Can I use parallel computing when training a gaussian process with separate length scales for predictors with fitgrp?

My code for training the GP looks like this:

gpMdlspeed1 = fitrgp(model1,Speed1,'Basis','constant','FitMethod','exact',... 'PredictMethod','exact','KernelFunction','ardsquaredexponential','KernelParameters',[sigmaM01;sigmaF01],... 'Sigma',sigma01,'Standardize',1,'HyperparameterOptimizationOptions',struct('UseParallel',true), 'Verbose',2 );

But this is not using the parallelpool since my understanding is that hyperparameteroptimizationoptions applies only for bayesianopt optimizer. Is there a way to train this Gaussian process with separate length scales for predictors using parallel computing?

Answer 1

[Gautam Pendse]

Hi Lauri,

Even when there are separate length scales for predictors, these are jointly optimized during training. This optimization proceeds serially by moving from one set of values of the length scales to another set of values such that the log likelihood increases.

To speed up the optimization, you can consider loosening the convergence criterion as in this example:

https://www.mathworks.com/help/stats/fitrgp.html#d120e330919

Hope this helps,

Gautam

[Lauri Nenonen]

Hi Gautam,

Thank you for you answer! It did help indeed.

Best regards, Lauri