Second order polynomial tutorial

The square polynomial model can be expressed by: $y = Xβ + ϵ$ Where X is the matrix of the linear model augmented by adding 2d columns, containing pair by pair product of variables and variables squared.

using Surrogates
using Plots
default()

Sampling

f = x -> 3*sin(x) + 10/x
lb = 3.0
ub = 6.0
n = 10
x = sample(n,lb,ub,LowDiscrepancySample(2))
y = f.(x)
scatter(x, y, label="Sampled points", xlims=(lower_bound, upper_bound))
plot!(f, label="True function", xlims=(lower_bound, upper_bound))

Building the surrogate

sec = SecondOrderPolynomialSurrogate(x, y, lb, ub)
plot(x, y, seriestype=:scatter, label="Sampled points", xlims=(lb, ub))
plot!(f, label="True function",  xlims=(lb, ub))
plot!(sec, label="Surrogate function",  xlims=(lb, ub))

Optimizing

@show surrogate_optimize(f, SRBF(), lb, ub, sec, SobolSample())
scatter(x, y, label="Sampled points")
plot!(f, label="True function",  xlims=(lb, ub))
plot!(sec, label="Surrogate function",  xlims=(lb, ub))

The optimization method successfully found the minima.