Palindrome Partitioning Using Backtracking in DSA Java - Time & Space Complexity

Time Complexity: Palindrome Partitioning Using Backtracking

O(n * 2^n)

Understanding Time Complexity

We want to understand how the time needed to find all palindrome partitions grows as the input string gets longer.

How does the number of steps change when the string size increases?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

public void backtrack(String s, int start, List<String> current, List<List<String>> result) {
    if (start == s.length()) {
        result.add(new ArrayList<>(current));
        return;
    }
    for (int end = start; end < s.length(); end++) {
        if (isPalindrome(s, start, end)) {
            current.add(s.substring(start, end + 1));
            backtrack(s, end + 1, current, result);
            current.remove(current.size() - 1);
        }
    }
}

private boolean isPalindrome(String s, int left, int right) {
    while (left < right) {
        if (s.charAt(left++) != s.charAt(right--)) return false;
    }
    return true;
}

This code finds all ways to split the string into palindrome parts using backtracking.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The for-loop tries every possible substring start to end, and the recursive call explores partitions from the next position.
• How many times: The recursion explores many combinations, and for each substring, palindrome checking scans characters between start and end.

How Execution Grows With Input

As the string length grows, the number of palindrome partitions grows very fast because many substring combinations are possible.

Input Size (n)	Approx. Operations
10	Thousands of checks and recursive calls
100	Millions to billions of operations due to many partitions
1000	Extremely large, often impractical without optimization

Pattern observation: The operations grow faster than any simple polynomial because of exploring all partition combinations and palindrome checks.

Final Time Complexity

Time Complexity: O(n * 2^n)

This means the time grows very fast as the string gets longer, roughly doubling the work for each extra character, multiplied by the cost to check palindromes.

Common Mistake

[X] Wrong: "The algorithm runs in simple polynomial time like O(n²) because it just checks substrings."

[OK] Correct: It explores all possible partitions, which grow exponentially, and palindrome checks add extra cost, making it much slower than polynomial time.

Interview Connect

Understanding this complexity helps you explain why backtracking can be slow and when to consider optimizations or different approaches.

Self-Check

"What if we used memoization to store palindrome checks? How would the time complexity change?"

Practice

1. What is the main goal of palindrome partitioning using backtracking?

easy

A. To count the number of palindromes in a string

B. To find the longest palindrome substring in a string

C. To split a string into all possible palindrome substrings

D. To reverse the string and check if it is a palindrome

Solution

1. Step 1: Understand palindrome partitioning

   Palindrome partitioning means dividing a string into parts where each part is a palindrome.

2. Step 2: Role of backtracking

   Backtracking helps explore all possible ways to split the string into palindrome parts.

3. Final Answer:

   To split a string into all possible palindrome substrings -> Option C

4. Quick Check:

   Palindrome partitioning = splitting into palindrome substrings [OK]

Hint: Backtracking explores all palindrome splits [OK]

Common Mistakes:

• Confusing palindrome partitioning with longest palindrome substring
• Thinking it only counts palindromes instead of listing partitions
• Assuming it reverses the whole string

2. Which of the following is the correct Java method signature for a backtracking function that partitions a string into palindromes?

easy

A. boolean backtrack(String s, int start, List<String> current)

B. void backtrack(String s, int start, List result, List<String> current)

C. int backtrack(String s, int start, List<String> result)

D. String backtrack(String s, int start, List result)

Solution

1. Step 1: Analyze method parameters

   The method needs the string, current start index, a list to store all partitions, and the current partition path.

2. Step 2: Check return type

   Backtracking methods usually return void because they build results via parameters.

3. Final Answer:

   void backtrack(String s, int start, List result, List<String> current) -> Option B

4. Quick Check:

   Backtracking method returns void and uses lists [OK]

Hint: Backtracking returns void and uses lists for results [OK]

Common Mistakes:

• Using wrong return type like int or boolean
• Missing current partition list parameter
• Returning String instead of void

3. What will be the output partitions for the input string "aab" using palindrome partitioning?

medium

A. [["aab"]]

B. [["aa", "ab"]]

C. [["a", "ab"]]

D. [["a", "a", "b"], ["aa", "b"]]

Solution

1. Step 1: Identify palindrome substrings

   "a" and "aa" are palindromes; "b" is a palindrome; "ab" is not.

2. Step 2: List all palindrome partitions

   Partitions are ["a", "a", "b"] and ["aa", "b"].

3. Final Answer:

   [["a", "a", "b"], ["aa", "b"]] -> Option D

4. Quick Check:

   Only palindrome splits included [OK]

Hint: Check each substring palindrome before partitioning [OK]

Common Mistakes:

• Including non-palindrome substrings like "ab"
• Missing the partition with "aa"
• Returning only one partition

4. Identify the error in this Java backtracking snippet for palindrome partitioning:

void backtrack(String s, int start, List result, List<String> current) {
  if (start == s.length()) {
    result.add(current);
    return;
  }
  for (int end = start; end < s.length(); end++) {
    if (isPalindrome(s, start, end)) {
      current.add(s.substring(start, end + 1));
      backtrack(s, end + 1, result, current);
      current.remove(current.size());
    }
  }
}

medium

A. Incorrect removal index in current.remove()

B. Missing base case for empty string

C. Wrong substring indices in s.substring()

D. Backtracking call uses wrong start index

Solution

1. Step 1: Check removal from current list

   List indices start at 0, so removing at current.size() causes IndexOutOfBoundsException.

2. Step 2: Correct removal index

   Should remove last element with current.remove(current.size() - 1).

3. Final Answer:

   Incorrect removal index in current.remove() -> Option A

4. Quick Check:

   Remove last element with size()-1 [OK]

Hint: Remove last added element with size()-1 index [OK]

Common Mistakes:

• Removing element at size() instead of size()-1
• Confusing substring indices
• Incorrect recursive call start index

5. Given the string "racecar", how many palindrome partitions will the backtracking algorithm generate?

hard

A. 6

B. 8

C. 4

D. 10

Solution

1. Step 1: Identify palindrome substrings in "racecar"

   Palindromes include "r", "a", "c", "e", "cec", "aceca", "racecar" and their combinations.

2. Step 2: Count all palindrome partitions

   Partitions are:
   ["racecar"],
   ["r", "aceca", "r"],
   ["r", "a", "cec", "a", "r"],
   ["r", "a", "c", "e", "c", "a", "r"],
   and others including splits like ["r", "a", "c", "ec", "a", "r"] are invalid because "ec" is not palindrome.
   Counting all valid partitions, the total is 6.

3. Final Answer:

   6 -> Option A

4. Quick Check:

   Count all palindrome partitions = 6 [OK]

Hint: Count all palindrome splits including nested palindromes [OK]

Common Mistakes:

• Counting only single-character partitions
• Missing longer palindrome substrings
• Double counting partitions