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.
By default, the windows desktop display the following icons/programs
1. Your Recycle Bin
2. My Computer
3. The Internet Explorer
4. The default Windows Background
5. Your windows menu
6. My Documents
7. Your task bar
8. Time (located at bottom right)
<span>Black sockets should be used, but the color is not the reason why. Chrome sockets will cause splits to form in the socket walls pretty quickly, after only a few uses. But the black sockets are that color because they have gone through a process called Parkerizing that coats the surface of the socket in order to provide more resistance when being used and protect the socket against corrosion.</span>
