Longest non-decreasing Subsequence with adjoining variations

Given an array arr[] of N components, the duty is to search out the size of the longest non-decreasing subsequence such that the variations between adjoining components are non-decreasing. For indices i, j, and okay: 
i < j < okay, ai − aj ≤ aj − ak

Examples:

Enter: N = 9, arr[] = [1, 3, 5, 4, 7, 8, 10, 6, 9]
Output: 6
Clarification: Longest non-decreasing Subsequence is [1, 3, 5, 7, 8, 10]. Right here, the subsequence satisfies the given situation, (3 – 1) <= (5 – 3), (4 – 3) <= (5 – 4), (7 – 5) <= (8 – 7), (8 – 7) <= (10 – 8), and (10 – 8) <= (9 – 6). The size of such Longest Subsequence on this case is 6.

Enter: N = 8, arr[] = [1, 4, 5, 6, 2, 3, 8, 9]
Output: 5
Clarification: Longest non-decreasing Subsequence with given situation is [1, 4, 6, 8, 9].

Method: To resolve the issue observe the beneath concept: 

The thought is to make use of a dp array to retailer the size of such longest subsequence as much as index i of the given array. Then, we traverse the array for every index i, and for every index j (0 ≤ j < i), we verify if the subsequence [j, i] kinds a subsequence. If it does, we replace the worth of dp[i] with the utmost worth of dp[j]+1. Lastly, we return the utmost worth within the dp array.

Under are the steps for the above strategy:

• Initialize a variable say n to retailer the dimensions of the enter array arr.
• Test if the dimensions of the given array is lower than or equal to 2, and return the dimensions of the array.
• Initialize an array dp of dimension n  all 0’s.
• Initialize the primary two values of the dp to 1 and a pair of respectively.
• Initialize a variable say ans = 2.
• Iterate from index i = 2 to i < n,
  • Contained in the for loop, run one other loop from index j = 1 to j < i
    • Test if the present factor and the earlier factor type a subsequence with the given situation, if (arr[i] – arr[j] == arr[j] – arr[j-1]),
    • Replace the dp[] array with the utmost worth between the present dp worth of i and dp worth of j + 1.
  • Replace the ans variable with the utmost worth between itself and the present dp worth of i.
• Return ans.

Under is the implementation for the above strategy:

Longest Subsequence: 3

Time Complexity: O(n²)
Auxiliary House: O(n), the place n is the size of the given array.

Grasping Method: To resolve the issue observe the beneath concept:

The thought is to make use of a variable ‘len’ to retailer the size of the longest subsequence seen up to now, and one other variable ‘curr_len’ to retailer the size of the present subsequence. Additionally, use a variable ‘diff’ to retailer the distinction between two consecutive components of the present subsequence. Initialize ‘len’ and ‘curr_len’ with 2, as any two components type a subsequence. Traverse the array and verify if the distinction between two consecutive components is identical because the ‘diff’ variable, increment ‘curr_len’, else, replace the ‘len’ variable with the utmost worth between ‘len’ and ‘curr_len’, and reset ‘curr_len’ to 2, and ‘diff’ to the distinction between the 2 consecutive components. Return the utmost worth between ‘len’ and ‘curr_len’.

Under are the steps for the above strategy:

• Initialize a variable say n to retailer the dimensions of the enter array arr.
• Test if the dimensions of the given array is lower than or equal to 2, and return the dimensions of the array.
• Initialize a variable len = 2, because the size of the smallest subsequence, is 2.
• Initialize a variable diff to the distinction between the second and the primary components of the enter array arr.
• Initialize the present size of subsequence as 2, curr_len = 2.
• Traverse the array from index i = 2 to i < n and verify if the distinction between the present factor and the earlier factor is identical because the distinction between the earlier two components, increment curr_length by 1.
• Else if the distinction between the present factor and the earlier factor shouldn’t be the identical because the distinction between the earlier two components, replace the distinction between the consecutive components, diff = arr[i] – arr[i-1].
• Replace size of longest subsequence, len = max(len, curr_len).
• The present size of the subsequence is reset to 2.
• As soon as the for loop completes, the size of the longest subsequence is up to date by taking the utmost worth between itself and the present size of the subsequence, len = max(len, curr_len).
• Return the worth of variable len.

Under is the implementation for the above strategy:

Longest non-decreasing Subsequence: 3

Time Complexity: O(n), the place n is the size of the given array.
Auxiliary House: O(1), since we’re not utilizing any further information constructions.