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.
That’s not a question but good for her
Application of a new technology and is much superior to rival products
Answer:
An employee is having trouble opening a file on a computer.
- → ✔ <u>information services and support</u>
The president of a company wants to give the company website a fresh new look.
- → ✔ <u>interactive media</u>
An employee wants to work from home but can’t connect to the network from there.
- → ✔ <u>network systems administration</u>
The vice president of sales would like help designing a new software program to keep track of sales.
- → ✔<u> programming and software development</u>
<u>OAmalOHopeO</u>
Answer:
1. Classes and objects
2. Inheritance
3. Polymorphism
4. Data hiding/ encapsulation
5. Interfaces.
Explanation:
Classes and objects depict the major component of the OOP (object oriented programming). It explains the object like a ball in a soccer game development.
The inheritance is like the subclass of the object. Data hiding is a stage in oop where the codes or data are hidden from another users.
In the polymorphism stage, the object is given the ability to change to a sub-object, while in the interface stage a function or method signature is defined without implementing it.
