What is a greedy algorithm?

A greedy algorithm makes the locally optimal choice at each step with the hope that this leads to a globally optimal solution. Unlike dynamic programming, which considers all subproblems, greedy algorithms commit to a choice and never reconsider it. Greedy algorithms work when the problem has optimal substructure and a greedy choice property: the locally optimal choice leads to a globally optimal solution without needing to examine alternatives.

How do I know when to use a greedy algorithm versus dynamic programming?

Try greedy first if: (1) the problem involves intervals or sorting by a single key, (2) making the most extreme choice first (largest, smallest, earliest, latest) never eliminates a better solution, or (3) the problem structure has a natural greedy exchange argument. Use dynamic programming when choices interact such that an early decision affects the cost of later decisions, or when you can construct a counterexample to a greedy approach. In interviews, stating why greedy is safe before coding signals strong algorithmic reasoning.

What are the most common greedy problem categories in FAANG interviews?

Based on LeakCode's real interview data, the most reported greedy categories are: interval scheduling and activity selection, jump game reachability variants, task and job scheduling, minimum number of platforms or resources, gas station and circular route problems, and string rearrangement with frequency constraints. Interval-related greedy problems represent the largest single category in LeakCode's database.

How do I prove a greedy algorithm is correct in an interview?

Use the greedy exchange argument: assume an optimal solution differs from the greedy solution at some step. Show that swapping the greedy choice for the alternative in the optimal solution does not improve the solution (or makes it worse). This shows the greedy choice is at least as good as any alternative. You do not need a formal proof in an interview, but stating the exchange argument verbally demonstrates mathematical maturity and usually satisfies the interviewer.

How does LeakCode help with greedy algorithm prep?

LeakCode aggregates 51,000+ real interview reports from companies including Amazon, Google, Meta, and Microsoft. You can filter greedy questions by company to see which specific categories each company emphasizes. Amazon tends toward scheduling and resource allocation greedy problems. Google more often asks harder variants requiring proof of correctness. LeakCode gives you the company-level view that generic prep resources lack.

Greedy Algorithm Interview Questions: Complete Guide (2026)

Which greedy patterns FAANG companies ask most often, how to recognize when greedy is safe versus when dynamic programming is required, and what LeakCode's real data shows about company-specific frequency.

What greedy algorithm interviews actually test

Greedy algorithm problems appear deceptively simple because the solution code is often short. The interview value comes from testing whether you can argue why the greedy approach is correct, not just whether you can write it. An interviewer asking a greedy problem is evaluating your mathematical intuition about local versus global optima.

In LeakCode's database of 51,000+ real interview reports, greedy questions appear at every level. Mid-level rounds often use greedy as a faster alternative to DP that candidates must recognize during the interview. Senior-level rounds combine greedy with sorting, heaps, or union-find to create multi-step problems where the greedy insight is one component.

A common failure mode is applying greedy to a problem where it does not work and producing a solution that passes test cases but fails edge cases. Interviewers specifically probe edge cases to distinguish correctly reasoned greedy solutions from coincidentally correct ones.

Main greedy algorithm problem categories

Interval scheduling and activity selection

Select the maximum number of non-overlapping intervals. Sort by end time and greedily pick the interval that ends earliest. The exchange argument: replacing any selected interval with a later-ending one never allows more intervals to be selected afterward. This is the canonical greedy problem and the most reported category in LeakCode's dataset. Variations include minimum number of arrows to burst balloons and non-overlapping intervals deletion.

Jump game reachability

Determine if you can reach the last index, or find the minimum number of jumps to reach the end. Track the farthest reachable index greedily as you scan left to right. The greedy insight is that you never gain by holding a jump at the current level longer than necessary. Variants include jump game with variable cost and jump game in a grid. Heavy in Google and Meta coding rounds.

Task and job scheduling

Maximize throughput or minimize lateness by scheduling jobs with deadlines and profits, or minimize the number of time units wasted due to cooldown between identical tasks. These often combine greedy with a max-heap. Sorting jobs by deadline or profit ratio is the typical greedy key. Heavy at Amazon and companies with operations research applications.

Gas station and circular route

Determine if a circular route is completable given gas supply and cost at each station. The greedy insight: if the total net gas is non-negative, a valid starting station exists, and it is the station after the longest cumulative deficit streak. O(n) time single-pass solution. Common in Amazon and Uber coding rounds.

String rearrangement with frequency constraints

Rearrange a string so no two adjacent characters are identical, or rearrange to satisfy a minimum distance between identical characters. Use a max-heap by character frequency and greedily place the most frequent available character. If at any step no valid character exists, the rearrangement is impossible. Appears in Meta and Google string-focused rounds.

How to reason about greedy correctness

Before implementing, confirm two properties: (1) optimal substructure (optimal solution to the problem contains optimal solutions to its subproblems) and (2) the greedy choice property (there exists an optimal solution that includes the greedy choice). You do not need a formal proof, but sketching the exchange argument verbally takes 30 seconds and separates strong candidates from those who are pattern-matching without understanding.

1.Name the greedy key. What are you sorting by or optimizing at each step? Earliest end time, highest frequency, maximum gain? State it explicitly before coding.
2.State the exchange argument briefly. "Swapping the greedy choice for any alternative never improves the outcome." One sentence is sufficient in an interview.
3.Test with a counterexample attempt. Try to find an input where greedy fails. If you cannot construct one, proceed to code with confidence. If you can, switch to DP.

Companies that ask greedy algorithm questions

Browse real greedy algorithm interview questions from these companies on LeakCode:

Google Amazon Meta Microsoft Uber LinkedIn Airbnb Stripe

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How LeakCode helps with greedy prep

LeakCode indexes real candidate reports from 1Point3Acres, Blind, LeetCode Discuss, Glassdoor, and Reddit. You can filter greedy questions by company and see which specific categories appear most in recent interview cycles. This prevents you from over-preparing exotic greedy variants that your target company has never asked.