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15 Common Algorithms for Interviews & How to Use Them

October 27, 2025

I still remember freezing in front of a whiteboard when an interviewer asked me to write a graph traversal from scratch. My brain just… blanked. It wasn’t that I didn’t know BFS or DFS, I just couldn’t pull it together under pressure. That moment taught me something: coding interviews aren’t about memorizing code; they’re about thinking clearly when it counts.

If you’re serious about landing a big tech job, you’ve got to be fluent in the classics: arrays, linked lists, trees, graphs, recursion, dynamic programming, all that stuff. These are the building blocks that every interviewer secretly wants you to handle with calm confidence.

That’s why I built Interview Coder, an AI Interview Assistant. It helps you actually practice like it’s the real thing, targeted algorithm drills, instant feedback, and mock sessions that make you sweat (in a good way). Do this consistently, and walking into your following interview won’t feel like a panic test; it’ll feel like another rep at the gym.

Would you like me to make a version that’s slightly more SEO-optimized (e.g., includes “common algorithms for interviews” naturally a few times without sounding forced)? It’ll keep the same voice but improve keyword performance.

Summary

  • Most engineers think they’re decent at algorithms because they’ve watched a few YouTube videos or brute-forced LeetCode for a week. That illusion breaks fast when you're on a real screen and the interviewer throws something slightly off-pattern. I’ve seen it happen. Hell, it’s happened to me.
  • Here’s the reality: 70% of companies still ask algorithm questions. And not the kind you solve by memorizing a few tricks. You need to recognize patterns, make tradeoffs fast, and explain your thinking without rambling. If you can’t do that, you’re not ready, and no, solving 300 random problems won’t fix it.
  • When I was prepping for Amazon and Meta, what moved the needle wasn’t grinding endlessly; it was practicing the correct problems with time pressure and forcing myself to explain every decision I made. That’s precisely why I built Interview Coder. It mimics the stress of a real interview. It throws curveballs. It checks if you’re actually thinking or just regurgitating.
  • Because guess what? Tradeoffs matter. You should know when DFS is enough vs. when to reach for Dijkstra. Why does memoizing a naive recursive solution flip exponential time to linear? Why merge sort is safe, but quick sort bites you if the pivot sucks. That kind of stuff? It doesn’t come from flashcards.
  • Also, the data support this. Reflective practice beats raw volume. One study from Coding Interview 2025 showed a 50% bump in success for people who trained with structured reflection. And AI tools? Over 85% of users say they perform better with mock simulations, not because the AI is magical, but because it forces you to deal with uncertainty like a real interviewer would.
  • At the end of the day, it’s not about memorizing 100 questions. It’s about actually understanding 15 patterns, knowing when to use them, and being able to talk through your decisions like someone they’d want on the team. That’s the skill. That’s what Interview Coder helps you build.

15 Common Algorithms For Interviews (With Examples)

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I used to think I just needed to “know the algorithms.” Then I bombed a mock interview by freezing mid-sentence on a simple binary search variant. Not because I didn’t know it, but because I couldn’t explain why it worked in that specific use case.

Here’s the truth: Interviewers aren’t grading you on memorized code. They’re watching how you think out loud under pressure. They want to know if you can choose the correct pattern and back it up with calm, clear reasoning, even if you're staring at a half-broken whiteboard in a room that smells like leftover pizza.

I built Interview Coder to fix that part. Not to help you memorize bubble sort again, but to practice deciding between patterns when the clock’s ticking. I’ve used this exact list to prep for Meta, Amazon, and TikTok interviews, and I make every candidate I mentor start with it.

So here it is. No magic. No fluff. Just the 15 patterns that actually come up with notes on how to talk through them when it counts.

1. Dynamic Programming

You already know what this is, such as memoizing stuff to avoid repeating work. Most people think of it as some advanced technique, but it’s really just, “Did I already solve this? Then stop redoing it.”

Used For

Knapsack, coin change, and Fibonacci

Talk Through

What’s the state? What’s the recurrence? How are you caching?

2. Tree Traversal

The tree’s not the hard part. The traversal orders are preorder, inorder, postorder, same visit, and different sequence.

Used For

Reconstructing trees, symmetry checks, and recursive problems

Talk Through

What’s your base case? What’s the order? Stack space?

3. Graph Traversal (BFS / DFS)

You’ve done this 100 times. The real question is: when do you use BFS vs DFS?

Used For

Connected components, cycle detection, shortest path (unweighted)

Talk Through

How do you handle visited nodes? Why this traversal and not the other?

4. Linear Search

If you bring this up in an interview, you'd better explain why you're not using something better.

