Dynamic Programming/Longest Increasing SubsequenceMedium

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Longest Increasing Subsequence

Dynamic Programming·Medium

Given an integer array nums, return the length of the longest strictly increasing subsequence.

A subsequence is derived from another array by deleting some or no elements without changing the order of the remaining elements.

The O(n log n) approach uses patience sorting: maintain an array tails where tails[k] is the smallest tail of any increasing subsequence of length k + 1, and binary-search to find the position to extend or replace.

Examples

Input: nums = [10,9,2,5,3,7,101,18]

Output: 4

[2,3,7,101] or [2,3,7,18] both have length 4.

Input: nums = [0,1,0,3,2,3]

Output: 4

Input: nums = [7,7,7,7,7,7,7]

Output: 1

Constraints

1 <= nums.length <= 2500
-10^4 <= nums[i] <= 10^4

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Tests

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Passed32 ms

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Your approach

Pattern:Dynamic Programming
Time:O(N)
Space:O(1)

Complete approach — pattern and complexity both named.

Coach

What you got right

You correctly matched the sorted input requirement and implemented a linear-time scan using two pointers.

Where it diverged

No divergence detected. The code correctly aligns with the stated approach.

Next attempt: focus on

Try solving related sliding window problems to build familiarity with two-pointer variants.

Signals

No signals fired — clean run, you stayed in flow.