Understand core algorithmic meta-heuristics: Brute Force, Divide & Conquer, Greedy choice theorems, Backtracking pruning, and Dynamic Programming.
Exhaustive search spaces, power sets, permutations, dividing into non-overlapping subproblems, and recombining solutions.
Greedy-choice property, optimal substructure, exchange arguments and matroid theory, and identifying when greedy fails where DP succeeds.
Systematic state space tree exploration, explicit choice-make-unmake invariants, branch pruning bounding functions, and N-Queens/Sudoku.
Overlapping subproblems + optimal substructure, Top-Down Memoization vs Bottom-Up Tabulation, state representation, transitions, and space reduction.