexity is O(n^2) for the 2D array and O(4^n / (n^(3/2))) for the partitions, which gives us the overall space complexity of O(n^2 + 4^n / (n^(3/2))).

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