CrackML by @ml.with.umang
Interview questions / Python & DSA
Python & DSA interview question

Optimize a Grid Computation From O(n²) to O(n)

Solve a grid-based problem and improve a quadratic baseline to linear complexity.

hardcodingEvidence 77/1001 source reportGoogle

The 60-second answer

Use identify reusable prefix/suffix or rolling state so repeated grid work is computed once. Target O(n) over the effective input size after preprocessing.

Build the answer in this order

1
Clarify constraints

Use identify reusable prefix/suffix or rolling state so repeated grid work is computed once.

2
Choose the approach

Target O(n) over the effective input size after preprocessing.

3
Prove complexity

Call out edge cases such as grid boundaries, empty rows/columns, duplicate work, and validating the optimized result against brute force.

4
Test edge cases

Explain the invariant before coding, then dry-run a small case and state time/space complexity.

A useful interview mental model

This is the shape of a strong answer—not a script to memorize.

01Clarify
02Approach
03Implement
04Test
05Complexity

Senior-level signal

  • Show the brute-force recurrence first, then identify the overlapping computation that can be cached or accumulated.
  • Keep a brute-force oracle for randomized tests; optimization correctness matters more than cleverness.

What the interviewer is really testing

Problem decomposition, correctness, data-structure choice, complexity reasoning, and clean implementation under pressure.

Likely follow-up questions

Can you improve the time or space complexity?
Which edge case is most likely to break this solution?
How would you test this under interview time pressure?

Common weak-answer patterns

  • Starting to code before constraints are clear.
  • Giving complexity without explaining why it is correct.
  • Skipping adversarial and boundary cases.