Binary Search Questions at Anduril: What to Expect

Binary Search isn't just about finding an element in a sorted array. At Anduril, a defense technology company building autonomous systems and sensing platforms, it's a fundamental pattern for solving real-time, resource-constrained problems. Efficiently locating data points in sensor logs, determining optimal thresholds for detection algorithms, or finding the minimum viable configuration for a system are all problems where a naive linear scan is unacceptable. The computational efficiency of O(log n) is critical when processing massive datasets from LiDAR, radar, or other sensor feeds in real time. Mastering binary search demonstrates you can think about algorithmic efficiency—a non-negotiable skill for building performant systems at scale.

What to Expect — Types of Problems

Anduril's binary search questions typically extend beyond the classic textbook implementation. You should be prepared for two main categories:

1. Modified or "Search Space" Binary Search: These problems involve searching in a rotated or partially sorted array, or finding a specific condition (like the first bad version, peak element, or minimum in a rotated array). The core challenge is adapting the standard low, high, mid logic to a new comparison condition.
2. Binary Search on Answer (or "Optimization" Problems): This is a powerful and common pattern. Here, you are not searching a physical array but a range of possible answers. For example, "Find the minimum capacity to ship packages within D days" or "Allocate the minimum number of pages to students." You define a feasible condition, then use binary search to find the smallest or largest value in the answer space that satisfies it. This pattern is highly valued for solving optimization problems common in resource allocation and system design.

How to Prepare — Study Tips with One Code Example

Internalize the standard binary search pattern first. Then, practice by identifying the search space and the condition you are checking at each step. Always ask: "What am I searching for? Is it an index in an array, or a value within a range? What is the rule that tells me if I should go left or right?"

A key pattern to master is the generalized form that avoids infinite loops and cleanly handles edge cases. Here is a robust template for finding the first occurrence of a target (or the insertion point):

def binary_search_first(arr, target):
    low, high = 0, len(arr)  # Note: high is exclusive
    while low < high:
        mid = low + (high - low) // 2
        if arr[mid] < target:
            low = mid + 1   # Search right
        else:
            high = mid      # Search left (includes arr[mid] == target)
    return low  # First position where target could be inserted

This template is versatile. For finding the last occurrence, you would modify the condition to move low when arr[mid] <= target. Practice applying this pattern to both array-index problems and answer-space problems.

Recommended Practice Order

1. Classic & Modified Search: Start with standard binary search, then move to variations like "Find First and Last Position of Element," "Search in Rotated Sorted Array," and "Find Peak Element."
2. Binary Search on Answer: Begin with foundational problems like "Sqrt(x)" and "Capacity To Ship Packages Within D Days." These teach you to define a feasible(mid) function.
3. Advanced Applications: Tackle harder optimization problems like "Split Array Largest Sum" (also known as "Minimum Pages") and "Koko Eating Bananas." These combine the pattern with more complex condition checking.
4. Anduril-Specific Context: Consider how you might apply the pattern to sensor data—like finding the earliest timestamp where a threshold is crossed in a sorted log, or determining the minimum detection range for a system given constraints.

Practice Binary Search at Anduril