Solve sliding window, heap, DP, in-place tasks
This interview consists of several LeetCode-style coding tasks. For each task, you are expected to implement a solution that meets the stated time and space complexity goals, and correctly handles all specified edge cases.
1) Longest Substring with All Distinct Characters (Sliding Window)
Input: A string s (composed of ASCII or lowercase letters).
Output: An integer representing the maximum length of a contiguous substring of s that contains no repeated characters.
Constraints: Target time complexity O(n).
2) K Closest Points to Origin (Heap / Top-K)
Input: An integer k and an array of points points, where each point is given as (x, y).
Output: Any ordering of the k points that have the smallest squared distance to the origin (x^2 + y^2).
Constraints: Must achieve O(n log k) time complexity and O(k) extra space (excluding the output array).
3) Minimum Items to Reach a Target Sum (Dynamic Programming)
Input: An array of positive integers values (where each value can be reused an unlimited number of times) and an integer amount.
Output: The minimum number of values required to sum up to amount. Return -1 if it's impossible to reach the target sum.
Constraints: Clearly explain the state definition and initialization. The typical target time complexity is O(len(values) * amount).
4) Find All Anagram Start Indices (Sliding Window)
Input: Two strings, s and p.
Output: A list of all starting indices i in string s such that the substring s[i : i+len(p)] is a permutation (anagram) of string p.
Constraints: Target O(|s|) time complexity, utilizing a fixed-size window and character counts.
5) Previous Permutation with One Swap (Array In-Place)
Input: An integer array a.
Output: The lexicographically largest array that is strictly smaller than a and can be obtained by at most one swap of two indices. If no such array exists, return a unchanged.
Constraints: The solution must be in-place. Target time complexity is O(n) or O(n log n). Clearly discuss corner cases, including duplicates, arrays that are already the smallest possible permutation, and repeated values near the pivot point.
AI interview prep
Core dilemma
Demonstrating mastery across diverse algorithmic paradigms (sliding window, heaps, DP, array manipulation) with optimal time/space complexity and robust edge case handling, all within a constrained interview timeframe.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Solve sliding window, heap, DP, in-place tasks
This interview consists of several LeetCode-style coding tasks. For each task, you are expected to implement a solution that meets the stated time and space complexity goals, and correctly handles all specified edge cases.
1) Longest Substring with All Distinct Characters (Sliding Window)
Input: A string s (composed of ASCII or lowercase letters).
Output: An integer representing the maximum length of a contiguous substring of s that contains no repeated characters.
Constraints: Target time complexity O(n).
2) K Closest Points to Origin (Heap / Top-K)
Input: An integer k and an array of points points, where each point is given as (x, y).
Output: Any ordering of the k points that have the smallest squared distance to the origin (x^2 + y^2).
Constraints: Must achieve O(n log k) time complexity and O(k) extra space (excluding the output array).
3) Minimum Items to Reach a Target Sum (Dynamic Programming)
Input: An array of positive integers values (where each value can be reused an unlimited number of times) and an integer amount.
Output: The minimum number of values required to sum up to amount. Return -1 if it's impossible to reach the target sum.
Constraints: Clearly explain the state definition and initialization. The typical target time complexity is O(len(values) * amount).
4) Find All Anagram Start Indices (Sliding Window)
Input: Two strings, s and p.
Output: A list of all starting indices i in string s such that the substring s[i : i+len(p)] is a permutation (anagram) of string p.
Constraints: Target O(|s|) time complexity, utilizing a fixed-size window and character counts.
5) Previous Permutation with One Swap (Array In-Place)
Input: An integer array a.
Output: The lexicographically largest array that is strictly smaller than a and can be obtained by at most one swap of two indices. If no such array exists, return a unchanged.
Constraints: The solution must be in-place. Target time complexity is O(n) or O(n log n). Clearly discuss corner cases, including duplicates, arrays that are already the smallest possible permutation, and repeated values near the pivot point.