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Some of the ones that didn't make it:

Smushed list. Size O(1). The smushed list is a list of variables (of the same type), stored in a single variable of that type. To produce the smushed list, simply XOR all the elements of the list together, then store. To get a value back, simply XOR the smushed list by all the elements other than the one you want. Smushing is also embarrassingly parallel (you can smush two halves separately and then smush the results) so producing smushed lists is blazingly fast.

Unlinked list. O(n). This is slightly faster than a linked list, and acts as a "black box". Simply allocate nodes that are not linked to each other in any way. The data normally stays out of the way of your program, but in case of a core dump you can find it again. NOTE: If your language does reference-counting this will not work. Get a real language that does what you say.

Search-bush. Search trees are good at bisecting data, but they are not really conducive to a random walk for inspiration. Begin by constructing a binary search tree, keeping track of all the nodes you've added, and simply add a third, random, pointer to each node - have it point at a random node somewhere in the tree. In the search algorithm, either follow the left, right, or random node, depending on how much meandering you are interested in doing. The journey is the destination.




I've curious, under what conditions is a smushed list useful?


Actually, this one is real. AKA "parity". Another example (with n=2) is to create a doubly-linked list while only storing one pointer (ptr = prev_node xor next_node).


Oh, I hadn't made the connection between those and the general statement. Thanks for the clarification.


Funny, but why did you post this twice from two accounts?


Search-bush sounds pretty neat, thanks!




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