1246. Palindrome Removal

Problem Description

The problem gives us an integer array arr and asks us to remove palindromic subarrays using as few moves as possible. A subarray is palindromic if it reads the same backward as forward, for example [1, 2, 2, 1]. Each time you remove such a subarray, the remaining parts of the array close in to fill the gap. The task is to determine the minimum number of these moves required to remove all elements from the array.

Intuition

The intuition behind the solution is grounded in dynamic programming. We understand that trying all possible subarrays and checking if they are palindromic would be inefficient. Therefore, to optimize our approach, we can break down the problem:

1. We can start by understanding that if a single element is always a palindrome, thus it can be removed in one move.
2. For two elements, they can be removed in one move if they are the same or two moves if they are different, which can be the basis for our dynamic programming transition.
3. For a larger subarray, the number of moves depends on whether the ends of the subarray match. If they do, the subarray could potentially be removed in one move (if the entire subarray is a palindrome), or the minimum moves could be achieved by splitting the subarray at some other point.
4. For subarrays that don't have matching ends, we know that they can't be palindromic by themselves, so we look for where to split the subarray into two parts, each of which can be further split into palindromic subarrays.
5. A dynamic programming table f can store the minimum number of moves required to remove a subarray arr[i:j]. Initially, all entries are set to zero, except for subarrays of length 1, which are set to 1.

By approaching the problem in this way, we incrementally build up the solution using information from smaller subarrays, storing and reusing these results to make the algorithm efficient.

Solution Approach

We create a 2D array f to use as a dynamic programming table, where f[i][j] represents the minimum number of moves needed to remove the subarray starting at i and ending at j. We initialize a diagonal of this table with ones since any single element can be removed in one move.

The next step is to fill in the table for larger subarrays. We consider each possible size of subarray starting from the smallest (2) and going up to the size of the entire array.

For each size, we iterate over all possible starting indices. The table is filled in a manner that for each subarray arr[i:j]:

• If arr[i] equals arr[j], it means we can potentially remove the subarray in one move if the inside part arr[i+1:j-1] is also a palindrome (which we know from f[i+1][j-1]).
• If arr[i] is not equal to arr[j], it means we need to split the array into two parts and the number of moves is the sum of moves for each part at the optimal splitting point. To find this point, we iterate through all possible ways to split the subarray arr[i:j] into two parts arr[i:k] and arr[k+1:j], and keep the minimum number of moves.

Once we fill in the table, the answer to the problem is the entry that represents the number of moves to remove the entire array, which is stored in f[0][n - 1], where n is the length of the array.

Learn more about Dynamic Programming patterns.

Solution Approach

The solution uses dynamic programming, which is a method where we break down a complex problem into simpler subproblems and store the results of these subproblems to avoid redundant computations.

The algorithm uses a two-dimensional array f with dimensions n by n, where n is the length of the input array arr. Each entry f[i][j] in this table will eventually contain the minimum number of moves required to remove the subarray arr[i...j].

Here's the detailed process of implementing the solution:

1. Initialization:

   • First, we initialize the array f with zeros and then we fill the diagonal f[i][i] with 1, because a single element is a palindrome and can be removed in one move.

2. Filling the DP Table:

   • We then fill in the table in a bottom-up manner. To do this, we need to iterate over the subarrays starting from the smaller ones to the larger ones.
   • First, we iterate over the possible lengths of subarrays. Then for each length, we iterate over all possible starting points i.
   • For each subarray arr[i...j], we check:
     • If arr[i] and arr[j] are equal and we have already calculated the minimum moves for arr[i+1...j-1] in f[i+1][j-1], then the entire subarray is potentially a palindrome.
     • If arr[i] and arr[j] are the same and the inner subarray arr[i+1...j-1] is a palindrome, the entire subarray can be removed in one move—hence, f[i][j] gets the value of f[i+1][j-1].
     • If arr[i] and arr[j] are not equal, or if the inner subarray is not a palindrome, we need to split the subarray at some point. To find the optimal splitting point, we iterate through all possible k to split arr[i...j] into arr[i...k] and arr[k+1...j]. We calculate the sum of moves for these parts, and f[i][j] gets the minimum value among all possible splits.
   • This process continues until we fill in the entry for the entire array f[0][n-1].

