Answer:
Following are the program in the C++ Programming Language.
#include <iostream>
using namespace std;
//define function for swapping
void SwapValues(int* userVal1,int* userVal2){
//set integer variable to store the value
int z = *userVal1;
//interchange their value
*userVal1 = *userVal2;
//interchange their value
*userVal2 = z;
}
//define main method
int main()
{
//declare variables
int x,y;
//get input from the user
cin>>x>>y;
//Call the method to swap the values
SwapValues(&x,&y);
//print their values
cout<<x<<" "<<y;
return 0;
}
<u>Output</u>:
3 8
8 3
Explanation:
<u>Following are the description of the program</u>.
- Firstly, we define required header file and function 'SwapValues()', pass two pointer type integer variables in argument that is 'userVal1' and 'userVal2'.
- Set integer data type variable 'z' and initialize the value of 'userVal1' in it, then initialize the value of 'userVal2' in 'userVal1' and then initialize the value of 'z' in 'userVal2'.
- Finally, define the main method in which we set two integer type variables and get input from the user in it then, call and pass those variables and print it.
Abstraction make programming languages easier to use as It eliminates the need for codes to be automated.
<h3>How does abstraction make programming languages easier to implement?</h3>
Data abstraction is known to be a tool that helps a person to change a complex data structure into simple ones which can be used easily.
Note that Abstraction make programming languages easier to use as It eliminates the need for codes to be automated.
Learn more about abstraction from
brainly.com/question/7994244
#SPJ1
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.
