Which of the following time complexities indicates the slowest growing function?O(n!)O(2^n)O(n^3)O(n log n)

The slowest growing function among the given options is O(n log n).

Here's a brief explanation:

1. O(n log n): This is a linearithmic time complexity. It grows slower than polynomial time complexities like O(n^2), O(n^3), etc. It's commonly seen in efficient sorting algorithms like merge sort and heap sort.

2. O(n^3): This is a cubic time complexity. It grows faster than linear and linearithmic time complexities, but slower than exponential and factorial time complexities. It's often seen in algorithms with three nested loops, like certain brute force algorithms.

3. O(2^n): This is an exponential time complexity. It grows very fast and is often seen in recursive algorithms that solve a problem of size n by recursively solving two smaller problems of size n-1.

4. O(n!): This is a factorial time complexity. It grows the fastest among common time complexities. It's seen in algorithms that generate all permutations of a set, for example.

So, the slowest growing function in this list is O(n log n).