The "Time Limit Exceeded" error typically occurs when your Python code takes longer to execute than the allowed time limit for a particular task or problem. This is a common issue, especially in competitive programming, where strict time constraints are imposed.

To solve the "Time Limit Exceeded" error in Python, you can try the following strategies:

Algorithm Optimization:

Review your code and algorithm to see if there are any inefficiencies. Look for nested loops or recursive functions that might be causing unnecessary computational overhead.

Consider optimizing your algorithm to reduce its time complexity. Look for ways to eliminate redundant computations and unnecessary iterations.

Use Efficient Data Structures:

Use appropriate data structures for your problem. In some cases, using a different data structure can significantly improve the runtime of your code.

For example, if you're frequently searching for elements, consider using a set or dictionary for O(1) lookups instead of a list with O(n) lookups.

Avoid Unnecessary I/O Operations:

Minimize input/output (I/O) operations as they can be time-consuming. Read input data in a more efficient manner, and avoid unnecessary print statements.

Caching and Memoization:

Implement caching or memoization if your code involves repeated calculations. Storing and reusing the results of expensive computations can save time.

Use Built-in Functions and Libraries:

Utilize built-in functions and libraries in Python, which are often optimized for performance. For example, use list comprehensions instead of traditional for loops where applicable.

Parallelism and Concurrency:

If your problem allows it, consider using parallelism or concurrency to perform tasks concurrently, taking advantage of multi-core processors.

Profile Your Code:

Use profiling tools like cProfile to identify bottlenecks in your code. Profiling can help you pinpoint which parts of your code consume the most time.

Limit Input Size:

If possible, limit the input size for testing and debugging purposes. Smaller inputs will execute faster and allow you to identify issues more quickly.

Check for Infinite Loops:

Ensure that your code does not contain infinite loops or unintended recursion.

Check for Recursion Depth:

If you're using recursion, be cautious of exceeding the maximum recursion depth in Python. You can increase the recursion limit using sys.setrecursionlimit(), but it's better to convert the recursion into an iterative solution when possible.

Consider Language Alternatives:

Depending on the problem and platform, you might consider using a lower-level language like C++ or Java for better performance, as they have lower overhead compared to Python.

Check for External Dependencies:

If your code relies on external resources or APIs, slow responses from these sources can contribute to the "Time Limit Exceeded" error. Ensure that external calls are efficient or implement timeouts.

Remember that solving the "Time Limit Exceeded" error often requires a deep understanding of your algorithm and problem domain. Experiment with various optimization techniques, measure performance, and iterate until your code meets the time constraints imposed by your environment or problem statement.

Solution

First-off: Reimplementing list.sort will always be slower than just using it directly. If nothing else, getting rid of the arrange function and replacing the call to it with ref.sort() would improve performance (especially because Python's sorting algorithm is roughly O(n) when the input is largely sorted already, so you'll be reducing the work from the O(n**2) of your bubble-sorting arrange to roughly O(n), not just the O(n log n) of an optimized general purpose sort).

If that's not enough, note that list.sort is still theoretically O(n log n); if the list is getting large enough, that may cost more than it should. If so, take a look at the bisect module, to let you do the insertions with O(log n) lookup time (plus O(n) insertion time, but with very low constant factors) which might improve performance further.

Alternatively, if Ask operations are going to be infrequent, you might not sort at all when Adding, and only sort on demand when Ask occurs (possibly using a flag to indicate if it's already sorted so you don't call sort unnecessarily). That could make a meaningfully difference, especially if the inputs typically don't interleave Adds and Asks.

Lastly, in the realm of microoptimizations, you're needlessly wasting time on list copying and indexing you don't need to do, so stop doing it:

tasks=[]
for i in range(n):
    tasks.append(input().split())  # Removed list() call; str.split already returns a list

ref = []
for action, value in tasks:  # Don't iterate by index, iterate the raw list and unpack to useful
                             # names; it's meaningfully faster
    if action == 'Add':
        ref.append(int(value))
        ref.sort()
    elif action == 'Ask':
        print(ref[int(value) - 1])

Answer by: >ShadowRanger

Credit to >StackOverflow

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