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.
A ( micro ) is usually a smaller version of a data warehouse
Answer
Timekeeping has been a part of society since Ancient Egypt. The use of spring-powered mechanisms allowed clocks to be made smaller ... Essentially, the church bells and the mechanical clock now became the monitor of the working day.
Explanation:
Answer:
I think the answer is Writing but am not sure
