Last Updated: August 2, 2026
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20 min read
1. Introduction
What are GREEDY Interview Patterns?
GREEDY Interview Patterns represent the high-yield structural techniques used in technical interviews to solve linear, matrix, or non-linear computational problems efficiently.
Why study them?
Instead of memorizing individual LeetCode solutions, mastering these core patterns allows you to instantly recognize problem invariants and apply verified
O(N) or
O(N log N) templates.
Where is it Used?
High-Throughput Engines: CPU Conference Scheduling: Minimizing overlapping meeting room booking conflicts.
System Resource Optimization: Cloud Load Balancing: Optimizing batch jobs to maximize throughput under resource caps.
2. Mental Model
Imagine solving a complex puzzle where each piece has a predictable shape:
Once you identify the key pattern signal in the problem statement, you pull out the corresponding template.
You configure boundary invariants (such as left/right pointers, heap sizes, or stack monotonicity) and process elements in a single streamlined pass.
3. Core Patterns & Implementations
1. Interval Scheduling (Sort by End Time)
Sort intervals by finish time. Pick early finishing intervals to leave maximum room for remaining tasks.
2. Reachability Boundary Scan (Jump Game)
Iterate through array tracking
maxReach = max(maxReach, i + nums[i]). If
i > maxReach, destination is unreachable.
3. Surplus Accumulator (Gas Station)
Accumulate net gas
gain = gas[i] - cost[i]. If total sum >= 0, a valid start exists; reset start when tank drops < 0.
4. Visual Trace
5. Real-World Applications
Application 1: CPU Conference Scheduling: Minimizing overlapping meeting room booking conflicts.
Application 2: Cloud Load Balancing: Optimizing batch jobs to maximize throughput under resource caps.
6. Interview Perspective
How Interviewers Ask This Topic
Interviewers verify whether you recognize key problem constraints and select optimal patterns rather than defaulting to brute force.
Common Mistakes
Warning: 1. Sorting by start-time instead of end-time in Interval Non-Overlapping : Sorting by start-time instead of end-time in Interval Non-Overlapping — fails to maximize open room.
> 2. Restarting gas station check from index 0 on failure instead of skipping to i + 1 in O(N) time.: Restarting gas station check from index 0 on failure instead of skipping to i + 1 in O(N) time.
7. Summary
Pattern 1: Interval Scheduling (Sort by End Time) -> O(N) optimized pass.
Pattern 2: Reachability Boundary Scan (Jump Game) -> Invariant boundary handling.
Pattern 3: Surplus Accumulator (Gas Station) -> Optimal time and space efficiency.
8. Quiz
Question 1: What is the main time complexity advantage of using these patterns?
Answer: They reduce nested loop brute force solutions (O(N^2) or higher) down to optimal linear O(N) or logarithmic O(N log N) bounds.
Question 2: How do you choose between Pattern 1 and Pattern 2 during an interview?
Answer: Look at problem invariants such as whether the input array is sorted, contiguous, or requires global bounds.
Question 3: Why is space complexity critical in production environments for these patterns?
Answer: In-place algorithms (O(1) auxiliary space) eliminate garbage collection overhead and prevent out-of-memory errors on large data streams.
Question 4: True or False: You should always test edge cases (empty input, single element, negative values) before finishing code.
Answer: True. Edge cases reveal hidden pointer out-of-bounds errors or division-by-zero crashes.
Question 5: What is the best strategy when stuck on an interview problem?
Answer: Walk through a small manual example, state the brute force solution, identify unnecessary repeated work, and apply one of these core patterns.
