TaylorModels.jl
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Range bounders for the upper bound
Currently, the linear_dominted_bounder and quadratic_fast_bounder only improves the lower bound, there isn't an implemented way to use these algorithms to improve the upper bound. I have two options in mind to solve this
- Implement a modified version of the algorithms to address the upper bound case.
- Make an auxiliary function whose purpose is to apply the algorithms to the negative of the Taylor Model which gives the desired results.
I think the last one is the best option since is easier to implement, but I don't know if this approach will give unexpected results for some cases.
Thanks for opening this!
I would proceed as you suggest, using the negative of the TaylorModel. One would expect a factor ~2 in the time to improve both bounds. This probably can be optimized by naive paralelization, using e.g. @threads.