maxIterations_

Maximum number of iterations.

Are these iterations through the TimeSeries data or future iterations of how many results will be returned.

maxIterations is used to tell the optimization method, regardless of the method, the maximum number of times that it should iterate. If the method does not determine success (however that is defined by the method), then it should stop after this many iterations. In general, it should mean the maximum number of times that the objective function should be evaluated, although this is not necessarily true for some methods, e.g. swarm methods for which each iteration evaluates N times the objective.

maxStationaryStateIterations_

Maximun number of iterations in stationary state.

What is the meaning of stationary state.

Stationary state refers here to the best answer until now. Meaning that if the optimization method does not find a better answer in the next maxStationaryStateIterations, it should stop looking.

rootEpsilon_

root, function and gradient epsilons

I assume this is the value from which the process starts searching.

No, all three epsilons relate to the number accuracy that the method should concern itself with. rootEpsilon refers to the number accuracy in the parameter space, e.g. when comparing whether two points are the same, they would be so considered up to the given accuracy. For most applications, the full machine accuracy (e.g. ca. 10^-15 for doubles) is not needed, so giving a wider tolerance will allow the method to stop earlier.

functionEpsilon_

What's this?

This epsilon refers to the accuracy at the objective/target space. If the method compares whether a new solution is as good as the best current solution, then it only cares up to the accuracy given here.

gradientNormEpsilon_

What's this?

Ditto, but for the gradient.

It is the obligation of the optimization method to pay attention to these parameters, but some might not be relevant. If the method does not use a gradient, then gradientNormEpsilon_ will of course not be important. There might be other parameters that might be ignored by the method, e.g. like maxStationaryStateIterations_, which for a local optimization method is also not important.

cheers,

Andres

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