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Suppose we want to arrange the n numbers stored in an array such that all negative values occur before all positive ones. The minimum number of exchanges required in the worst case is: začněte se učit
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The time complexity of linear search is given by: začněte se učit
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a = 0 N=1000 for i in range(0, N,1): for j in range(N, 0,-1): a = a + i + j; print(a) The running time is: začněte se učit
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The complexity of recursive Fibonacci series is začněte se učit
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N=5 a = 0 i = N while (i > 0): a = a + i; i = i/2; The running time is: začněte se učit
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Consider the following function: T(n) = n if n ≤ 3 T(n) = T(n-1) + T(n-2) - T(n-3) otherwise The running time is: začněte se učit
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The time complexity of an algorithm T(n), where n is the input size, is given by T(n) = T(n - 1) + 1/n if n > 1 The order of this algorithm is začněte se učit
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Which of the following best describes the useful criterion for comparing the efficiency of algorithms? začněte se učit
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Which of the following is not O(n2)? začněte se učit
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Suppose T(n) = 2T(n/2) + n, T(0) = T(1) = 1 Which one of the following is false začněte se učit
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The following statement is valid. log(n!) = \theta (n log n). začněte se učit
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To verify whether a function grows faster or slower than the other function, we have some asymptotic or mathematical notations, which is_________. začněte se učit
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Big Omega Ω (f), Big Oh O (f), Big Theta θ (f)
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An algorithm performs lesser number of operations when the size of input is small, but performs more operations when the size of input gets larger. State if the statement is True or False or Maybe. začněte se učit
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An algorithm that requires ........ operations to complete its task on n data elements is said to have a linear runtime. začněte se učit
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The complexity of adding two matrices of order m*n is začněte se učit
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The order of an algorithm that finds whether a given Boolean function of 'n' variables, produces a 1 is začněte se učit
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The concept of order (Big O) is important because začněte se učit
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When we say an olgorithm has a time complexity of O(n), what does it mean? začněte se učit
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The computation time taken by the algorithm is proportional to n
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What is recurrence for worst case of QuickSort and what is the time complexity in Worst case? začněte se učit
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Recurrence is T(n) = T(n-1) + O(n) and time complexity is O(n^2)
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Suppose we are sorting an array of eight integers using quicksort, and we have just finished the first partitioning with the array looking like this: 2 5 1 7 9 12 11 10 Which statement is correct? začněte se učit
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The pivot could be either the 7 or the 9.
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Which of the following is not an in-place sorting algorithm? začněte se učit
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Running merge sort on an array of size n which is already sorted is začněte se učit
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začněte se učit
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Which of the following algorithm design technique is used in the quick sort algorithm? začněte se učit
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