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There are only a few useful parts of the algorithm theory in practice. The time complexity is surely one of them, but it is still overvalued in a sense that the

There are only a few useful parts of the algorithm theory in practice. The time complexity is surely one of them, but it is still overvalued in a sense that the actual performance on the real hardware and realistic input distribution is more important. And when you actually need algorithms, you always have a luxury of existing literatures and implementations. Real-time algorithm design in the interview is very unrealistic and in most cases only exists to detect interviewee's signaling.

EvilEy3 6y ago
> And when you actually need algorithms, you always have a luxury of existing literatures and implementations. And how well that works in practice? How will candidate even know where to look at if he has no idea what he needs to find?
- lifthrasiir 6y ago
  > How will candidate even know where to look at if he has no idea what he needs to find? That happens all the time, not just for algorithms. I don't expect candidates to know every possible algorithm (as I surely don't), I expect candidates to identify and learn what's required for the task. A knowledge of the specific algorithm is not of much value. The ability to learn and possibly implement algorithms is.
  - EvilEy3 6y ago
    > I expect candidates to identify and learn what's required for the task And that comes for free in people who spent time on Algorithms and Data Structures.
    - lifthrasiir 6y ago
      Yes, I haven't said that you should not learn algorithms. The best way to learn that skill is to learn (some) algorithms; DP is particularly worthwhile to learn because it is pretty hard to invent by one's own. But as an interviewer I would spend more time to check the general ability to adapt than the algorithmic knowledge.