L1 · FoundationsList basics~2 min · 5 tests

Find the Largest Number Without max()

Find the largest element of a Python list with a single loop instead of max(), and handle an empty list. Your first O(n) scan, run live in the browser.

The problem

Return the largest number in nums without using max() or sorting. Return None for an empty list.

Examples

Example 1
Input
```
find_max([3, 9, 2, 7])
```
Expected output
```
9
```
Example 2
Input
```
find_max([-5, -2, -9])
```
Expected output
```
-2
```

+ 3 hidden tests on Submit.

Edge cases to ask about

All negative
Empty list
Single element

Hints

0/3

How an interviewer scores this

0/9

1. Understand the problemRestate it, ask about edge cases and input size.
2. Write a brute-force solutionCorrect first, clever later.
3. Get the correct outputautoAll visible examples pass.
4. Analyse time complexityautoCommit to a Big-O in the Complexity Lab.
5. Analyse space complexityautoExtra memory, not the input.
6. Identify the bottleneckautoMeasure your code — where does it bend?
7. OptimiseUsually: a set or dict instead of a scan.
8. Explain why it worksSay it out loud to Solvi.
9. Test the edge casesautoSubmit passes every hidden test.

Python 3.13 · find_max

def find_max(nums):
    """Largest value without max(); None if empty."""
    # Write your solution here
    pass

⌘/Ctrl + Enter runs the examples

Your code runs in real CPython inside your browser — nothing is sent anywhere. The first run downloads the interpreter (about 6 MB, once). Your code is saved on this device as you type.

Complexity Lab

What does this cost as n grows?

Interviewers score the analysis as much as the code. Commit to an answer first — then check it, and measure your code against the optimal one at growing input sizes.

Time complexity of the optimal solution

Space complexity (extra memory)

Pick both to reveal the answer.

Measure it

Runs the function on inputs of size 250 up to 16,000 and records the time and peak memory. Slow solutions stop early — a short curve is itself the answer.

From brute force to optimal

The progression an interviewer wants to hear, one step at a time.

Approach	Time	Space	Idea
Track the best so far	O(n)	O(1)

Walkthrough of the optimal approach (try it yourself first)

Start best at nums[0] and replace it whenever a bigger value appears. Starting at 0 is the classic bug: it returns 0 for an all-negative list.

You can't do better than O(n): an element you skip could be the maximum.

Complexity: O(n) time, O(1) space. Every element must be seen once — any unseen element could be the largest.

Reveal the reference solution

def find_max(nums):
    if not nums:
        return None
    best = nums[0]
    for x in nums[1:]:
        if x > best:
            best = x
    return best

Follow-ups interviewers ask

Return the index of the largest instead.

Frequently asked interview questions

Core interview concepts, complexities, and follow-ups scored by hiring teams.

What is the time complexity of Find the Largest Number Without max() in Python?

The optimal solution runs in O(n) time and O(1) auxiliary space. Every element must be seen once — any unseen element could be the largest.

What follow-up questions do interviewers ask about Find the Largest Number Without max()?

Return the index of the largest instead.