Used For

Small arrays, unsorted data, fundamental cases

Talk Through

Why is brute-force acceptable here? What’s the tradeoff?

5. Binary Search

This is the one pattern you must be able to explain in your sleep. And still, most people screw up the loop condition.

Used For

Sorted arrays, rotated arrays, weird search space tricks

Talk Through

What’s your invariant? What does your loop guarantee?

6. Bubble Sort

Not useful in practice, but it shows whether you understand what a swap is and when a sort can early exit.

Used For

Toy problems, first-round warmups

Talk Through

Why is it inefficient? When does it finish early?

7. Insertion Sort

It looks dumb at first until you realize it crushes on almost-sorted input.

Used For

Small input, hybrid sorts (e.g., TimSort)

Talk Through

Why is it better than bubble? What’s the best-case behavior?

8. Selection Sort

Brute-force minimalism. Fewest swaps. Terrible on time, great on write-limited memory.

Used For

Write-sensitive systems, sorting with constraints

Talk Through

Why use this over insertion sort? What’s the pattern for selecting min?

9. Hashing

This is your shortcut pattern. If you ever need an O(1) lookup, your brain should immediately yell hash table.

Used For

Frequency maps, memoization, uniqueness checks

Talk Through

What’s the hash function? How do you handle collisions?

10. Depth-First Search (DFS)

You go deep first. Backtrack when stuck. People confuse it with recursion; it’s not the same thing.

Used For

Backtracking, topological sort, path-finding

Talk Through

How are you marking visited? Are you pre/post-ordering?

11. Breadth-First Search (BFS)

This is the shortest-path go-to when weights are equal. Always think of BFS when “fewest steps” shows up.

Used For

Shortest path in a grid, level order traversal

Talk Through

What’s in the queue? Why does this guarantee a minimum steps?

12. Divide and Conquer

Split the problem, handle each piece, and merge them. Most people can code it. Very few can explain the cost correctly.

Used For

mergesort, quicksort, matrix multiplication

Talk Through

What’s your recurrence? What happens during combine?

13. Merge Sort

It’s the boring but reliable sort. Predictable time. Extra space cost.

Used For

linked lists, stable sort needs, massive files

Talk Through

Why not quicksort here? What’s the space complexity?

14. Quick Sort

Everyone loves this one until they forget to randomize the pivot and end up with the worst-case time.

Used For

In-place array sorts, general-purpose sorting

Talk Through

How are you choosing pivots? What’s the stack depth?

15. Greedy Algorithms

Make the best move right now and hope it all works out. Sometimes it works beautifully. Sometimes it fails spectacularly.

Used For

Dijkstra, Huffman coding, interval scheduling

Talk Through

Why is greedy valid here? Can you prove it gets the global best?

Related Reading

Top 10 Algorithm Interview Questions (That Actually Show Up)

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Let’s skip the fluff. This list isn’t for “brushing up” or “exploring your interests.” This is your crash course in what interviewers will actually throw at you. You either show up sharp, or you get chewed up by the clock and ghosted before you hit “Thank you for your time.”

These 10 questions? I’ve seen them. I’ve been hit with them. I’ve bombed a few, fixed my approach, and landed offers at Amazon, Meta, and TikTok because I treated questions like these as part of my warm-up, not a pop quiz.

You’ll get:

  • What the question really asks.
  • What the interviewer is silently judging you on.
  • How I would go about it under pressure.
  • Where candidates flinch (and how to not be that guy).

Run through these, time yourself, and don’t just settle for “I solved it.” Solve it three different ways and figure out where you break.

1. What Is An Algorithm?

What They Want

Not a textbook quote. Can you explain what an algorithm is without sounding like ChatGPT?

My Take

Inputs. Outputs. Steps in between. Keep it short. Use a real example (even bubble sort if you must).

Why It Matters

They want to see if you get how instructions become logic. Don’t overcomplicate.

Watch Out For

They’ll throw in a variation that asks you to compare two algorithms based on speed or memory. Be ready with a quick Big-O comparison.

2. How Do You Figure Out Time And Space Complexity?

What they want

You actually need to know what Big-O means beyond “O(n log n) sounds smart.”

How I do it

  • Find the part of your code that loops hardest.
  • Count the steps with n.
  • Ignore constants unless they’re painful.
  • If it’s recursive, write the recurrence. If you can’t solve it, be honest and talk through your limits.

Heads Up

They'll sometimes shift the input to something weird (like nearly sorted or huge-but-mostly-empty) just to see if you fall for the usual assumptions.

3. Describe Linear Search

What They Want

Do you even remember CS101, or are you copy-pasting from LeetCode?

