Greedy Questions at Huawei: What to Expect

Greedy algorithms are a small but critical slice of Huawei’s technical interview. With 3 out of 20 questions typically dedicated to this paradigm, you cannot afford to be unprepared. These questions test your ability to identify optimal local choices that lead to a globally optimal solution, a skill that mirrors real-world optimization problems in networking and resource allocation—core to Huawei’s business. Acing them demonstrates sharp problem-solving intuition under constraints.

What to Expect — Types of Problems

Huawei’s greedy questions are rarely abstract. They are applied problems where the greedy choice is often intuitive but requires rigorous justification. Expect these categories:

1. Interval Scheduling & Merging: Problems like meeting room scheduling, merging overlapping intervals, or finding minimum points to cover intervals. These test your ability to sort and select based on endpoints.
2. Resource Allocation & Partitioning: Tasks like assigning tasks to workers, splitting arrays into fair subsets, or minimizing maximum load. The greedy choice often involves processing the largest or smallest item first.
3. String Manipulation & Lexicographic Order: Constructing the smallest/largest number from digits, palindrome checks, or removing digits to form a specific result. The strategy usually involves a stack-like approach with conditional pops.
4. Simple “Coin Change” Style: Note that the standard coin change for minimum coins is dynamic programming, but if the coin system is canonical (like standard currency), a greedy “take the largest coin first” approach works. Huawei may test your ability to recognize when greedy is valid.

The key is to quickly identify the sorting key (by start time, end time, value, or cost) and the rule for making the immediate best choice.

How to Prepare — Study Tips with One Code Example

First, master the proof of concept. For any greedy algorithm, you must be able to argue why making the locally optimal choice at each step leads to a global optimum. Common proof techniques include exchange arguments or proving optimal substructure.

Second, pattern recognition is everything. Do not memorize problems—memorize the decision pattern. For interval problems, you almost always sort by ending time. For partitioning, you often sort to process largest items first.

Practice this classic pattern: the "Merge Intervals" problem. The greedy choice is to sort intervals by their start time, then merge any overlapping interval with the previous one. If no overlap, start a new current interval.

def merge(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = []
    for interval in intervals:
        # if merged list is empty or no overlap
        if not merged or merged[-1][1] < interval[0]:
            merged.append(interval)
        else:
            # there is overlap, merge by updating the end
            merged[-1][1] = max(merged[-1][1], interval[1])
    return merged

Recommended Practice Order

Build competence sequentially. Start with foundational problems to internalize the sorting and selection pattern.

1. Foundations: Merge Intervals, Meeting Rooms (LeetCode 252), Assign Cookies.
2. Core Patterns: Task Scheduler, Partition Labels, Valid Parenthesis Check.
3. Advanced Application: Minimum Number of Arrows to Burst Balloons, Remove K Digits, Queue Reconstruction by Height.
4. Huawei-Specific: Finally, practice questions filtered by the Huawei tag on platforms like CodeJeet to see how these patterns are framed in their interviews.

Always code by hand or in a simple text editor to simulate the interview environment. Time yourself.

Practice Greedy at Huawei