Tony's Log HackerRank - The Maximum Subarray 4 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)returnretdefmaxSubar...
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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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))...
Problems must exhibit two properties in order to implement a Greedy solution: Optimal Substructure An optimal solution to the problem contains optimal solutions to the given problem's subproblems The Greedy Property An optimal solution is reached by "greedily" choosing the locally optimal choice without...
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)) ...
├── Array │ ├── bestTimeToBuyAndSellStock.java │ ├── findTheCelebrity.java │ ├── gameOfLife.java │ ├── increasingTripletSubsequence.java │ ├── insertInterval.java │ ├── longestConsecutiveSequence.java │ ├── maximumProductSubarray.java │ ├── maximumSubarray....
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 Algorithmsare 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 solutions ...
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 ...