Sliding Window Maximum Using Deque in DSA Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of using a deque in the Sliding Window Maximum problem?

A deque helps keep track of indices of elements in the current window in a way that the front always holds the index of the maximum element. It allows efficient insertion and removal from both ends to maintain the window's maximum.

intermediate

In the Sliding Window Maximum problem, why do we remove elements from the back of the deque when they are smaller than the current element?

Because smaller elements cannot be the maximum if a bigger element comes after them in the window. Removing them keeps the deque elements in decreasing order, ensuring the front is always the maximum.

beginner

What does the deque store in the Sliding Window Maximum algorithm?

The deque stores indices of elements from the array, not the elements themselves. This helps to check if an element is out of the current window and to access the element values easily.

intermediate

How do you know when to remove the front element of the deque in the Sliding Window Maximum problem?

Remove the front element if its index is outside the current window (i.e., if it is less than the current index minus window size plus one). This keeps the deque relevant to the current window.

intermediate

What is the time complexity of the Sliding Window Maximum algorithm using a deque?

The time complexity is O(n) because each element is added and removed from the deque at most once, making the operations linear in total.

What does the front of the deque represent in the Sliding Window Maximum algorithm?

A The size of the current window

B Index of the minimum element in the current window

C The last element added to the window

D Index of the maximum element in the current window

The front of the deque always holds the index of the maximum element in the current sliding window.

Why do we remove elements from the back of the deque when processing a new element?

A Because they are out of the window

B Because they are smaller than the new element

C Because they are equal to the new element

D Because the deque is full

Smaller elements at the back are removed to maintain decreasing order and ensure the maximum is at the front.

What data structure is best suited for the Sliding Window Maximum problem?

A Deque

B Stack

C Queue

D Hash Map

A deque allows efficient insertion and removal from both ends, which is essential for this problem.

How many times can each element be added and removed from the deque in the Sliding Window Maximum algorithm?

A Multiple times

B Once

C Twice

D Never removed

Each element can be added once and removed once, leading to O(n) complexity.

What happens if the front element of the deque is outside the current window?

A It is removed from the front

B The window size increases

C It is moved to the back

D It stays in the deque

Elements outside the window are removed from the front to keep the deque relevant.

Explain how a deque helps solve the Sliding Window Maximum problem efficiently.

Think about how the deque keeps track of maximums as the window slides.

Describe the step-by-step process of updating the deque when moving the sliding window by one position.

Focus on what happens to the deque before and after adding the new element.