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Jul 14, 2018 at 11:41 history closed Sycorax
Peter Flom
Duplicate of Optimization when Cost Function Slow to Evaluate
Jul 13, 2018 at 14:01 review Close votes
Jul 14, 2018 at 11:41
Jul 13, 2018 at 10:45 comment added Fabian Werner I think that the name of the field dedicated to optimizing costly functions is called „Bayesian optimization“ googling for that will lead you (for example) to the R package rBayesianOptimization. They have implemented Gaussian process optimization.
Jul 13, 2018 at 10:43 comment added Fabian Werner In contrast to the answers above I have to say that there are dedicated algorithms in order to execute just the task that you describe. They are usually used in order to optimize the hyperparameters of a neural net. They go like this: approximate the function, optimize the approximated function and then take the minimum of that as the next candidate. One possibility to do this is Gaussian Processes: arimo.com/data-science/2016/…
Jul 13, 2018 at 10:24 answer added stan0 timeline score: 0
Jul 13, 2018 at 9:32 comment added Christian @stan0 yes this is in the working, but this question is particular about the algorithm
Jul 13, 2018 at 9:19 comment added stan0 Is there an option to speedup the function calculation? Like, using a GPU or a cluster, etc.?
Jul 13, 2018 at 7:12 answer added THN timeline score: 5
Jul 13, 2018 at 7:09 comment added deemel Is it possible for you to translate those fixed boundaries for the variables into constraints for the optimization problem? Also, did you have a look at variable-step solvers? They could save you some executions in contrast to fixed-step ones. Besides that I'm not aware of specific solvers for this case, often people just deploy more computation power to solve the issue.
Jul 13, 2018 at 6:50 history asked Christian CC BY-SA 4.0