One good way to think about this is to rank the problems by the probability
that you will solve them.

Of course, you can't compute this exactly, but you can approximate it in
many ways -- from simple averages to complex machine learning models.
Imagine that each problem has a hidden difficulty rating, and each person
has a hidden skill level. The probability that person X solves problem Y is
a function of the skill of X and the difficulty of Y. If you have the full
history of who solved which problems, you can start by assigning a skill
rating to each person and then using those to estimate the difficulty
values of the problems.

And then there are lots of ways of iterating on that model. For example,
you can treat it as a classic supervised learning problem -- given the
history of previous rounds, predict who will solve which problem in the
next round.

One cool outcome of this is that you might discover patterns of problem
types and solvers. Maybe you are really good at dynamic programming, and
for you those problems are easier. Your model will also learn that if
Gennady solves a problem that's not a very useful signal about the
problem's difficulty. :)

On Fri, Apr 27, 2018 at 2:08 AM, Anil M <[email protected]> wrote:

> I tried to use fraction of people solved it, but it brought so many Round
> 2, Round 3, World Finals problems up, I can't make adjustment for Round
> because some problems in Round 2 are in fact easy if have been solved by
> 95% of the participants(especially for small input).  So I stuck with
> absolute value.
>
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