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.
Answer:
Answered below
Explanation:
This solution is written in Kotlin programming language.
fun average (a: Int, b: Int, c: Int, d: Int, e: Int) : Double {
#variable to hold the addition of all parameters
var sum = a + b + c + d + e
#variable to hold the average of sum
var avg = sum / 5
return avg
}
#call the function to see how it works.
# this operation is done in the fun main()
var test: Double = average ( 5, 4, 7 , 3, 9)
print (test)
Is there answer choices because I’m not understanding what you want me to answer
Answer:
social media like snap chat twitter and face book .it can even happen over email.or in school chats.
Explanation:
