DP Knapsack

Learning Outcome

Subset, partition, coin, and target-sum families.

Pattern Recognition

Intuition

Knapsack DP asks whether each item is used, skipped, or used multiple times.

Exact Practice Question Names

• Partition Equal Subset Sum
• Target Sum
• Coin Change
• Coin Change II
• Subset Sum Problem

Interview Approach

1. Name the exact family.
2. Write the invariant or state.
3. Pick the standard container or recurrence.
4. Dry-run the smallest example.
5. State complexity and one follow-up.

Pseudocode

identify pattern
initialize state/container
for each input unit:
  update state safely
  update answer when invariant is valid
return answer
  

Sample Dry Run

Take the smallest DP Knapsack example, track the state after every operation, and verify the final answer before coding.

Edge Cases

• Empty or single-item input
• Duplicate values
• Boundary constraints
• Large values requiring long/int64

Common Mistakes

• Memorizing code without naming the invariant
• Skipping the brute-force baseline
• Not explaining why the optimized approach is correct

Complexity

Java, C++ and Python Notes

• Java: prefer explicit classes and clear helper methods over clever one-liners.
• C++: use vector, unordered_map, set, priority_queue, and long long when sums can grow.
• Python: keep state readable with dict, set, deque, heapq, and lru_cache where appropriate.

Quick Revision Checklist

• Name the pattern before coding.
• State the invariant or DP state in one sentence.
• Dry-run the smallest non-trivial example.
• Close with time and space complexity.