3. End Result:

   • After completly filling up the DP table, the solution to the problem—which is the minimum number of moves needed to remove all elements from the array—is found in f[0][n-1].

This dynamic programming approach is more efficient than brute force because we only compute the minimum moves for each subarray once, and we use these precomputed values to calculate the moves for larger subarrays.

The implementation is a classical DP solution where overlapping subproblems are solved just once and their solutions are stored, which reduces the time complexity significantly compared to a naive recursive approach. This showcases the power of dynamic programming in optimizing problem-solving strategies for specific types of problems.

By walking through the solution implementation, we see the application of the dynamic programming pattern, specifically the use of a 2D DP table, initialization based on base cases, filling in the table based on recursive relationships, and retrieval of the solution from the filled table.

Example Walkthrough

Let's walk through a small example to illustrate the solution approach. Consider the integer array arr = [1, 2, 1, 3, 2, 2].

1. Initialization: We create a matrix f of size 6x6 because our array arr has 6 elements. We then fill the diagonal from f[0][0] to f[5][5] with 1 because a single element is a palindrome and can be removed in one move.

2. Filling the DP Table:

   • For subarrays of length 2, we compare each pair of elements:
     • arr[0] and arr[1] are not equal, so f[0][1] will be set to 2 (1 move for each element).
     • arr[1] and arr[2] are not equal, so f[1][2] will also be set to 2.
     • arr[2] and arr[3] are not equal, so f[2][3] will be set to 2.
     • arr[3] and arr[4] are not equal, so f[3][4] will be set to 2.
     • arr[4] and arr[5] are equal, so f[4][5] will be set to 1, since [2, 2] is a palindrome and can be removed in one move.
   • For subarrays longer than 2, we consider their internal splits:
     • arr[0:2] could potentially be palindromic if arr[0] and arr[2] are equal, which they are, so we check if f[1][1] is a palindrome, and indeed it is (since it's a single element). Hence, f[0][2] is set to 1.
     • We continue this process for subarrays arr[1:3], arr[2:4], arr[3:5], checking the ends and the internal sections of the subarrays, updating our table as we find palindromes.

3. End Result:

   • After filling out the table, if we want to compute f[0][5], which is the minimum number of moves to remove all elements in the array, we check:
     • The array ends with arr[0] and arr[5] are not the same, so we must split the array.
     • We try all possible splits, looking for the minimum f[i][j] values for i to k and k+1 to j subarrays.
     • We find the minimum moves for all splits, and f[0][5] will have the minimum of these values.

As a result, f[0][5] holds the minimum number of moves required to remove all elements from arr by removing palindromic subarrays. This process reduces the problem into smaller, manageable steps that dynamically build upon each other to find the most efficient solution.

Solution Implementation

Time and Space Complexity

Time Complexity

The given code employs dynamic programming to find the minimum number of moves to make a palindrome by merging elements in the array. The time complexity is determined by the nested loops and the operations performed within them.

1. There are two nested loops, where one loops in a backward manner from n-2 to 0 and the other loops from i+1 to n. Each of these loops has O(n) iterations resulting in O(n^2) for the nested loops combined.
2. Inside the inner loop, there is another loop that ranges from i to j. In the worst-case scenario, this loop can iterate n times.
3. The innermost computation, however, is just a min comparison which is O(1).

Multiplying all these together, the worst-case time complexity is O(n^3).

Space Complexity

The space complexity is derived from the storage used for the dynamic programming table f.

1. The f table is a 2D array with dimensions n x n, containing n^2 elements.
2. None of the loops use additional significant space.

Thus, the space complexity of the code is O(n^2).

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