A greedy algorithm builds a solution by taking the best immediate choice at each step. It is correct only when the problem has the greedy-choice property and optimal substructure, as in interval scheduling and Huffman coding.
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A greedy algorithm builds a solution by taking the best immediate choice at each step. It is correct only when the problem has the greedy-choice property and optimal substructure, as in interval scheduling and Huffman coding.
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It needs the greedy-choice property, where a local optimum leads to a global optimum, plus optimal substructure.
Interval scheduling, Huffman coding, and minimum spanning trees are canonical greedy problems.
When greedy choices fail, dynamic programming that explores options is usually required.
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Prove the greedy-choice property, often with an exchange argument, or test it against dynamic programming on small cases.
Greedy commits to one choice per step; dynamic programming considers all choices and reuses subproblem results.
