wrapFunctionParallel

the function that shall be evaluated in parallel

Optional, an existing computation cluster

Optional, amount of cores to use for creating a new computation cluster. Default is all cores.

nCores

This is a simple wrapper that turns a function of type <code>y=f(x)</code>, where x is a vector and y is a scalar,
into a function that accepts and returns matrices, as required by <code><a rd-options="" href="/link/spot?package=SPOT&version=2.11.10" data-mini-rdoc="SPOT::spot">spot</a></code>.
While doing so, the wrapper will use the parallel package in order to parallelize the execution of each function
evaluation. This function will create a computation cluster if no cluster is specified and there is no
default cluster setup!

A set of tools for model-based optimization and tuning of
algorithms (hyperparameter tuning respectively hyperparameter optimization). It includes surrogate models, optimizers, and design of experiment
approaches. The main interface is spot, which uses sequentially updated
surrogate models for the purpose of efficient optimization. The main goal is
to ease the burden of objective function evaluations, when a single evaluation
requires a significant amount of resources.

Thomas Bartz-Beielstein

SPOT

Sequential Parameter Optimization Toolbox

Martin Zaefferer

Frederik Rehbach

Margarita Rebolledo

Joerg Stork

Christian Lasarczyk

wrapFunctionParallel function

This is a simple wrapper that turns a function of type <code>y=f(x)</code>, where x is a vector and y is a scalar,
into a function that accepts and returns matrices, as required by <code><a rd-options='' href='spot'>spot</a></code>.
While doing so, the wrapper will use the parallel package in order to parallelize the execution of each function
evaluation. This function will create a computation cluster if no cluster is specified and there is no
default cluster setup!

wrapFunctionParallel: Parallelized Function Evaluation Wrapper

Description

Usage

Arguments

Value