Priority queue with ubounded number of elements (i.e., with dynamic storage) I'm looking for a data structure that can work as a priority queue with reasonable maintenance complexities (like $O(\log n)$ for insertion and deletion) and that has a theoretical unbounded limit for its number of elements (like a tree structure, that is bounded only by the computer's available memory, and unlike a traditional heap, that uses an static array, which is too costly to augment).

The reason is that I'm implementing a program that makes use of a priority queue and I don't know a priori how many elements I'm going to insert in this queue at once, so sometimes I'm out of space to add another element.

There is no way to estimate this number, and to create a huge array to support a static type of queue is a terrible option, as maybe not even a half of it will be used and I'll be short on memory to allocate other objects.

I've heard of something like a Dynamic Heap (or something in the like) that is some sort of linked list of arrays, whose elements are dynamically allocated when needed, but I'm not sure this is the best strategy to follow, moreover, I would like to know if there were other options.



Just for the record, I'm implementing a Branch-and-Bound algorithm for solving a linear integer optimization problem, with each node stored on the queue being the abstraction of an active node on the algorithm. The number of active nodes cannot be estimated at any time, so a theoretically unbounded queue would help a lot.
I don't understand your claim that a traditional heap is too costly to augment. If the heap is currently of size $N$ and the $N+1$st element is inserted, allocate a new array of size $2N$ and copy the old heap into its prefix. This takes amortized constant time. You can also shrink the heap in this way, for example if it is only half full you can shrink it to two-thirds of its size.