Question

Give the big-Oh notation of the following expression: 1000 + 1000000*n + 5*n^2

Give the big-Oh notation of the following expression:

1000 + 1000000*n + 5*n^2

Engineering   Computer-Science   
0 0
Add a commentTranscribed image text
Next >

Homework Answers

Answer #1

The big -Oh notation of the given expression is: O(n2)

Explanation:

Big o notation provides a tight upper bound and it is a mathematical notation to represent the behavior of a given function.

This notation tells about how long the given source code or algorithm takes to run and used for algorithm analysis.

The given expression is:

= 1000 + 1000000*n + 5*n^2

The given function is a sum of three terms.

The third term has the highest growth and this term is a product of 5 and n2. The first factor doesn't depend upon the n.

So, the big -Oh notation of the given expression is: O(n2)

0 0
Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Log In

Sign Up

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
give T(n)=T(n-1)+2^n use substituiton method prove that it’s big oh O(n^2)
give T(n)=T(n-1)+2^n use substituiton method prove that it’s big oh O(n^2)
1.) the following code fragments give running time analysis (Big Oh). Explain your answer: sum2 =...
1.) the following code fragments give running time analysis (Big Oh). Explain your answer: sum2 = 0; sum5 = 0; for(i=1; i<=n/2; i++) { sum2 = sum + 2; } for(j=1; j<=n*n; j++) { sum5 = sum + 5; } i think it is O(n^2) since big oh finds the order of worst case which is the the second loop being n^2 complexity. but im not sure. 2.) Give an efficient algorithm along with running time analysis to find the...
Analyze the worst case running time of the following code in “big-Oh” notation in terms of...
Analyze the worst case running time of the following code in “big-Oh” notation in terms of the variable n. a. void fdisp1(int n) { for(int i=0; i < n; i++) { for(int j=0; j < n; j++) { for(int k=0; k < n; k++) { for(int m=0; m < n; m++) { System.out.println("Hi I am Here"); } } } } } b. voidprintAllPossibleOrderedPairs(intarr[], int size) { for (int i = 0; i < size; i++) { for (int j =...
Fill in the following table, using Big-O notation to give the worst and average-case times for...
Fill in the following table, using Big-O notation to give the worst and average-case times for each of the stack methods for a stack of size N. OPERATION WORST-CASE TIME AVERAGE-CASE TIME constructor empty size push pop peek
#data structures Give the appropriate execution time efficiency of the following array algorithms using Big-O notation....
#data structures Give the appropriate execution time efficiency of the following array algorithms using Big-O notation. Assume the size of the array is n. a)   Linear Search (average case)   ______________________    b)   Binary Search (worst case)       ______________________    c)   Insertion Sort (best case)       ______________________    d)   Insertion Sort (average case)           ______________________    e)   Quick Sort (average case)           ______________________
The mass action expression (MAE) for the following reaction is equal to what? Ca(OH)2 (s) +...
The mass action expression (MAE) for the following reaction is equal to what? Ca(OH)2 (s) + CO32-(aq) <---> CaCO3(s) + OH- (aq) [CaCO3][OH-]/[Ca(OH)2][CO32-] [OH-]/[CO32-] [Ca(OH)2][CO32-]/[CaCO3][OH-]/ [CO32-]/[OH-]
give the notation(using letter designation for 1) for the subshells denoted by the following numbers? A)...
give the notation(using letter designation for 1) for the subshells denoted by the following numbers? A) n=6, L=2 B)n=5, l=0 C)n=4, l=3 D) n=6,l=1
2. Write the big-O expression to describe the number of operations required for the following two...
2. Write the big-O expression to describe the number of operations required for the following two pieces of code. You need to explain your solution and show your work. (3 points) code 1: counter = 0       for (i = 1; i < n; ++i) {           for (j = 1; j < i; j++) { counter++; }    } code 2: counter = 0; for (i = 1; i < n; ++i) { for (j = 1; j <...
Use Big-O Notation to characterize the computational cost of algorithm A1 and A2. The complexity of...
Use Big-O Notation to characterize the computational cost of algorithm A1 and A2. The complexity of A1 is 10000 * n, with n being the number elements processed in A1. The complexity of A2 is 100 * n, with n elements in A2. State in Big-O notation the cost of f1, with f1(x) = log2(2x) – 14x + 3 sqrt(x) + 12x^2 - 150 this is also a theoretical question just answer no code.
Given the following list of functions, determine the order of growth of each using big-Theta notation...
Given the following list of functions, determine the order of growth of each using big-Theta notation and put all the functions in order from slowest-growing to fastest-growing. Be sure to put functions of equal growth rate on the same level. Unless otherwise noted, you can assume all logarithms are base-2. 6nlog(2n)+8n 4n2log(log(8n))+8n2+n 500 n3+7nlog(n2) + 4n 2n+2n+1 log(4n2)+3n+1 12 8log(24n)+10 8n2log(5n2)+7n+200 4log(n3)+1000 100log(16n)log(n6)+23 8nlog(log(n4))+6n+32 9log(log(8n))
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT