Let's pretend we're interested in checking whether there are any duplicates in an unsorted array of n entries, A. These components are whole numbers in the range [1, 2n]. Share the worst-case running time T(n) as an asymptotic order. Seek for the most effective algorithm.

Let's pretend we're interested in checking whether there are any duplicates in an unsorted array of n entries, A. These components are whole numbers in the range [1, 2n]. Share the worst-case running time T(n) as an asymptotic order. Seek for the most effective algorithm.

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There’s a length n (n ≥ 3) array and we’d like to fill in each element of the array with an integer.The integers should be in {1, 2, . . . , k} and any 3 adjacency entries cannot have the same number. Given nand k , design a polynomial time algorithm to compute the number of ways to fill this array. Please alsojustify the correctness and the time complexity of your answers.
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Algorthim of how to find the largest element missing in an unsorted array of n positive integers and the running time of the algorthim is in O(n). Example of this is that the n=6 array C= [5,90,8,6,26,9] The largest element missing in the array is 7
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I warn you don't use AI to generate answer and don't try to copy other's work otherwise I'll give you multiple downvotes for sure if I see plagiarism.
Please send me answer of this question with pseudo code, algorithms and time complexity and answers all the parts nd i will rate you sure sir please.
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Question A: Mapping. The two-sum problem is a popular algorithm problem. Given an array of integers and an integer target, return indices of the two numbers such that they add up to the target. You may assume that each input would have exactly one solution, and you may not use the same element twice. For example, suppose we have an array arr = [1,10,100]. If the target is 11, you should return [0,1] because arr[0] + arr[1] = 1+ 10 = 11. If the target is 101, you should return [0,2] because arr[0] + arr[2] = 1+ 100 = 101. Complete this problem in O(n). Hint: using a hash table.
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