-2

My idea of the program is :

Input = n sets

objective function ObjFn equals to O(n^3)

Output = the order of n sets

Steps:

  1. Applying ObjFn to all n sets
  2. Choose the n of the Minimum ObjFn to be ordered first
  3. Eliminate the chosen n
  4. Repeat steps 1:3 for the rest n-1 sets Note: If there are more than same minimum ObjFn for more than one of the n-1 , clustering all available solutions

I need to check the time complexity of this type of clustering and the overall time complexity of the method.

In addition if there any reference for a similar optimization method to study it.

Thanks in advance.

Example: If n= 4 and the ObjFns are n1= 30 , n2=50, n3=50, n4=60 then the outputs are n1 n2 n3 n4 and n1 n3 n2 n4 .

1234
  • 1
  • 1

1 Answers1

0

Seems $O(n^5)$ since the obj function needs to be evaluated $O(n)$ times for each of the remaining sets (of which there are $n$ at the start). I'm assuming your objective function changes as you add selected sets (i.e. you can't calculate the obj function once per set and store it).

TickaJules
  • 71
  • 1
  • 4