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Sonja [21]
2 years ago
7

Which of the following shows a list of Big-Oh running times in order from slowest to fastest?

Computers and Technology
2 answers:
Irina18 [472]2 years ago
8 0

Answer: I pretty much agree with him.

Explanation:

Rufina [12.5K]2 years ago
5 0

Answer:

O(N!), O(2N), O(N2), O(N), O(logN)

Explanation:

N! grows faster than any exponential functions, leave alone polynomials and logarithm. so O( N! ) would be slowest.

2^N would be bigger than N². Any exponential functions are slower than polynomial. So O( 2^N ) is next slowest.

Rest of them should be easier.

N² is slower than N and N is slower than logN as you can check in a graphing calculator.

NOTE: It is just nitpick but big-Oh is not necessary about speed / running time ( many programmers treat it like that anyway ) but rather how the time taken for an algorithm increase as the size of the input increases. Subtle difference.

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That would be a virus.
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Zanzabum

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5 0
3 years ago
4.3 mini programs AP computer science
allochka39001 [22]

1.

name = input("Enter your name: ")

num1 = int(input("Hello "+name+ ", enter an integer: "))

num2 = int(input(name+", enter another integer: "))

try:

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   else:

       print("The first number is not divisible by the second number")

except ZeroDivisionError:

   print("The first number is not divisible by the second number")

try:

   if num2 % num1 == 0:

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   else:

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except ZeroDivisionError:

   print("The second number is not divisible by the first number")

2.

import random, math

num1 = float(input("Enter a small decimal number: "))

num2 = float(input("Enter a large decimal number: "))

r = round(random.uniform(num1, num2), 2)

print("The volume of a sphere with radius " + str(r) + " is " + str(round(((4 / 3) * math.pi * (r ** 3)), 2)))

I hope this helps!

8 0
3 years ago
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Answer:

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