Comments (5)
I think optim
is actually kind of okay (aside from warnings resulting from invalid rho
estimates).
Currently I'm thinking a method kind of like this might work well:
- Specify constraints as a list of formulae. Something like
c(x) ~ equal_to(0)
.- Could specify the penalty/barrier method here. Like
gte_zero('log_barrier')
or something.
- Could specify the penalty/barrier method here. Like
- Convert formulae to a set of penalty functions.
- Create a new objective function as sum of original and the penalty functions.
I guess there could be some difficulties/annoyances with the penalty weights. These would probably need to be customisable either through some control
type function argument (or varargs with!!!
) or in the constraint/formula specification (like 0.1 * c(x) ~ equal_to(0)
).
Sequential solutions with increasing penalty weights should also be considered. In this case each constraint could return a function that takes a weight and returns a penalty function (or just takes weight as an argument - maybe this is more idiomatic in R?).
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Another thought: adding constraints to team parameters could be annoying.
One potential solution would be to look for parameters named by off___*
or def___*
. This would at least work for the default method, even if it is a little inelegant.
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The easiest way to make model fitting faster is probably to compute the gradient of the the objective function, rather than relying on gradient-free methods...
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Potentially a dumb idea, but given it's just matrices, could try any use the rstudio/keras api??
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Closing for now since the kind of optimisation improvements I want would be their own project...
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Related Issues (20)
- Return tibbles
- Use rsample > modelr HOT 1
- Use tidyeval > lazyeval HOT 1
- Create a package site HOT 1
- Dixon-Robinson fit
- predict.dixoncoles requires unnecessary home/away goals columns
- Informative error message when predicting with different factor levels
- Warnings after fit HOT 2
- Dixon-Robinson predict method HOT 1
- Broom model methods
- Include example goal-times data
- Correct old blogs and documentation
- Speed up dixoncoles tests
- Dixoncoles really really slow HOT 6
- DixonColes error message HOT 5
- Can't create table of scoreline probabilities without dixoncoles class object HOT 3
- error with broom HOT 8
- Use Github Actions
- Unplayed games - factor issues HOT 8
- Non-list contrasts argument ignored while modeling HOT 1
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