Answer:
The space complexity of different data structures can vary, and it's not accurate to say that all data structures have the same space complexity or that one is universally worse than the others. Space complexity depends on several factors, including the implementation details and the specific use case.
Here's a brief overview of the space complexities of the mentioned data structures:
a. Set: The space complexity of a set typically depends on its implementation. In a hash set, the space complexity is O(n), where n is the number of elements in the set. In a balanced binary search tree (e.g., a red-black tree), the space complexity is also O(n).
b. Array: The space complexity of an array is O(n), where n is the number of elements in the array. Arrays have a fixed size, and the space required is proportional to the number of elements stored.
c. All have the same space complexity: This statement is not accurate. Different data structures have different space complexities, as mentioned above.
d. Object: The space complexity of an object in a programming language depends on its attributes and their data types. It can vary widely based on how the object is constructed and used. There is no fixed space complexity for objects.
e. Linked List: The space complexity of a linked list is O(n), where n is the number of elements in the list. Each element in a singly linked list (for example) requires space for the data and a reference to the next element.
So, the correct answer is that not all data structures have the same space complexity, and it depends on the specific data structure and its implementation. If you were looking for the data structure with the worst space complexity among those listed, it would likely be the set or array, both with O(n) space complexity when considering the worst case. However, this can vary depending on the specific details of their implementation and use.
Step-by-step explanation: