Learn how to solve the Non-Overlapping Intervals coding problem to prepare for your next technical interview!

"Merge Intervals" is one of those classic algorithm problems that shows up frequently in technical interviews. It's a great test of your...

Jump Game II is a classic follow-up to the original Jump Game problem. It’s not just about whether you can reach the end... now you have to do it in the fewest jumps possible! That small change turns a simple reachability problem into one that tests how well you can optimize greedy strategies or dynamic programming under pressure.

The "Jump Game" question is a popular one for interviews because it tests your ability to think greedily and work with dynamic movement through an array. It's a great warm-up for range-based greedy logic and helps build intuition for reachability problems, concepts that show up often in competitive coding and systems design.

The "Gas Station" problem is one of those deceptively simple problems that really tests your understanding of greedy strategies and circular arrays. It shows up frequently in interviews because it blends math, algorithmic reasoning, and a need for linear-time optimization. Whether you're optimizing a delivery route or designing a resource allocation system, this kind of thinking comes in handy.

"Candy" is one of those deceptively simple problems that tests your ability to think through constraints and find a clean, efficient strategy. The "Candy" problem is a classic greedy algorithm challenge often asked in interviews. It tests whether you can distribute limited resources while meeting strict conditions on relative ranking — something that comes up surprisingly often in real-world systems.

"Assign Cookies" is a problem that is a simple yet elegant introduction to greedy algorithms. It asks us to match two lists - one representing the greed of children and the other representing cookie sizes - in a way that maximizes happiness. It’s a great warm-up to practice sorting and thinking about matching strategies in real-world-like scenarios.

Determining the maximum water a container can hold between vertical lines is foundational in graphics rendering, fluid simulations, and two-pointer optimizations. The “Container With Most Water” problem challenges you to squeeze the best area in O(n) time.

Scheduling tasks under cooling constraints is a common challenge in operating systems, rate-limited APIs, and real-time pipelines. The “Task Scheduler with Cooling Interval” problem exercises frequency counting, greedy scheduling, and heap-based prioritization to minimize idle time.