At companies like Stripe or Netflix, interviewers often ask how you would improve a working but slow algorithm. They want to see structured reasoning, not just a faster final answer.
Explain how you would optimize an existing algorithm to improve performance. In your answer, address:
Focus on a practical engineering approach: start from baseline complexity, profile or reason about hot spots, propose targeted improvements, and discuss trade-offs. You do not need to cover low-level compiler or hardware tuning unless it directly affects the algorithmic design.
The first step in optimization is understanding the current algorithm's time and space complexity. Without a baseline, it is impossible to know whether a proposed change meaningfully improves performance or just changes implementation details.
for i in range(n):
for j in range(n):
if nums[i] == nums[j]:
... # O(n^2)
Optimization should target the dominant cost, not random parts of the code. In interviews, this usually means finding the nested loop, repeated recomputation, expensive search, or unnecessary sorting that drives the asymptotic complexity.
for x in queries:
if x in arr: # O(n) membership check repeated many times
count += 1
Many optimizations come from replacing one data structure with another. For example, switching from a list to a hash set can reduce repeated membership checks from O(n) to average O(1), changing the overall algorithm from O(n*m) to O(n+m).
seen = set(arr)
for x in queries:
if x in seen:
count += 1
If the same work is repeated, preprocessing or memoization can avoid redundant computation. This is common in dynamic programming, prefix sums, and lookup tables where extra memory is used to reduce repeated time cost.
prefix = [0]
for num in nums:
prefix.append(prefix[-1] + num)
A faster algorithm may use more memory, be harder to maintain, or only help for large inputs. Strong answers explain not only how performance improves, but also the cost, assumptions, and when the optimization is worth applying.
