This is basically the LeetCode text justification problem but they added a small twist to the padding rule that tripped me up.
Start by clarifying the problem constraints and edge cases, then outline a greedy line-packing algorithm. Walk through the algorithm step-by-step, emphasizing how to distribute spaces and handle the last line. Conclude with a clear time and space complexity analysis.
Pro tip: Mention that you can avoid unnecessary string concatenations by using a list of strings and joining at the end, which improves performance. Also, proactively discuss how you would test edge cases like a single word longer than W or empty input.
Ask questions to confirm constraints: Can words exceed W? Is W always positive? Are there multiple spaces between words? How should the last line be handled? This ensures you understand the problem fully before coding.
Iterate through words, accumulating them into a line until adding the next word would exceed W. For each complete line (except the last), distribute spaces as evenly as possible, with extra spaces on the left. For the last line, left-justify with single spaces.
For a line with k words and total characters C, the number of spaces to distribute is W - C. If k > 1, each gap gets at least (W - C) // (k - 1) spaces, and the first (W - C) % (k - 1) gaps get one extra space. If k == 1, the single word is left-justified and padded with spaces to width W.
After processing all words, the last line is left-justified with single spaces between words and padded with spaces to width W. Collect all lines into a list and return.
Time complexity is O(n) where n is the total number of characters in all words, as each character is processed a constant number of times. Space complexity is O(n) for the output, plus O(W) for temporary line construction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem and constraints, then propose a dynamic programming solution that tracks the sum of products ending at each index. Derive the recurrence relation and analyze time and space complexity, optimizing space to O(1) if possible.
Pro tip: Mention that the problem can be solved in O(n) time using DP, and discuss how to handle negative numbers and zeros, which can affect the product sums. Also, consider edge cases like empty array or single element.
Confirm that subarrays are contiguous and that we need to sum the products of all such subarrays. Ask about constraints, input size, and potential edge cases like empty array or large numbers.
Mention that a naive solution would iterate over all O(n^2) subarrays and compute products, leading to O(n^3) time if done naively, but can be optimized to O(n^2) by maintaining running product. This is not optimal for large n.
Define dp[i] as the sum of products of all subarrays ending at index i. Derive the recurrence: dp[i] = arr[i] * (1 + dp[i-1]). Then the total sum is the sum of dp[i] for all i.
Since dp[i] only depends on dp[i-1], we can use a single variable to keep track of the previous dp value, achieving O(1) space complexity.
State that the time complexity is O(n) and space is O(1). Discuss handling of negative numbers and zeros, and test with small examples to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.