Longest increasing subsequence
Python data engineering interview problem. Difficulty: advanced. Pattern: Dynamic Programming. About 20 minutes. Part of the Pro drill bank.
Length of the longest strictly increasing subsequence of a list. Treat this as a production helper: match the contracted return shape, including empty and duplicate inputs.
A metric series may improve over time with noisy values between improvements. A subsequence can skip noise but must keep original order. Return the length of the longest strictly increasing subsequence. Equal values do not increase the length. Write lis_length(nums: list[int]) -> int. In [2,2,3,3], choose one 2 and one 3, so the strict length is 2. The starter file already contains the function name, harness and print lines. Keep them and fill only the function body.
Requirements
- Preserve original order.
- Allow skipped values.
- Require every next value to be strictly larger.
Constraints
- The input may be empty and values can repeat.
- Return an integer length.
Examples
Input: lis_length([2, 2, 3, 3]) Output: 2 The repeated values do not extend a strictly increasing subsequence: 2, 3 is the longest, so the length is 2.
Topics: lakebench, python, subsequence, patience sorting.
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Longest increasing subsequence
Interview-style drill: Length of the longest strictly increasing subsequence of a list.
Implement `lis_length(nums: list[int]) -> int`. A subsequence keeps the original order but may skip elements. Return the length of the longest **strictly** increasing subsequence of `nums`. Equal values do not extend a subsequence. An empty list gives `0`.