How I Do It

Loop through, compare at each index, and return if it matches. Don’t overthink.

Gotcha

Works fine unless you’re on a linked list or a stream, in which case your O(n) behavior suddenly isn’t so obvious.

4. What Is A Sorting Algorithm?

What They Want

Can you pick the right tool based on the input shape, memory, or stability requirements?

How I Do It

I default to quicksort unless told otherwise. If it's sorted chiefly, I say insertion. If memory sucks, I say heapsort.

Tip

Don’t explain the algorithm like a professor. Explain it like you're helping a junior dev who’s in a panic before pushing to prod.

5. BFS vs DFS: What’s The Difference?

What they want

Can you map these to real problems, or do you just know the acronym?

Roy tip

  • BFS = shortest unweighted paths, queues.
  • DFS = topological sort, backtracking, stack (or recursion). Memory tradeoff? BFS eats more.

Trick Question Alert

They may ask you to adapt one for giant graphs or limited memory. You should have thoughts on iterative deepening or bidirectional search.

6. Write A String Reversal Algorithm.

What they want

Can you swap stuff without making dumb off-by-one mistakes?

How I do it

Two pointers, one at each end —swap until they meet. Simple. If you mess this up, they assume your bug rate in prod will be high.

Twist They'll Throw At You

“Now reverse it in a language with immutable strings.” If you didn’t prep for that, congratulations —you just got cornered.

Fact You Should Know

Candidates who practice algorithm problems with variation, not just solving, but re-solving with different methods, increase their pass rates by 50% (Coding Interview, 2025). Most people grind LeetCode. The ones who get offers break their solutions on purpose to get better.

7. What’s a greedy algorithm? When does it work?

What They Want

Can you prove it works? Do not just say "it works here."

How I Do It

Mention the greedy choice property. Show one example (coin change or interval scheduling). Then give an example where greedy fails.

Watch Out For

They'll ask you to switch from greedy to dynamic programming. If you panic at that pivot, you're toast.

8. Swap Two Integers In Java Without Using A Temp Variable.

What They Want

Bitwise skills or arithmetic tricks with safety in mind.

How I Do It

XOR method (classic), but I also mention overflow risk with a = a + b style. Then I say what I’d do in production (just use a temp, bro).

Extra Challenge

Immutable integers? Talk about boxing, language-specific quirks.

Mini-Rant

When interviewers change the question halfway through? That’s not them being mean. That’s real life. They’re checking how brittle your prep is. Most candidates freeze because they’ve memorized scripts, not built flexible thinking. That’s why I built Interview Coder to throw these pivots at you before they do.

9. Explain Dynamic Programming. Give Examples.

What They Want

You are not to fake it. Can you actually define state, recurrence, and transition?

How I Do It

  • State: what’s changing.
  • Recurrence: how states relate.
  • Memoization: if top-down.
  • Tabulation: if bottom-up.

Then I talk about real problems (knapsack, edit distance), not some abstract chart.

Extra Test

They'll ask if you can reduce the space from O(n) to O(1). If you just stare at them, that’s a red flag.

10. What Does A Hash Algorithm Do?

What They Want

Do you get why we use hashing, not just “HashMap is fast”?

How I Do It

  • Map significant inputs to minor keys.
  • Handle collisions (and mention how).
  • Talk about when hash is good, and when it’s better to use trees.

Bonus

Mention the difference between cryptographic and non-crypto hashes. You’ll sound like someone who reads docs.

Here’s The Part You Can’t Skip

Most people think “solving problems” is enough. It’s not. You get hired because you adapt fast while being watched. Static prep doesn’t teach that. Interview Coder does. You’ll get live pivots, real-time pressure, and feedback you can rewind and learn from without being graded by a stranger.

Try This

  • Pick any two problems above.
  • Time yourself solving each.
  • Then pick one and solve it three different ways.
  • Then explain why your first solution wasn’t the best.

That’s where the skill shows up. That’s what gets you hired.

Related Reading

Nail Coding Interviews with our AI Interview Assistant − Get Your Dream Job Today

I used to think that more LeetCode would solve everything more grinding, more problems, and more false hope. But let’s be real no one remembers all the edge cases under pressure. That’s why I built Interview Coder. It's the tool I wished I had when I bombed my first big tech interview. It sits quietly while you code, nudges you when you’re off-track, and helps you actually explain what you're doing without blanking out. Not some magic fix, just something that works when your brain short-circuits mid-interview.

85% of folks using tools like this say they did better in interviews. Another report showed a 50% bump in actual offers. You can keep brute-forcing the problem set, or you can be smarter about how you practice.

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