What is the optimal time complexity of solving Count 1s in Binary Array?

The optimized solution achieves a time complexity of O(n log n) by using sorting, mathematical shortcuts, or hash table lookups.

What is the auxiliary space complexity for Count 1s in Binary Array?

The space complexity is O(1) since we only use a minimal/constant amount of extra memory for tracking variables (or auxiliary space if dynamic structures are allocated).

What are the typical edge cases we must handle in Count 1s in Binary Array?

Key edge cases include empty collections, negative or large bounds causing integer overflow, arrays with only one element, or inputs where target criteria are not met.

How can we optimize the brute force approach for Count 1s in Binary Array?

Brute force methods often inspect all possible combinations. We optimize this by sorting the input list to enable binary search, or tracking processed items in a set to avoid redundant nested loop lookups.

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Count 1s in Binary Array

Learn how to solve the 'Count 1s in Binary Array' problem. This detailed resource details brute force and optimized approaches.

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Problem Statement

Easy

Write a function count_ones(arr) that takes a binary array arr sorted in descending order (where all 1s appear before all 0s) and returns the count of 1s in the array. Your solution should run in O(log n) time.

Constraints

•0 <= len(arr) <= 10^5
•arr[i] is either 0 or 1

Examples

Example 1

Input

count_ones([1, 1, 1, 1, 0, 0, 0])

Output

Explanation

There are four 1s at the beginning of the array.

Example 2

Input

count_ones([1, 1, 0, 0, 0])

Output

Explanation

There are two 1s at the beginning of the array.

Need a Hint?

Analyze the input constraints. Try sorting first (O(n log n)) or using a hash map/set to track seen elements in O(n) time.

Edge Cases to Watch

Empty list or null input variables
Single item lists/arrays
Extremely large input bounds causing integer or stack overflow

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