Backtracking Explained: N-Queens to Subsets
A complete guide to backtracking for coding interviews. Explains backtracking as constrained depth-first search over a decision tree, then introduces the universal choose/explore/unchoose template that solves nearly every backtracking interview problem. Works through four problem archetypes with full Python solutions: subsets (include/exclude decisions), permutations (ordering with a used-set), combination sum (reuse with pruning), and N-Queens (constraint satisfaction with column and diagonal tracking). Covers time complexity for each archetype, the classic mutable-list copy bug, and how to recognize a backtracking problem from its problem statement.