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:
<u> A. It uses binary numbers in its algorithm</u>
Explanation:
A Binary search is a type of algorithm designed to look through <em>only </em>a sorted array of data for a particular item.
It is<em> more efficient (faster) </em>than sequential search since the algorithm doesn't have to look up the entire array of data, but simply repeatedly divide in half the section of the array that could contain the searched item.
Answer:
player vs anonymous players
Answer:
The answer is that it is a speaker note.
Explanation:
It leaves a note for people that use presentation files. I use it all the time on my google slides.
Answer:
It is an object. And this is because an object has the data and procedures that defines how it is going to react when it is going to be activated. The data is the details about the object, and it explains what the object actually is. And the procedures are the details of the functions that the particular objects can perform. Like for a hospital, data can be mentioning list of medication services they provide, and procedure can be like registering for any medication service, the complete process.
Explanation:
The answer is self explanatory.
