# N-D Test Functions W¶

class go_benchmark.Watson(dimensions=6)

Watson test objective function.

This class defines the Watson global optimization problem. This is a unimodal minimization problem defined as follows:

Where, in this exercise, .

Here, represents the number of dimensions and for .

Global optimum: for

class go_benchmark.Wavy(dimensions=2)

W / Wavy test objective function.

This class defines the W / Wavy global optimization problem. This is a multimodal minimization problem defined as follows:

Where, in this exercise, . The number of local minima is and for odd and even respectively.

Here, represents the number of dimensions and for .

Two-dimensional W / Wavy function

Global optimum: for for

Wayburn and Seader 1 test objective function.

This class defines the Wayburn and Seader 1 global optimization problem. This is a unimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Wayburn and Seader 1 function

Global optimum: for

Wayburn and Seader 2 test objective function.

This class defines the Wayburn and Seader 2 global optimization problem. This is a unimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Wayburn and Seader 2 function

Global optimum: for

class go_benchmark.Weierstrass(dimensions=2)

Weierstrass test objective function.

This class defines the Weierstrass global optimization problem. This is a multimodal minimization problem defined as follows:

Where, in this exercise, , and .

Here, represents the number of dimensions and for .

Two-dimensional Weierstrass function

Global optimum: for for

class go_benchmark.Whitley(dimensions=2)

Whitley test objective function.

This class defines the Whitley global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Whitley function

Global optimum: for for

class go_benchmark.Wolfe(dimensions=3)

Wolfe test objective function.

This class defines the Wolfe global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Global optimum: for for

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