Its very rare imo that computational problems emerge fully formed & ready to be tackled like proofs.
Usually even deciding what the problem is is in part an art, requires an act of narrativization, to shape and form concepts of origin, movement, and destination.
A good problem solver has a very wide range of abstract ideas and concepts and concrete tools they can use to model and explain problem, solution, & destination. Sometimes raw computational intellect can arrive at stunningly good proposals, can see brilliant paths through. But more often, my gut tells me it's about having a breadth of exposure, to different techniques and tools, and being someone who can both see a vast number of ways to tackle a situation, and being able to see tradeoffs in approaches, being able to weight long and short term impacts.
> Its very rare imo that computational problems emerge fully formed & ready to be tackled like proofs.
In my generation, the perfect example is Python's Timsort. It is an modest improvement upon prior sorting algorithms, but it has come to dominate. And, frankly, in terms of computer science history, it was discovered very late. The paper was written in 1993, but the first major, high-impact open source implementation was not written until 2003. Ref: https://en.wikipedia.org/wiki/Timsort
It has been reimplemented in a wide variety of languages today. I look forward to the next iteration: WolfgangSort or FatimaSort or XiaomiSort or whatever.
The a capital example of an exception that prove the rule.
I absolutely value & have huge respect for the deeply computational works that advance us all along!
But this is an exceedingly rare event. Most development is more glue work than advancing computional fundamentals. Very very very little of the industry is paid to work on honing data structures so generally.
Usually even deciding what the problem is is in part an art, requires an act of narrativization, to shape and form concepts of origin, movement, and destination.
A good problem solver has a very wide range of abstract ideas and concepts and concrete tools they can use to model and explain problem, solution, & destination. Sometimes raw computational intellect can arrive at stunningly good proposals, can see brilliant paths through. But more often, my gut tells me it's about having a breadth of exposure, to different techniques and tools, and being someone who can both see a vast number of ways to tackle a situation, and being able to see tradeoffs in approaches, being able to weight long and short term impacts.