Clarify the dimensionality of the points (2D, 3D, or nD) and whether the distance is Euclidean or another metric. Then explain the straightforward formula: distance = sqrt((x2-x1)^2 + (y2-y1)^2) for 2D, and generalize to n dimensions. Discuss edge cases like identical points and potential overflow with large coordinates.
Pro tip: Mention that in production code, you might avoid the square root if only comparing distances, and use a numerically stable method like Math.hypot to prevent overflow. This shows awareness of real-world constraints beyond the textbook formula.
Ask about the dimensionality of the points, the distance metric (Euclidean, Manhattan, etc.), and the expected input types (integers, floats).
For Euclidean distance in 2D: sqrt((x2-x1)^2 + (y2-y1)^2). Generalize to n dimensions by summing squared differences.
Consider identical points (distance 0), very large coordinates causing overflow, and floating-point precision issues.
If only comparing distances, skip the square root. Use Math.hypot or similar for numerical stability.
Implement the solution clearly, handling input validation and returning the result.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.