Depending on how complicated the function you're trying to approximate is,
I'd take a look at a simple linear model (using %. ; take a look at the
best fit lab), or a neural network (see e.g. the neural network example on
the J Playground, which classifies 2d data).

Another classifier often used is decision trees and random forests,
implementations of which can be found in e.g. Python's sci-kit learn.

The neural network and others are not necessarily going to be monotonic...

Hope this helps. Otherwise a toy problem would be handy...

Jan-Pieter


On Wed, 5 Apr 2023, 13:23 'Bo Jacoby' via Programming, <
[email protected]> wrote:

>  See https://en.wikipedia.org/wiki/Automated_machine_learning
> Good luck!
> Bo.
>     Den onsdag den 5. april 2023 kl. 12.44.56 CEST skrev Elijah Stone <
> [email protected]>:
>
>  I know not the first thing about linear programming or numerical
> optimisation,
> but I have a problem which I think is related; can anyone point me in the
> right direction?
>
> I have a set of variables x y... (usually not more than two, though there
> might be in some cases), and an oracle which can tell, given a value for
> each
> variable, whether the result is 'good'.  The goal is to construct a model
> for
> the oracle.
>
> The oracle is reasonably nice: it is continuous and monotonic, but
> slightly
> noisy; it might end up being locally non-monotonic, but the model should
> still
> be monotonic.
>
> I expect the oracle can generally be modeled by a simple equation like: 0
> < a
> + (b*x) + (c*y)--then the goal is to find values for a, b, and c;--or
> possibly
> the second-order equivalent of the same; or possibly a piecewise
> composition
> of such equations.  The model should be fast to evaluate, so it shouldn't
> have
> any more terms than are necessary to construct a reasonable approximation.
>
> Any pointers?
>
>   -E
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