Main Content

Variables for a Bayesian Optimization

Syntax for Creating Optimization Variables

For each variable in your objective function, create a variable description object using optimizableVariable. Each variable has a unique name and a range of values. The minimal syntax for variable creation is

variable = optimizableVariable(Name,Range)

This function creates a real variable that ranges from the lower bound Range(1) to the upper bound Range(2).

You can specify three types of variables in the Type name-value argument:

  • 'real' — Continuous real values between finite bounds. Give Range as the two-element vector [lower upper], which represent the lower and upper bounds.

  • 'integer' — Integer values between finite bounds, similar to 'real'.

  • 'categorical' — Cell array of names of possible values, such as {'red','green','blue'}, that you specify in the Range argument.

For 'real' or 'integer' variables, you can specify that bayesopt searches in a log-scaled space by setting the Transform name-value argument to 'log'. For this transformation, ensure that the lower bound in the Range is strictly positive for 'real' and nonnegative for 'integer'.

Include variables for bayesopt as a vector in the second argument.

results = bayesopt(fun,[xvar,ivar,rvar])

To exclude a variable from an optimization, set Optimize to false, either in the name-value argument of optimizableVariable, or by dot notation:

xvar.Optimize = false;


  • There are two names associated with an optimizableVariable:

    • The MATLAB® workspace variable name

    • The name of the variable in the optimization

    For example,

    xvar = optimizableVariable('spacevar',[1,100]);

    xvar is the MATLAB workspace variable, and 'spacevar' is the variable in the optimization.

    Use these names as follows:

    • Use xvar as an element in the vector of variables you pass to bayesopt. For example,

      results = bayesopt(fun,[xvar,tvar])
    • Use 'spacevar' as the name of the variable in the optimization. For example, in an objective function,

      function objective = mysvmfun(x,cdata,grp)
      SVMModel = fitcsvm(cdata,grp,'KernelFunction','rbf',...
      objective = kfoldLoss(crossval(SVMModel));

Variables for Optimization Examples

Real variable from 0 to 1:

var1 = optimizableVariable('xvar',[0 1])
var1 = 
  optimizableVariable with properties:

         Name: 'xvar'
        Range: [0 1]
         Type: 'real'
    Transform: 'none'
     Optimize: 1

Integer variable from 0 to 1000 on a log scale:

var2 = optimizableVariable('ivar',[0 1000],'Type','integer','Transform','log')
var2 = 
  optimizableVariable with properties:

         Name: 'ivar'
        Range: [0 1000]
         Type: 'integer'
    Transform: 'log'
     Optimize: 1

Categorical variable of rainbow colors:

var3 = optimizableVariable('rvar',{'r' 'o' 'y' 'g' 'b' 'i' 'v'},'Type','categorical')
var3 = 
  optimizableVariable with properties:

         Name: 'rvar'
        Range: {'r'  'o'  'y'  'g'  'b'  'i'  'v'}
         Type: 'categorical'
    Transform: 'none'
     Optimize: 1

Related Topics