In big O notation of time complexity in algorithmic analysis, is O(n + k log n) the same as O(n log n) if k is larger than n? I am not entirely sure about this.
I am not 100% sure what you mean by N+KlogN. I'm used to seeing K used as a subset of N, for example "the top Kth set of items in N" which for large N it is common to simply return the top K items in N because then the Big-O time is NlogK which is much faster than NlogN (because K is a smaller number).
If you literally mean N+KlogN, then that would be more complex than simply NlogN as K adds to the number. For example, as K goes to zero you simply end up with NlogN, otherwise you get a greater than NlogN, which I hope would be obvious is more complex.
I hope that does something to answer the question. I confess I feel like I might be missing the point here and if so I apologize.