An LRU (Least Recently Used) cache is most efficiently implemented using a combination of a HashMap and a Doubly Linked List. The HashMap provides O(1) access to cache entries, while the Doubly Linked List maintains the order of usage, allowing for quick updates to the most recently and least recently used elements.

The space complexity of a recursive algorithm is often higher than its iterative counterpart because recursion requires additional stack space to store the state of each function call. Each recursive call adds a new frame to the call stack, which increases the overall space complexity.

A heap data structure is typically used to implement a priority queue. Heaps allow for efficient retrieval of the highest (or lowest) priority element and support operations like insertion and deletion in logarithmic time, making them ideal for priority queues.

Dijkstra's algorithm fails to find the shortest path if the graph contains negative weight edges. The algorithm assumes that all edge weights are non-negative, and the presence of negative weights can lead to incorrect results and infinite loops.

In the worst case, a Binary Search Tree (BST) can become a skewed tree (like a linked list), where each node has only one child. In such a scenario, the time complexity of inserting an element becomes O(n), where n is the number of nodes in the tree.