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Which of the following problems CANNOT be effectively solved using backtracking with bitmask memoization for partitioning into k equal sum subsets?

easy🔍 Pattern Recognition Q2 of Q15
Subsets & Combinations - Partition to K Equal Sum Subsets
Which of the following problems CANNOT be effectively solved using backtracking with bitmask memoization for partitioning into k equal sum subsets?
APartition array into k subsets with equal sums
BDivide array into k groups minimizing maximum subset sum
CFind the longest increasing subsequence in an array
DFind all subsets that sum to a target value
Step-by-Step Solution
Solution:

  1. Step 1: Analyze problem types

    Partitioning into k equal sum subsets and subset sum problems fit backtracking with bitmask memoization well.

  2. Step 2: Identify mismatch

    Longest increasing subsequence is a classic DP problem solved with different techniques, not subset partitioning or bitmask backtracking.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    LIS requires DP on indices, not subset partitioning [OK]
Quick Trick: LIS is DP, not subset partitioning [OK]
Common Mistakes:
MISTAKES
  • Confusing LIS with subset partitioning
  • Assuming all subset problems use bitmask backtracking
Trap Explanation:
PITFALL
  • Candidates often think all subset problems fit bitmask backtracking, but LIS is sequence-based DP.
Interviewer Note:
CONTEXT
  • Tests anti-pattern recognition and problem classification skills.
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