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Let T(n) = 7T(n/2) + 3n2 + 2.

Using Master Theorem and the Limits approach, show that T(n) = O(n3)

Using Master Theorem and the Limits approach, show that T(n) = O(n3)

Let T(n) = 7T(n/2) + 3n2 + 2.

Using Master Theorem and the Limits approach, show that T(n) = O(n3)

Using Master Theorem and the Limits approach, show that T(n) = O(n3)

Let T(n) = 3T(n/4) + nlogn. Solve the recurrence using Master Theorem.

Solve the following recurrence relation (without using Master Theorem)

C(n/2) + logn, for n > 1. C(1) = 0

C(n/2) + logn, for n > 1. C(1) = 0

Solve the following recurrence relation (without using Master Theorem)

C(n) = C(n/2) + logn, for n > 1. C(1) = 0

C(n) = C(n/2) + logn, for n > 1. C(1) = 0

Consider an array of prime integers in the range [1...20] with the entries randomly distributed. Find the average number of comparisons for a sequential search in the array.

COMPARE THE MERITS AND DEMERITS OF MACHINE LEVEL LANGUAGE AND ASSEMBLY LANGUAGE

DISCUSS THE ADVANTAGES AND DEMERITS OF THE TECHNOLOGICAL ADVANCES IN THE COMMUNICATION TECHNOLOGY

. Consider an array of prime integers in the range [1...20] with the entries randomly distributed. Find the average number of comparisons for a sequential search in the array.

Consider the following two variants of the pseudo code for the Insertion Sort algorithm. Using each variant, sort the array: 51 52 53 54 55. Note that 51, 52, ..., 55 are five different instants of integer 5 and need to be treated as separate elements (that are of the same numerical value). Determine the number of comparisons encountered with each of the two variants of the algorithm to sort the above array and what is the final sorted array (include the suffixes of the elements throughout your work).

Pseudo Code - I Pseudo Code - II

Pseudo Code - I Pseudo Code - II