Data structure for choosing random elements?

Does anyone know of a data structure that supports the two operations efficiently?

1. Insert a value into the data structure.
2. Dequeue and return an entry from the data structure with uniformly random probability.

This is sort of like the canonical "bag of marbles" that always comes up in introductory probability classes. You can put arbitrary marbles into the bag, and can then efficiently remove the marbles at 开发者_运维百科random.

The best solution I have is as follows - use a self-balancing binary search tree (AVL, AA, red/black, etc.) to store the elements. This gives O(lg n) insertion time. To remove a random element, pick a random index i, then locate and remove the ith element from the tree. If you've augmented the structure appropriately, this can be made to run in O(lg n) time as well.

This runtime certainly isn't bad, but I'm curious if it's possible to do better - perhaps O(1) for insertion and O(lg n) for dequeues? Or perhaps something that runs in expected O(1) insert and delete using hash functions? Or perhaps a stronger amortized bound?

Does anyone have any ideas on how to make this asymptotically faster?

Yes. Use a vector.

To insert, simply place at the end, and increment the size. To remove, pick an element at random, swap its contents with the end value, then pop off the end value (i.e., return the end value and decrement the vector's size).

Both operations are amortised O(1).