En computación, un montículo (heap en inglés) es una estructura de datos del tipo árbol con información perteneciente a un conjunto ordenado. Los montículos máximos tienen la característica de que cada nodo padre tiene un valor mayor que el de cualquiera de sus nodos hijos, mientras que en los montículos mínimos, el valor del nodo padre es siempre menor al de sus nodos hijos. Un árbol cumple la condición de montículo si satisface dicha condición y además es un árbol binario casi completo. Un árbol binario es completo cuando todos los niveles están llenos, con la excepción del último, que se llena desde la izquierda hacia la derecha. En un montículo de prioridad, el mayor elemento (o el menor, dependiendo de la relación de orden escogida) está siempre en el nodo raíz. Por esta razón, los montículos son útiles para implementar colas de prioridad. Una ventaja que poseen los montículos es que, por ser árboles completos, se pueden implementar usando arreglos (arrays), lo cual simplifica su codificación y libera al programador del uso de punteros. La eficiencia de las operaciones en los montículos es crucial en diversos algoritmos de recorrido

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In computer science, a heap is a specialized tree-based data structure that satisfies the heap property: If A is a parent node of B then the key (the value) of node A is ordered with respect to the key of node B with the same ordering applying across the heap. A heap can be classified further as either a "max heap" or a "min heap". In a max heap, the keys of parent nodes are always greater than or equal to those of the children and the highest key is in the root node. In a min heap, the keys of parent nodes are less than or equal to those of the children and the lowest key is in the root node. Heaps are crucial in several efficient graph algorithms such as Dijkstra's algorithm, and in the sorting algorithm heapsort. A common implementation of a heap is the binary heap, in which the tree is a complete binary tree (see figure). In a heap, the highest (or lowest) priority element is always stored at the root. A heap is not a sorted structure and can be regarded as partially ordered. As visible from the heap-diagram, there is no particular relationship among nodes on any given level, even among the siblings. When a heap is a complete binary tree, it has a smallest possible height—a heap with N nodes always has log N height. A heap is a useful data structure when you need to remove the object with the highest (or lowest) priority. Note that, as shown in the graphic, there is no implied ordering between siblings or cousins and no implied sequence for an in-order traversal (as there would be in, e.g., a binary search tree). The heap relation mentioned above applies only between nodes and their parents, grandparents, etc. The maximum number of children each node can have depends on the type of heap, but in many types it is at most two, which is known as a binary heap. The heap is one maximally efficient implementation of an abstract data type called a priority queue, and in fact priority queues are often referred to as "heaps", regardless of how they may be implemented. A heap data structure should not be confused with the heap which is a common name for the pool of memory from which dynamically allocated memory is allocated. The term was originally used only for the data structure.

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