(d) Consider the following piece of JavaScript code: function sumOfArray(array, l, r) { if (1>r) {...

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(d) Consider the following piece of JavaScript code: function sumOfArray(array, l, r) { if (1>r) { } return 0; } else if (1 ==r) { return array[1]; } var m= Math.floor((1+r)/2): var sumL = sumofArray (l, m); var sumR = sumOfArray (m + 1, r); return sumL + sumR; function sum0fNumbers (n) { if (n == 0) { return 0; } for (var i = 1; i <= n; i++){ array.push(i); } return sum0fArray(array, 8, n-1); } 1. Briefly explain why the function sumOfArray is an example of an implementation of a divide and conquer algorithm. (3 marks) 2. What is the worst-case time complexity of the algorithm being implemented by sum0fArray, written in Big O notation and in terms of n, the length of the input array? Assume that when sumOfArray is called, the input parameters are: the array, 1 is 0 and r is n - 1. Also assume computing the floor takes constant time. (2 marks) 3. Briefly explain your answer to part 2 of question (d). (5 marks) 4. The algorithm implemented by the function sum0fNumbers calculates the sum of all non-negative integers from 0 to n (inclusive), just like RECSUM from question (b). Explain which of the two algorithms you would prefer to use to compute this sum, and why. In particular, compare the worst-case time and space complexities to make your choice. (6 marks) (d) Consider the following piece of JavaScript code: function sumOfArray (array, 1, r) { if (1>r) { return 0; } else if (1 ==r) { return array[1]; } } var m Math.floor((1+r)/2): var sumL = sumofArray (l, m); var sumR = sumOfArray (m + 1, r); return sumL + sumR; function sum0fNumbers (n) { if (n == 0) { return 0; } for (var i = 1; i <= n; i++){ array.push(i); } return sum0fArray(array, 8, n-1); } 1. Briefly explain why the function sumOfArray is an example of an implementation of a divide and conquer algorithm. (3 marks) 2. What is the worst-case time complexity of the algorithm being implemented by sum0fArray, written in Big O notation and in terms of n, the length of the input array? Assume that when sumOfArray is called, the input parameters are: the array, 1 is 0 and r is n - 1. Also assume computing the floor takes constant time. (2 marks) 3. Briefly explain your answer to part 2 of question (d). (5 marks) 4. The algorithm implemented by the function sum0fNumbers calculates the sum of all non-negative integers from 0 to n (inclusive), just like RECSUM from question (b). Explain which of the two algorithms you would prefer to use to compute this sum, and why. In particular, compare the worst-case time and space complexities to make your choice. (6 marks) (d) Consider the following piece of JavaScript code: function sumOfArray(array, l, r) { if (1>r) { } return 0; } else if (1 ==r) { return array[1]; } var m= Math.floor((1+r)/2): var sumL = sumofArray (l, m); var sumR = sumOfArray (m + 1, r); return sumL + sumR; function sum0fNumbers (n) { if (n == 0) { return 0; } for (var i = 1; i <= n; i++){ array.push(i); } return sum0fArray(array, 8, n-1); } 1. Briefly explain why the function sumOfArray is an example of an implementation of a divide and conquer algorithm. (3 marks) 2. What is the worst-case time complexity of the algorithm being implemented by sum0fArray, written in Big O notation and in terms of n, the length of the input array? Assume that when sumOfArray is called, the input parameters are: the array, 1 is 0 and r is n - 1. Also assume computing the floor takes constant time. (2 marks) 3. Briefly explain your answer to part 2 of question (d). (5 marks) 4. The algorithm implemented by the function sum0fNumbers calculates the sum of all non-negative integers from 0 to n (inclusive), just like RECSUM from question (b). Explain which of the two algorithms you would prefer to use to compute this sum, and why. In particular, compare the worst-case time and space complexities to make your choice. (6 marks) (d) Consider the following piece of JavaScript code: function sumOfArray (array, 1, r) { if (1>r) { return 0; } else if (1 ==r) { return array[1]; } } var m Math.floor((1+r)/2): var sumL = sumofArray (l, m); var sumR = sumOfArray (m + 1, r); return sumL + sumR; function sum0fNumbers (n) { if (n == 0) { return 0; } for (var i = 1; i <= n; i++){ array.push(i); } return sum0fArray(array, 8, n-1); } 1. Briefly explain why the function sumOfArray is an example of an implementation of a divide and conquer algorithm. (3 marks) 2. What is the worst-case time complexity of the algorithm being implemented by sum0fArray, written in Big O notation and in terms of n, the length of the input array? Assume that when sumOfArray is called, the input parameters are: the array, 1 is 0 and r is n - 1. Also assume computing the floor takes constant time. (2 marks) 3. Briefly explain your answer to part 2 of question (d). (5 marks) 4. The algorithm implemented by the function sum0fNumbers calculates the sum of all non-negative integers from 0 to n (inclusive), just like RECSUM from question (b). Explain which of the two algorithms you would prefer to use to compute this sum, and why. In particular, compare the worst-case time and space complexities to make your choice. (6 marks)

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1 sumOfArray function uses the divide and conquer algorithm because here we are basically dividing t... View the full answer