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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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 ...
Given an array of integersnumsand an integertarget, return indices of the two numbers such that they add up totarget. You may assume that each input would have exactly one solution, and you may not use the same element twice. You can return the answer in any order. ...
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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 ...
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...
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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 ...