How do you solve Counter on LeetCode?

Counter (LeetCode #2620) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(1) per call time complexity and O(1) space complexity. Key insight: Understanding closures is crucial for maintaining state in functions.

What is the brute force solution for Counter?

The brute force approach for Counter is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves creating a function that keeps track of the current count using a simple variable. Each time the function is called, it increments this variable by 1 and returns the new value. The optimal Optimal Solution approach improves this to O(1) per call.

What are the interview tips for Counter?

Clearly explain your thought process while coding. Test your function with edge cases, such as negative numbers or zero. Be prepared to discuss the time and space complexities of your solution.

What are common mistakes when solving Counter?

Not using closures correctly, leading to incorrect state management. Overcomplicating the solution instead of leveraging simple variable increments.

#2620

Counter

Easy

ClosureState Management

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(1) per call
Space	O(1)	O(1)

💡

Intuition

Time O(1) per callSpace O(1)

The optimal solution leverages closures in JavaScript (or similar constructs in other languages) to maintain state without unnecessary overhead. This allows us to efficiently manage the counter with minimal space and time complexity.

⚙️

Algorithm

3 steps

1Step 1: Define a function that initializes a counter variable with n.
2Step 2: Return an inner function that increments and returns the counter variable.
3Step 3: Each call to the inner function updates the counter in constant time.

solution.py8 lines

1def createCounter(n):
2    count = n
3    def counter():
4        nonlocal count
5        result = count
6        count += 1
7        return result
8    return counter

ℹ

Complexity note: The time complexity is O(1) for each call because we are simply returning and incrementing a variable. The space complexity is O(1) as we are not using any additional data structures.

1Understanding closures is crucial for maintaining state in functions.
2Incrementing a variable within a closure allows for efficient state management.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.