Tony's Log HackerRank - The Maximum Subarray 67 23 23 Point: not necessarily contigous max sub array, at least one element should be selected: defmaxSubarrCont(arr, n): ret=arr[0] curr=arr[0]foriinrange(1, n): curr= max(arr[i], curr +arr[i]) ret=max(ret, curr)returnretdef...
Point: not necessarily contigous max sub array, at least one element should be selected: defmaxSubarrCont(arr, n): ret=arr[0] curr=arr[0]foriinrange(1, n): curr= max(arr[i], curr +arr[i]) ret=max(ret, curr)returnretdefmaxSubarrNoCont(arr, n):#Corner case: no empty ret ...
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HackerRank 2D Array - DS 🟢Easy Array Data Structures / Arrays HackerRank Sales by Match 🟢Easy Array Algorithms / Implementation HackerRank Minimum Absolute Difference in an Array 🟢Easy Greedy Algorithms / Greedy CTCI - Cracking The Coding Interview PROBLEM DESCR.SOLUTIONDIFFICULTYRELATED TOPICSADD...
A brute force solution where we check all the substring one by one to see if it has no duplicate character. An optimized sliding window approach where we slide the window whenever we found a repeating character and update the maximum length. ...
Mergesortis also a divide and conquer algorithm. It continuously divides an array into two halves, recurses on both the left subarray and right subarray and then merges the two sorted halves Stable:Yes Time Complexity: Best Case:O(nlog(n)) ...
It continuously divides an array into two halves, recurses on both the left subarray and right subarray and then merges the two sorted halves Stable: Yes Time Complexity: Best Case: O(nlog(n)) Worst Case: O(nlog(n)) Average Case: O(nlog(n))...
Mergesortis also a divide and conquer algorithm. It continuously divides an array into two halves, recurses on both the left subarray and right subarray and then merges the two sorted halves Stable:Yes Time Complexity: Best Case:O(nlog(n)) ...
It continuously divides an array into two halves, recurses on both the left subarray and right subarray and then merges the two sorted halves Stable: Yes Time Complexity: Best Case: O(nlog(n)) Worst Case: O(nlog(n)) Average Case: O(nlog(n))...
Greedy Algorithms are algorithms that make locally optimal choices at each step in the hope of eventually reaching the globally optimal solution Problems must exhibit two properties in order to implement a Greedy solution: Optimal Substructure An optimal solution to the problem contains optimal ...