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Home/ Questions/Q 7054751
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Editorial Team
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Editorial Team
Asked: May 28, 20262026-05-28T03:39:54+00:00 2026-05-28T03:39:54+00:00

I am trying to implement my own heap with the method removing any number

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I am trying to implement my own heap with the method removing any number (not only the min or max) but I can’t solve one problem. To write that removing function I need the pointers to the elements in my heap (to have O(logn) time of removing given element). But when I have tried do it this way:

vector<int*> ptr(n);

it of course did not work.

Maybe I should insert into my heap another class or structure containing int, but for now I would like to find any solution with int (because I have already implemented it using int)?

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  1. Editorial Team
    Editorial Team
    2026-05-28T03:39:55+00:00Added an answer on May 28, 2026 at 3:39 am

    When you need to remove (or change the priority of) other objects than the root, a d-heap isn’t necessarily the ideal data structure: the nodes keep changing their position and you need to keep track of various moves. It is doable, however. To use a heap like this you would return a handle to the newly inserted object which identifies some sort of node which stays put. Since the d-heap algorithm relies on the tree being a perfectly balanced tree, you effectively need to implement it using an array. Since these two requirements (using an array and having nodes stay put) are mutually exclusive you need to do both and have an index from the nodes into the array (so you can find the position of the object in the array) and a pointer from the array to the node (so you can update the node when the position changes). Almost certainly you don’t want to move your nodes a lot, i.e. you rather accept finding the proper direction to move a nodes by searching multiple nodes, i.e. you want to use a d > 2.

    There are alternative approach to implement a heap which are inherently nodes based. In particular Fibonacci heaps which yield for certain usage patterns a better amortized complexity than the usual O(ln(n)) complexity. However, they are somewhat harder to implement and the actual efficiency only pays off if you either need to change the priority of a node frequently or you have fairly large data sets.

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