Greedy Algorithms are problem-solving methods that build up a solution piece by piece, always choosing the next piece that offers the most immediate benefit or seems the most promising.
At each step, choose the locally optimal option with the hope that this will lead to a globally optimal solution.
Goal: Find the minimum number of coins for a given amount.
coins = [1, 2, 5, 10, 20, 50, 100]
amount = 93
→ Use largest coins first → 50 + 20 + 20 + 2 + 1Goal: Select maximum number of activities that don’t overlap.
Greedy Choice: Always pick the next activity with the earliest finish time.
Goal: Compress data by assigning shorter codes to more frequent characters.
Greedy Choice: Combine the two least frequent nodes first.
Goal: Maximize value in the knapsack when fractions of items are allowed.
Greedy Choice: Pick items with the highest value/weight ratio first.
Greedy algorithms are ideal when the greedy choice property holds true. They are fast and elegant but must be applied carefully with correctness proofs.
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