odatse.solver.analytical module

class odatse.solver.analytical.Solver(info: Info)[source]

Function Solver with pre-defined benchmark functions

Initialize the solver.

Parameters:: info (Info) – Information object containing solver configuration.

__init__(info: Info) → None[source]

Initialize the solver.

Parameters:: info (Info) – Information object containing solver configuration.

odatse.solver.analytical.ackley(xs: ndarray) → float[source]

Ackley’s function in arbitrary dimension

Notes

It has one global minimum f(xs)=0 at xs=[0,0,…,0]. It has many local minima.

odatse.solver.analytical.himmelblau(xs: ndarray) → float[source]

Himmelblau’s function.

Notes

It has four global minima f(xs) = 0 at xs=[3,2], [-2.805118…, 3.131312…], [-3.779310…, -3.2831860], and [3.584428…, -1.848126…].

odatse.solver.analytical.linear_regression_test(xs: ndarray) → float[source]

Negative log likelihood of linear regression with Gaussian noise N(0,sigma)

y = ax + b

trained by xdata = [1, 2, 3, 4, 5, 6] and ydata = [1, 3, 2, 4, 3, 5].

Model parameters (a, b, sigma) are corresponding to xs as the following, a = xs[0], b = xs[1], log(sigma**2) = xs[2]

It has a global minimum f(xs) = 1.005071.. at xs = [0.628571…, 0.8, -0.664976…].

odatse.solver.analytical.quadratics(xs: ndarray) → float[source]

Quadratic (sphere) function.

Notes

It has one global minimum f(xs)=0 at xs = [0,0,…,0].

odatse.solver.analytical.quartics(xs: ndarray) → float[source]

Quartic function with two global minima.

Notes

It has two global minima f(xs)=0 at xs = [1,1,…,1] and [0,0,…,0]. It has one saddle point f(0,0,…,0) = 1.0.

odatse.solver.analytical.rosenbrock(xs: ndarray) → float[source]

Rosenbrock’s function.

Notes

It has one global minimum f(xs) = 0 at xs=[1,1,…,1].