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The following code attempts to implement the space-optimized 0/1 Knapsack solution. Identify the line that contains a subtle bug that can cause incorrect results.

medium🐞 Bug Identification Q14 of Q15
Dynamic Programming: Knapsack - 0/1 Knapsack Problem
The following code attempts to implement the space-optimized 0/1 Knapsack solution. Identify the line that contains a subtle bug that can cause incorrect results.
def knapsack_space_optimized(weights, values, W):
    n = len(weights)
    dp = [0] * (W + 1)
    for i in range(n):
        for w in range(weights[i], W + 1):  # Note: iterating forwards
            dp[w] = max(dp[w], dp[w - weights[i]] + values[i])
    return dp[W]
ALine 5: Inner loop iterating forwards over weights.
BLine 4: Outer loop iterating over items.
CLine 2: Initializing dp array with zeros.
DLine 6: Updating dp[w] with max of current and new value.
Step-by-Step Solution
Solution:
  1. Step 1: Analyze iteration order in inner loop

    The inner loop iterates forwards from weights[i] to W, which causes the current item to be counted multiple times in the same iteration, violating 0/1 knapsack constraints.
  2. Step 2: Correct iteration direction

    Iterating backwards (from W down to weights[i]) ensures each item is only counted once per capacity, preserving correctness.
  3. Final Answer:

    Option A -> Option A
  4. Quick Check:

    Forward iteration causes overcounting items [OK]
Quick Trick: Inner loop must iterate backwards to avoid overcounting [OK]
Common Mistakes:
MISTAKES
  • Iterating forwards in dp array updates for 0/1 knapsack.
Trap Explanation:
PITFALL
  • Forward iteration looks natural but breaks the 0/1 constraint by reusing updated dp values in the same iteration.
Interviewer Note:
CONTEXT
  • Tests if candidate knows subtle iteration order requirement in space-optimized DP.
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