Greedy algorithms are a powerful problem‑solving approach in data structures and algorithms where you make the locally optimal choice at each step with the hope of reaching a globally optimal solution. Instead of exploring every possible combination, a greedy strategy chooses the best immediate option and moves forward. This makes greedy solutions extremely efficient and commonly used in competitive programming and technical interviews.
In coding interviews, greedy problems often appear because they test your ability to recognize patterns and prove correctness. Many well‑known interview questions—like interval scheduling, minimum number of coins, or merging intervals—can be solved using greedy thinking. Companies such as Google, Amazon, and Meta frequently include greedy-style optimization questions to evaluate whether candidates can design efficient algorithms without brute force.
Several common patterns appear in greedy problems. Recognizing these patterns helps you quickly identify when a greedy approach works:
However, greedy algorithms do not always work. The key challenge is proving that the locally optimal choice leads to the global optimum. In cases where greedy fails, problems often require Dynamic Programming instead.
FleetCode offers 390 Greedy practice problems ranging from beginner-friendly patterns to advanced interview challenges. By solving these problems and learning the reasoning behind each solution, you'll develop the intuition to quickly recognize greedy opportunities in interviews and competitive programming.
Most greedy problems operate on arrays of intervals, costs, or values. Understanding traversal, indexing, and basic array manipulation is essential before applying greedy decisions.
Many classic greedy algorithms such as Prim’s and Kruskal’s minimum spanning tree operate on graphs, making graph fundamentals useful for advanced greedy questions.
A large portion of greedy algorithms rely on sorting data first (e.g., intervals by end time). Sorting establishes the order that makes greedy choices correct.
Learning dynamic programming alongside greedy helps you recognize when greedy works and when optimal substructure requires a DP solution instead.
Heaps help repeatedly select the minimum or maximum element efficiently, which is a common greedy strategy in scheduling, merging, and resource allocation problems.
Start Easy, progress to Hard.
Frequently appear alongside Greedy.
Common questions about Greedy.
Greedy algorithms make the best local choice at each step in order to achieve a globally optimal solution. They avoid exploring all possibilities and instead rely on problem properties that guarantee the greedy choice leads to the optimal answer.
Yes. Greedy algorithms frequently appear in FAANG-style interviews because they test optimization thinking and algorithm design. While not as frequent as arrays or dynamic programming, greedy questions are common in medium-level interview rounds.
Start by understanding the greedy-choice property and optimal substructure. Then practice classic patterns such as interval scheduling, sorting-based selection, and heap-based selection. Studying why greedy works—or fails—on each problem builds strong intuition.
Popular interview problems include Activity Selection, Jump Game, Gas Station, Minimum Number of Arrows to Burst Balloons, and Merge Intervals. Practicing 30–50 representative greedy problems is usually enough to recognize the most common interview patterns.
Look for problems where making the locally optimal choice never prevents reaching the global optimum. If the problem can be solved by repeatedly selecting the best available option after sorting or prioritizing elements, a greedy approach is likely applicable.
Most candidates benefit from solving 40–80 greedy problems covering core patterns such as interval scheduling, resource allocation, and greedy with heaps. Platforms like FleetCode provide 390 problems so you can progress from fundamentals to advanced interview challenges.
