BI S AL Y - -100%% .0 .00 123-Default (Ari. - 10A1- fx |ABDFGH1O(1)O(log n)O(n)O(n)O(n^2)Instructions:nT(n) = 100T(n) = log2 n + 100T(n) = 3n + 400T(n) = 10nT(n) = n^2 - 100n + 22* You are not to modify the given values in the table!* log2 n ==> is to be read as log of n, base 231004200* n^2 ==> is to be read as n raised to the power of 2300Tasks to be done:64001Determine the values for the given time complexity functions with the given500n values (100 to 1000).78.600Note: You may issue formulas to easily fill out the needed values in the table.7002 What generalizations can you derive from the values that you were able to10800generate?Place your answer below:119001210001314151617181920212223242526272829

The question is on: determining the values of given time complexity functions given in the excel…

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Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Generate a graph of performance improvement for code below. Tip: Figure 5.17 in the textbook provides an example of a graph depicting performance improvements associated with loop unrolling. code: //normal code int main() { int n, sum=0; n=50; int a[50]; { for(int i=0; i<n; i++) { sum= sum +a[i]; } printf("sum is %d", sum); return 0; } //with unrolling int main() { int n, sum=0; n=50; int a[50] for(int i=0; i<n; i+=10) { scanf("%d", &a[i]); } sum= sum + a[0]; sum= sum + a[1]; sum= sum + a[2]; sum= sum + a[3]; sum= sum + a[4]; sum= sum + a[5]; sum= sum + a[6]; sum= sum + a[7]; sum= sum + a[8]; sum= sum + a[9]; printf("sum is %d", sum); return 0; }
Assume that each of the expressions in the following table has processing time T(n) to solve a problem of size n. Identify the dominant term(s) having the growing increase in n and specify the Big-Oh complexity. Expression Dominant term(s) Big-Oh A.1 8 + 0.081n2 + 0.040n A.2 100 + n log2 n A.3 100n + 0.081 log3 n + n3 A.4 0.081 log4 n + 81n
2.Length of your answer: 1-2 pages.)Let fn = xn y0 + xn-1 y1 + xn-2 y2 + . . . + x0ynPresent an O(log n) time (n / log n)-processor CREW PRAM algorithm to evaluate the polynomial f.
Assume that each of the expressions in the following table has processing time T(n) to solve a problem of size n. Identify the dominant term(s) having the growing increase in n and specify the Big-Oh complexity. Expression Dominant term(s) Big-Oh A.1 8 + 0.081n2 + 0.040n A.2 100 + n log2 n A.3 100n + 0.081 log3 n + n3 A.4 0.081 log4 n + 81n Find the Big-Oh for each of the following programs. B.1 sum = 0; for(i = 0; i < n; i++) sum++; B.2 sum = 0 for(i = 0; i < n; i++) for( j = 0; j < n * n; j++) sum++;
The n-th Fibonacci number Fnis defined recursively as Fn=Fn−1+Fn−2 whereF0=0 and F1=1 . For instance, F2=F1+F0=1+0=1 , F3=F2+F1=1+1=2 andF4=F3+F2=2+1=3 . Write an ARM Assembly Language program to compute then-th Fibonacci number Fn. Test your program by computing F15 and F30 .Your progam must write the outputs for the two computations on the screen.
Question 6 sm.Is 2^(n+1) = O(2^n )? Is 2^(2n) = O(2^n ) ? Suppose that on a particular computer, an Algorithm A, with inputs of size n, runs in 10n^2 steps, and another Algorithm B runs in 60n lg n steps [Note: lg x = log2(x)]. For what values of positive integers n is Algorithm A better than Algorithm B? Full explain this question and text typing work only We should answer our question within 2 hours takes more time then we will reduce Rating Dont ignore this line
What is the largest n for which one can solve withina minute using an algorithm that requires f(n) bit operations, where each bit operation is carried out in10^(−12) seconds, with these functions f(n)? a) log n b) n^2 c ) 2^n
Assume that each of the expressions below gives the processing time T(n) spent by an algorithm for solving a problem of size n. Select the dominant term(s) having the steepest increase in n and specify the lowest Big-Oh complexity of each algorithm. For example, the dominant term in 0.1n + 10n4 is 10n4 and it is O(n4). Expression Dominant term(s) O(. . .) 5 + 0.001n3 + 0.025n 500n + 100n1.5 + 50n log10 n 0.3n + 5n1.5 + 2.5 · n1.75 n2 log2 n + n(log2 n)2 n log3 n + n log2 n 100n + 0.01n2 0.01n + 100n2 2n + n0.5 + 0.5n1.25 0.01n log2 n + n(log2 n)2 100n log3 n + n3 + 100n
please show the steps Q5- find the complexities based on theta notation ? a-f(X)=3n^2 +10n log n +1000n+4 log n +999 b- f(X)= (log n)^2 +2n+4n+log n +50 c-f(X)= n+ n^2 + 2^n + n^4
The given time complexity is: m T(m-1) + ca > 1 cb = 0 expanding using substitution: m T(m-1) + ca m[(m-1) T(m-2) + ca]+ ca m(m-1) T(m-2) + mca + ca m(m-1) [(m-2) T(m-3) + ca] + mca + ca m(m-1)(m-2) T(m-3) + m(m-1)ca + mca + ca what is the time complexity? form an expression for adding all the ca
Given two sorted arrays nums1 and nums2 of size m and n respectively, return the median of the two sorted arrays. The overall run time complexity should be O(log (m+n)). Example 1: Input: nums1 = [1,3], nums2 = [2] Output: 2.00000 Explanation: merged array = [1,2,3] and median is 2. Example 2: Input: nums1 = [1,2], nums2 = [3,4] Output: 2.50000 Explanation: merged array = [1,2,3,4] and median is (2 + 3) / 2 = 2.5. Constraints: nums1.length == m nums2.length == n 0 <= m <= 1000 0 <= n <= 1000 1 <= m + n <= 2000 -106 <= nums1[i], nums2[i] <= 106 Write the whole code in python language Attach the code outputs also and explain the implementation.
Given two sorted arrays nums1 and nums2 of size m and n respectively, return the median of the two sorted arrays. The overall run time complexity should be O(log (m+n)). Example 1: Input: nums1 = [1,3], nums2 = [2] Output: 2.00000 Explanation: merged array = [1,2,3] and median is 2. Example 2: Input: nums1 = [1,2], nums2 = [3,4] Output: 2.50000 Explanation: merged array = [1,2,3,4] and median is (2 + 3) / 2 = 2.5. Constraints: nums1.length == m nums2.length == n 0 <= m <= 1000 0 <= n <= 1000 1 <= m + n <= 2000 -106 <= nums1[i], nums2[i] <= 106

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