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In this question, you will construct the max heap that results from using BUILD-MAX-HEAP to create a heap from the following array:  . Steps 1-2 were performed above.  Now perform step 3: Step 3.  Fill in the tree which results after the second exchange is performed.   [C5] / \ [C10] [C12] / \ / \ [C7] [C8] [C11] [C9] / \ / \ [C6] [C2] [C4] [C3]  

In this question, you will find the maximum subarray of the array A = [1, -2, 8, -25, 3, 4, -1, 3] using the θ(nlog(n)) divide and conquer algorithm discussed in class. Step 5: Give the recurrence relation for the maximum subarray algorithm FIND-MAXIMUM-SUBARRAY.  That is fill in the right hand side of the following equation: T(n) =

In this question, you will construct the max heap that results from using BUILD-MAX-HEAP to create a heap from the following array:  . Steps 1-4 were performed above.  Now perform step 5: Step 5.  Fill in the tree which results after the fourth exchange is performed. The tree is the finished max heap in the pointer representation.   [E12] / \ [E10] [E11] / \ / \ [E7] [E8] [E5] [E9] / \ / \ [E6] [E2] [E4] [E3]  

In this question, you will construct the max heap that results from using BUILD-MAX-HEAP to create a heap from the following array:  . Steps 1-3 were performed above.  Now perform step 4: Step 4.  Fill in the tree which results after the third exchange is performed.   [D12] / \ [D10] [D5] / \ / \ [D7] [D8] [D11] [D9] / \ / \ [D6] [D2] [D4] [D3]  

In this question, you will use dynamic programming to determine the longest common subsequence of   and . Step 1. Provide the recursive equation for the recurrence.  That is, complete the right hand side: c[i, j] =

In this question, you will use dynamic programming to determine the longest common subsequence of   and . Step 2. Use the LCS-Length algorithm to select the correct b-entry (arrow) and c-entry (number) for each entry in the table.   ____ j____ ____0____ 1 2 3 4 5 6 ____i____ ____yj____ A C G T T A ____0____ ____xi____ ____0____ 0 0 0 0 0 0 ____1____ ____C____ ____0____ [U-11][0-11] [UL-12][1-12] [L-13][1-13] [L-14][1-14] [L-15][1-15] [L-16][1-16] ____2____   ____A____ ____0____ [UL-21][1-21] [U-22][1-22] [U-23][1-23] [U-24][1-24] [U-25][1-25] [UL-26][2-26] ____3____ ____G____ ____0____ [U-31][1-31] [U-32][1-32] [UL-33][2-33] [L-34][2-34] [L-35][2-35] [U-36][2-36] ____4____ ____T____ ____0____ [U-41][1-41] [U-42][1-42] [U-43][2-43] [UL-44][3-44] [UL-45][3-45] [L-46][3-46] ____5____ ____A____ ____0____ [UL-51][1-51] [U-52][1-52] [U-53][2-53] [U-54][3-54] [U-55][3-55] [UL-56][4-56]          

In this question, you will find the maximum subarray of the array A = [1, -2, 8, -25, 3, 4, -1, 3] using the θ(nlog(n)) divide and conquer algorithm discussed in class. Step 5: Give the recurrence relation for the maximum subarray algorithm FIND-MAXIMUM-SUBARRAY.  That is fill in the right hand side of the following equation: T(n) =

In this question, you will construct the max heap that results from using BUILD-MAX-HEAP to create a heap from the following array:  . Steps 1-5 were performed above, yielding the finished max heap in Step 5. Now fill in the array A to give the array representation of the max heap. A[ 1 ] A[ 2 ] A[ 3 ] A[ 4 ] A[ 5 ] A[ 6 ] A[ 7 ] A[ 8 ] A[ 9 ] A[ 10 ] A[ 11 ] [12] [10] [11] [7] [8] [5] [9] [6] [2] [4] [3]

In this question, you will find the maximum subarray of the array A = [1, -2, 8, -25, 3, 4, -1, 3] using the θ(nlog(n)) divide and conquer algorithm discussed in class. Step 4: Select the array elements which constitute the maximum subarray.

Assume that you are given the following linear time algorithm: BSTNode *BuildBST(A, n): It takes an array and its size as arguments, builds a binary search tree, and returns a pointer to its root in θ(n) time. A BSTNode structure contains pointers LC, RC of type BSTNode* pointing to the left child and right child of that node respectively. In Step 1 above, you used the above function to write pseudocode to output the array in ascending order in linear time. Step 2: Explain in great detail why the algorithm you wrote is a linear time algorithm.