Longest Increasing Subsequence
A medium-tier problem at 58% community acceptance, tagged with Array, Binary Search, Dynamic Programming. Reported in interviews at Licious and 22 others.
Longest Increasing Subsequence hits your OA and you blank. Twenty-three companies ask this, including TikTok, PayPal, and Intuit. The naive DP approach works but tanks on large inputs. The binary search optimization is the trick nobody remembers under pressure. If you haven't drilled the pattern, StealthCoder surfaces a working solution invisible to the proctor, solving it in seconds while you stay calm.
Companies that ask "Longest Increasing Subsequence"
Longest Increasing Subsequence is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderThe problem asks for the length of the longest strictly increasing subsequence in an array. Most candidates jump to O(n squared) DP: for each position, check all prior positions and track the max. It's correct but slow. The real solution uses binary search on a patience-sorting array. Maintain a small array where index i holds the smallest tail value of all length-(i+1) subsequences. For each new number, binary search to find where it fits, then update. This drops complexity to O(n log n) and actually passes. The trick is recognizing that you don't need the full DP table, just the tails. StealthCoder is your hedge if the optimization doesn't click during the live assessment.
Pattern tags
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Longest Increasing Subsequence recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Longest Increasing Subsequence interview FAQ
Is Longest Increasing Subsequence still actually asked at big tech?+
Yes. It appears in reports from TikTok, PayPal, Intuit, Samsung, and others. Twenty-three companies in the dataset alone have asked it. It's a classic that never fully left the interview rotation. Acceptance sits at 58%, so it's hard enough to filter but common enough to prepare.
What's the difference between LIS and Longest Increasing Subarray?+
Subsequence is non-contiguous. Subarray must be contiguous. LIS is harder because you can skip elements. For this problem, you're finding the longest non-contiguous increasing sequence, which requires DP or binary search, not a simple sliding window.
Why does the O(n squared) DP solution fail in interviews?+
It doesn't fail logically, but it times out on large inputs. Interviewers often set constraints that force O(n log n). If you code O(n squared) first, you're buying time, but mentioning the binary search optimization early signals you know the pattern and helps you avoid the timeout trap.
How does binary search fit into this problem?+
Binary search isn't searching the original array. It's searching a constructed tails array where tails[i] is the smallest ending value of all increasing subsequences of length i+1. Each new element binary searches to find its position in tails, updating it. This keeps the array sorted and lets you solve in O(n log n).
Do I need to return the actual subsequence or just the length?+
Most versions ask for length only. If the prompt asks for the actual sequence, you'll need to track parent pointers or reconstruct from the DP table afterward. Check the exact problem statement in your assessment. Length-only is the standard.
Want the actual problem statement? View "Longest Increasing Subsequence" on LeetCode →