To solve this problem, you need to split the given string into a Fibonacci-like sequence using backtracking. At each step, check whether the sum of the last two numbers matches the next number in the sequence. Prune invalid paths early to optimize performance, especially when dealing with large strings. Pay special attention to handling cases like leading zeros in numbers.
Problem Statement
You are given a string of digits, num. Your task is to split the string into a Fibonacci-like sequence, where each number in the sequence is the sum of the two preceding ones.
A valid Fibonacci-like sequence must adhere to the following conditions: each number is non-negative, there are no leading zeroes unless the number is exactly zero, and the sequence must follow the Fibonacci property.
Examples
Example 1
Input: num = "1101111"
Output: [11,0,11,11]
The output [110, 1, 111] would also be accepted.
Example 2
Input: num = "112358130"
Output: []
The task is impossible.
Example 3
Input: num = "0123"
Output: []
Leading zeroes are not allowed, so "01", "2", "3" is not valid.
Constraints
- 1 <= num.length <= 200
- num contains only digits.
Solution Approach
Backtracking Search
Start by exploring all possible pairs of the first two numbers. For each pair, attempt to build the sequence by checking if the sum of the two numbers matches the next number in the string. Use recursion to explore further, pruning invalid paths early to reduce the search space.
Pruning Invalid Paths
Prune paths where the sum of the two numbers does not match the next number in the sequence. Additionally, avoid paths that start with invalid numbers, like those with leading zeros. This step reduces unnecessary computations and improves the efficiency of the backtracking approach.
Edge Case Handling
Ensure that you handle edge cases such as when there are leading zeroes in the sequence or when it's impossible to split the string into a valid Fibonacci-like sequence. In such cases, return an empty list.
Complexity Analysis
| Metric | Value |
|---|---|
| Time | O(N^2) |
| Space | O(N) |
The time complexity is O(N^2) due to the need to check each possible pair of the first two numbers and recursively build the sequence. The space complexity is O(N) as we store the numbers during backtracking.
What Interviewers Usually Probe
- Can the candidate efficiently prune invalid paths without exploring every possibility?
- Does the candidate handle edge cases, such as invalid numbers with leading zeros, correctly?
- Can the candidate explain the backtracking approach and its pruning mechanism in detail?
Common Pitfalls or Variants
Common pitfalls
- Not handling the case where the sequence contains leading zeroes.
- Overlooking cases where it's impossible to form a valid sequence, leading to an infinite search.
- Not efficiently pruning invalid paths, causing unnecessary recursive calls.
Follow-up variants
- What if the string is extremely long? Consider optimizations for larger input sizes.
- What if the Fibonacci-like sequence can contain numbers with more than one digit?
- Can you extend the problem to include larger numbers beyond typical integer ranges?
How GhostInterview Helps
- GhostInterview guides you in refining your backtracking search approach to efficiently handle pruning, ensuring you don’t explore unnecessary paths.
- GhostInterview helps in debugging edge cases, providing insights into common pitfalls like leading zeroes and impossible sequences.
- GhostInterview provides clear, structured approaches to solve string-based backtracking problems, making it easier to stay on track during an interview.
Topic Pages
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- LeetCode Interview Copilot - Use GhostInterview as a live solver when you want direct help with LeetCode-style coding questions.
- Coding Interview Assistant - See how GhostInterview supports array, string, linked list, graph, and tree interview workflows.
- How GhostInterview Works - Review the screenshot, reasoning, and answer flow before using the solver in a live interview.
FAQ
How do I approach solving the Split Array into Fibonacci Sequence problem?
Start by using backtracking to explore all possible splits. Prune invalid paths where the sum of the last two numbers doesn't match the next part of the sequence.
What is the time complexity of the solution?
The time complexity is O(N^2), as each potential pair of the first two numbers is checked and the sequence is built recursively.
What should I do when I encounter a leading zero in a number?
Avoid numbers that start with leading zeroes, unless the number is exactly zero. This is a common constraint in string-based number problems.
Can this problem be solved with a dynamic programming approach?
While backtracking is the most intuitive approach, dynamic programming could be used if you need to store previously computed sums, but it may not be as efficient in this case.
How does pruning help in solving this problem?
Pruning helps by cutting off invalid paths early in the recursion, preventing unnecessary checks and reducing the overall complexity of the solution.
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Open problem page