multiset

How can I get the first N elements from a multiset structure, without constantly getting the first (.begin()) element and then erasing it? I just want to sum the first N elements without affecting the multiset. I just want to sum the first N elements without affecting the multiset. #include <numeric> #include <iterator> // ... int sum = std::accumulate(my_set.begin(), std::next(my_set.begin(), N)); std::next is a C++11 library addition. Here is a solution for older compilers: std::multiset<int>::iterator it = my_set.begin(); std::advance(it, N); int sum = std::accumulate(my_set.begin(), it);

Say you have a List of 32-bit Integers and the same collection of 32-bit Integers in a Multiset (a set that allows duplicate members) Since Sets don't preserve order but List do, does this mean we can encode a Multiset in less bits than the List? If so how would you encode the Multiset? If this is true what other examples are there where not needing to preserve order saves bits? Note, I just used 32-bit Integers as an example. Does the data type matter in the encoding? Does the data type need to be fixed length and comparable for you to get the savings? EDIT Any solution should work well for

Assuming we got 2 lists, always with the same length and always containing strings. list1 = ['sot', 'sot', 'ts', 'gg', 'gg', 'gg'] list2 = ['gg', 'gg', 'gg', 'gg', 'gg', 'sot'] we need to find: How many items of the list2 should change, in order for it to be equals with list1 . So on the previous example it should return 2 For this example: list1 = ['sot', 'sot', 'ts', 'gg', 'gg', 'gg'] list2 = ['gg', 'gg', 'gg', 'gg', 'sot', 'sot'] it should return 1 and finally for this example: list1 = ['sot', 'sot', 'ts', 'gg', 'gg', 'gg'] list2 = ['ts', 'ts', 'ts', 'ts', 'ts', 'ts'] it should return 5 .

What is the time complexity of applying the next() and prev() function on an multiset<int>::iterator type object where the corresponding multiset contains the N elements? I understand that in STL, a multiset is implemented as a balanced binary search tree and hence I expect the time complexity to be O(log N) per operation (in the worst case) in case we just traverse the tree until we find the appropriate value, but I have a hunch that this should be O(1) on average. But what if the tree is implemented as follows - when inserting element x in the balanced binary search tree, we can also

Say I have a set D of multisets: D = { {d, g, o}, {a, e, t, t}, {a, m, t}, } Given a multiset M , like M = {a, a, m, t} I would like an algorithm f to give me all elements of D that are subsets (or more precisely, "submultisets") of M : f = {{a, m, t}} If we do only one such query, scanning over all elements of D (in O(#D) time) is clearly optimal. But if we want to answer many such queries for the same D and different M , we might be able to make it faster by preprocessing D into some smarter data structure. We could toss all of D into a hashtable and iterate over all possible subsets of M ,

Please help me to confirm is that behavior explained below is a bug, or clearly explain why it's right. There are a high probability that I misunderstood some concept, but now for me it looks like a bug. All examples below simplified as much as possible to demonstrate core of the issue. Real situation is very complex, so only general answers and workarounds related to principle of query construction is acceptable. You are welcome to ask clarifying questions in comments and i'll try to do my best to answer them. Thank you for attention. :) Question Why in last Example (Example 5) collection