Julia

In this page we briefly discuss the Julia wrapper, which provides most of Optunity’s functionality. For a general overview, we recommend reading the User Guide.

For installation instructions, please refer to Installing Optunity and in particular to Installing Optunity for Julia.

Manual

For Julia the following main functions are available:

minimize(f[, named_args])

Perform minimization of the function f based on the provided named_args parameters.

Parameters:

• f – minimized function (can be an anonymous or any multi-argument function, or a function accepting Dict)
• named_args – various arguments can be provided, such as num_evals, solver_name, but mandatory named arguments are related to the hard constraint on the domain of the function f (for instance x=[-5,5]).

Returns:

vars::Dict, details::Dict

If f is provided in the anonymous or multi-argument form then the order of the provided hard constraints matter. For instance if one defines:

f = (x,y,z) -> (x-1)^2 + (y-2)^2 + (z+3)^4

or:

f(x,y,z) = (x-1)^2 + (y-2)^2 + (z+3)^4

then we strictly require to provide among other named arguments the following hard constraints:

x=[lb,ub], y=[lb,ub], z=[lb,ub]

If one provides a function accepting Dict then the order of the provided hard constraints does not matter.

maximize(f[, named_args])

Perform maximization of the function f based on the provided named_args parameters.

Parameters:

• f – minimized function (can be an anonymous or any multi-argument function, or a function accepting Dict)
• named_args – various arguments can be provided, such as num_evals, solver_name, but mandatory named arguments are related to the hard constraint on the domain of the function f (for instance x=[-5,5]).

Examples

using Base.Test

vars, details = minimize((x,y,z) -> (x-1)^2 + (y-2)^2 + (z+3)^4, x=[-5,5], y=[-5,5], z=[-5,5])

@test_approx_eq_eps vars["x"]  1.0 1.
@test_approx_eq_eps vars["y"]  2.0 1.
@test_approx_eq_eps vars["z"] -3.0 1.

testit(x,y,z) = (x-1)^2 + (y-2)^2 + (z+3)^4

vars, details = minimize(testit, num_evals=10000, solver_name="grid search", x=[-5,5], y=[-5,5], z=[-5,5])

@test_approx_eq_eps vars["x"]  1.0 .2
@test_approx_eq_eps vars["y"]  2.0 .2
@test_approx_eq_eps vars["z"] -3.0 .2

testit_dict(d::Dict) = -(d[:x]-1)^2 - (d[:y]-2)^2 - (d[:z]+3)^4

vars, details = maximize(testit_dict, num_evals=10000, z=[-5,5], y=[-5,5], x=[-5,5])

@test_approx_eq_eps vars["x"]  1.0 .2
@test_approx_eq_eps vars["y"]  2.0 .2
@test_approx_eq_eps vars["z"] -3.0 .2