In my previous post, I have explained about longest monotonically increasing sub-sequence (LIS) problem in detail. However, the post only covered code related to querying size of LIS, but not the construction of LIS. I left it as an exercise. If you have solved, cheers. If not, you are not alone, here is code.
If you have not read my previous post, read here. Note that the below code prints LIS in reverse order. We can modify print order using a stack (explicit or system stack). I am leaving explanation as an exercise (easy).
#include <iostream> #include <string.h> #include <stdio.h> using namespace std; // Binary search int GetCeilIndex( int A[], int T[], int l, int r, int key) { int m; while ( r - l > 1 ) { m = l + (r - l)/2; if ( A[T[m]] >= key ) r = m; else l = m; } return r; } int LongestIncreasingSubsequence( int A[], int size) { // Add boundary case, when array size is zero // Depend on smart pointers int *tailIndices = new int [size]; int *prevIndices = new int [size]; int len; memset (tailIndices, 0, sizeof (tailIndices[0])*size); memset (prevIndices, 0xFF, sizeof (prevIndices[0])*size); tailIndices[0] = 0; prevIndices[0] = -1; len = 1; // it will always point to empty location for ( int i = 1; i < size; i++ ) { if ( A[i] < A[tailIndices[0]] ) { // new smallest value tailIndices[0] = i; } else if ( A[i] > A[tailIndices[len-1]] ) { // A[i] wants to extend largest subsequence prevIndices[i] = tailIndices[len-1]; tailIndices[len++] = i; } else { // A[i] wants to be a potential condidate of future subsequence // It will replace ceil value in tailIndices int pos = GetCeilIndex(A, tailIndices, -1, len-1, A[i]); prevIndices[i] = tailIndices[pos-1]; tailIndices[pos] = i; } } cout << "LIS of given input" << endl; for ( int i = tailIndices[len-1]; i >= 0; i = prevIndices[i] ) cout << A[i] << " " ; cout << endl; delete [] tailIndices; delete [] prevIndices; return len; } int main() { int A[] = { 2, 5, 3, 7, 11, 8, 10, 13, 6 }; int size = sizeof (A)/ sizeof (A[0]); printf ( "LIS size %d\n" , LongestIncreasingSubsequence(A, size)); return 0; } |
Exercises:
1. You know Kadane‘s algorithm to find maximum sum sub-array. Modify Kadane’s algorithm to trace starting and ending location of maximum sum sub-array.
2. Modify Kadane‘s algorithm to find maximum sum sub-array in a circular array. Refer GFG forum for many comments on the question.
3. Given two integers A and B as input. Find number of Fibonacci numbers existing in between these two numbers (including A and B). For example, A = 3 and B = 18, there are 4 Fibonacci numbers in between {3, 5, 8, 13}. Do it in O(log K) time, where K is max(A, B). What is your observation?
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