## What is longest monotonically increasing subsequence?

In computer science, the longest increasing subsequence problem is to find a subsequence of a given sequence in which the subsequence’s elements are in sorted order, lowest to highest, and in which the subsequence is as long as possible.

**How do you find the longest increasing subsequence?**

The Longest Increasing Subsequence (LIS) problem is to find the length of the longest subsequence of a given sequence such that all elements of the subsequence are sorted in increasing order. For example, the length of LIS for {10, 22, 9, 33, 21, 50, 41, 60, 80} is 6 and LIS is {10, 22, 33, 50, 60, 80}.

**How do you find the longest increasing subsequence in C?**

Let arr[0..n-1] be the input array and L(i) be the length of the LIS ending at index i such that arr[i] is the last element of the LIS. L(i) = 1, if no such j exists. To find the LIS for a given array, we need to return max(L(i)) where 0 < i < n.

### What is Big O of n log n?

At each level of the binary tree the number of calls to the merge function doubles but the merge time is halved, so the merge performs a total of N iterations per level. This means that the overall time complexity of a Merge sort is O(N log N).

**What is longest decreasing subsequence?**

The longest decreasing subsequence problem is to find a subsequence of a given sequence in which the subsequence’s elements are in sorted order, highest to lowest, and in which the subsequence is as long as possible. This subsequence is not necessarily contiguous or unique.

**How do you find the longest non decreasing subsequence?**

longest nondecreasing subsequence in O(nlgn)

- The first step: 30 is not greater than or equal to 20. We find the smallest element greater than 20.
- The second step: 20 is greater than or equal to 20. We extend the sequence and s now contains “20 20”
- The third step: 10 is not greater than or equal to 20.

#### How do you find the longest increasing subsequence in Python?

Longest Increasing Subsequence in Python

- trail := an array of length 0 to length of nums – 1, and fill this with 0.
- size := 0.
- for x in nums. i := 0, j := size. while i is not j. mid := i + (j – i) / 2. if trails[mid] < x, then i := mid + 1, otherwise j := mid. trails[i] := x. size := maximum of i + 1 and size.
- return size.

**How do you find the longest increasing Subarray?**

Given an array containing n numbers. The problem is to find the length of the longest contiguous subarray such that every element in the subarray is strictly greater than its previous element in the same subarray. Time Complexity should be O(n).

**What is the length of the longest common subsequence?**

What is the length of the longest common subsequence? Explanation: The longest common subsequence is “PRTPQRS” and its length is 7.

## What is longest monotone sequence?

For example, the sequence {6, 4, 3, 2, 5, 8, 9, 7, 1, 10} contains the sequence 4, 5, 7, 10, (and other 16 such). Of course, there are examples with more than four increasing terms. In fact, this sequence contains the larger subsequence 4, 5, 8, 9, 10.

**Is O N better than O Logn?**

O(n) means that the algorithm’s maximum running time is proportional to the input size. basically, O(something) is an upper bound on the algorithm’s number of instructions (atomic ones). therefore, O(logn) is tighter than O(n) and is also better in terms of algorithms analysis.

**How to calculate length of longest increasing subsequence?**

Length of Longest Increasing Subsequence is 6. Complexity: The loop runs for N elements. In the worst case (what is worst case input?), we may end up querying ceil value using binary search (log i) for many A[i]. Therefore, T(n) < O( log N! ) = O(N log N). Analyse to ensure that the upper and lower bounds are also O( N log N ).

### Which is the largest sequence in the array?

Making 1 as new sequence will create new sequence which is largest. The observation is, when we encounter new smallest element in the array, it can be a potential candidate to start new sequence. From the observations, we need to maintain lists of increasing sequences. In general, we have set of active lists of varying length.

**Which is the simplest implementation of the O ( nlogn ) approach?**

If you want to understand the O (NlogN) approach, it’s explained very clearly here. In this post, a simple and time-saving implementation of O (NlogN) approach using stl is discussed. Below is the code for LIS O (NlogN) : This article is contributed by Raj Kumar.

**Is there an O ( n * lg ( n ) ) solution?**

This is the O (n*lg (n)) solution from The Hitchhiker’s Guide to the Programming Contests (note: this implementation assumes there are no duplicates in the list): To account for duplicates one could check, for example, if the number is already in the set.