class Solution func halveArray_ nums Int - Int var pq = PriorityQueueDouble for num in nums pqpushDoublenum var res = 0 var sum = numsreduce0 +

The time complexity of the three solutions is the same - O(nlogn), where n is the length of the input array nums. This is because all three solutions involve iterating through the nums array once to build the priority queue, and each push and pop operation on the priority queue takes O(logn) time.

However, the difference in runtime and memory usage could be due to various factors such as the implementation details of the programming language, the efficiency of the data structures used (e.g. the priority queue), and the specific optimizations made by the compiler.

In general, the Swift solution (code 1) seems to be the most memory-efficient, as it uses a custom implementation of a priority queue that only stores the necessary data (i.e. the heap array). The Python solution (code 2) uses the heapq module, which may have some additional overhead for maintaining the heap in memory. The C++ solution (code 3) uses the standard library priority_queue, which may have its own memory management strategies.

As for the runtime, it's difficult to determine the exact reasons without further analysis. It could be due to differences in how the priority queue is implemented and how the push, pop, and top operations are performed. It could also be influenced by other factors such as the efficiency of the language runtime or the underlying hardware.

In general, it's important to consider both runtime and memory usage when comparing different solutions. Depending on the specific requirements and constraints of the problem, one may be more important than the other