The space complexity of DFS using recursion is O(V) due to the call stack that can go as deep as the number of vertices in the worst case.

The space complexity of Dijkstra's algorithm using an adjacency list is O(V + E), where V is the number of vertices and E is the number of edges.

The space complexity of Dijkstra's algorithm when using a priority queue is O(V), as it needs to store the distance for each vertex.

The space complexity of Dijkstra's algorithm using an adjacency list is O(V + E), where V is the number of vertices and E is the number of edges.

The space complexity of Dijkstra's algorithm using an adjacency list is O(V + E), where V is the number of vertices and E is the number of edges.

Heap Sort has a space complexity of O(1) as it sorts the array in place.

The space complexity of Merge Sort is O(n) because it requires additional space for the temporary arrays used during merging.

The average space complexity of Quick Sort is O(log n) due to the recursive stack space used during the sorting process.

The space complexity of Quick Sort in the worst case is O(log n) due to the recursive stack space.

The space complexity of recursive tree traversals is O(h), where h is the height of the tree. In the worst case of a skewed tree, this can be O(n), but for balanced trees, it is O(log n).

A linked list with n nodes requires O(n) space, as each node stores data and a pointer to the next node.

Binary search has a space complexity of O(1) when implemented iteratively, as it uses a constant amount of space.

The space complexity of the dynamic programming solution for the 0/1 Knapsack problem is O(n*w), where n is the number of items and w is the maximum weight capacity.

The space complexity of the dynamic programming solution for the 0/1 Knapsack problem using a 2D array is O(n * w), where n is the number of items and w is the maximum weight.

The space complexity of the dynamic programming solution for the edit distance problem is O(n * m), where n and m are the lengths of the two strings.

The space complexity of the dynamic programming solution for the Fibonacci sequence using memoization is O(n) due to the storage of computed values.

The space complexity of the dynamic programming solution for the Fibonacci sequence can be optimized to O(1) by storing only the last two computed values.

The space complexity of the dynamic programming solution for the Longest Increasing Subsequence problem is O(n) due to the storage of intermediate results.

The space complexity of the dynamic programming solution for the Longest Common Subsequence problem is O(m * n), where m and n are the lengths of the two sequences.

The space complexity of the iterative binary search is O(1) since it uses a constant amount of space.

The iterative implementation of binary search uses a constant amount of space, leading to a space complexity of O(1).

The iterative version of binary search uses a constant amount of space, leading to a space complexity of O(1).

The space complexity of the recursive implementation of inorder traversal is O(n) due to the call stack used for recursion.

A Class C address with 30 usable hosts requires a subnet mask of 255.255.255.252, which allows for 4 IP addresses (2 usable).

To support 500 hosts, a subnet mask of 255.255.254.0 (/23) is required, providing 510 usable addresses.

To accommodate 30 usable addresses, a /27 subnet mask (255.255.255.224) is needed, providing 32 total addresses (30 usable).

To accommodate at least 500 usable addresses, a subnet must have at least 512 total addresses, which corresponds to a /23 subnet mask (255.255.254.0).

A subnet mask of 255.255.255.252 allows for 2 usable IP addresses (4 total - 2 for network and broadcast), which is suitable for point-to-point links.

A /20 subnet mask corresponds to 255.255.240.0, allowing for 4096 addresses in the subnet.

The time complexity for deleting a node in a Red-Black tree is O(log n) due to its balanced structure.