First, clarify the problem and confirm the sliding window pattern applies by identifying the condition that makes a window valid. Then, implement the window with two pointers, expanding the right pointer and shrinking the left pointer as needed, while maintaining the required state (e.g., sum, frequency map). Finally, test with edge cases and analyze time/space complexity.
Pro tip: Before coding, verbally outline your approach and ask clarifying questions to show you think before typing. This also buys you time to consider edge cases and avoid bugs.
Restate the problem in your own words and ask clarifying questions about input constraints, expected output, and edge cases (e.g., empty input, negative numbers).
Determine if the problem involves a contiguous subarray/substring and a condition that can be maintained incrementally. Decide between fixed-size or variable-size window.
Define the state to maintain (e.g., sum, character counts), and outline the steps for expanding and shrinking the window. Consider using a hash map for frequency tracking if needed.
Write clean code with meaningful variable names. Test with provided examples and edge cases, walking through the logic step by step.
State the time and space complexity of your solution, typically O(n) time and O(k) space where k is the size of the state (e.g., alphabet size).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem constraints and identify the decision points that require backtracking. Then, design a recursive solution with pruning and validate it with small examples before coding. Finally, implement iteratively, test edge cases, and analyze time/space complexity.
Pro tip: Verbally explain your thought process and trade-offs as you code; interviewers value clear reasoning over silent typing. If stuck, simplify the problem or start with a brute-force approach and optimize later.
Restate the problem in your own words, ask clarifying questions about input size, constraints, and expected output. Confirm the backtracking requirement and any edge cases.
Identify the state space, decision points, and base cases. Outline the recursive function signature and how to undo choices (backtrack). Consider pruning strategies to reduce unnecessary exploration.
Write the code step by step, explaining each part. Start with a simple version that works for small inputs, then add optimizations like pruning or memoization if needed.
Walk through the code with a small example, checking base cases and recursive calls. Test edge cases such as empty input, single element, or maximum constraints.
State the time and space complexity of your solution, considering the branching factor and depth of recursion. Discuss potential improvements or trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.