Subset Generation: When to Use It

Start with the idea

A problem is a good match for Subset Generation when it asks for power sets and optional choices. Here the concrete task is: Return subsets in bitmask order. The visible input [1,2] is an example, not a definition of the whole problem. Ask what operations the solution must support and what the input guarantees. Use pruning when partial choices can already be ruled out. Meet-in-the-middle instead trades two exhaustive half-enumerations for a smaller search space. The right choice follows from those guarantees, not from a familiar noun in the prompt.

The decisive invariant is: At each item branch on exclude or include, or extend every current subset. Try to say why it applies to the task without quoting the implementation. Which fact becomes known after one update, and which possible answers remain? Most patterns here explore a decision tree; meet-in-the-middle instead enumerates two halves and combines them. Define the partial choice or subset summary that the pattern retains. If that claim cannot be maintained on a legal input, the pattern is wrong or one of its preconditions is missing. A counterexample is more useful than a vague warning: change one property of the sample, trace the first two steps, and identify the exact claim that fails.

Compare costs before committing. The advertised bound is O(n*2^n) time · O(n) recursion excluding output; identify the operation responsible for it and the extra memory it needs.