# N-D Test Functions C¶

class go_benchmark.CarromTable(dimensions=2)

CarromTable test objective function.

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

Here, represents the number of dimensions and for .

Two-dimensional CarromTable function

Global optimum: for for

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

Here, represents the number of dimensions and for .

Global optimum: for

class go_benchmark.Cigar(dimensions=2)

Cigar test objective function.

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

Here, represents the number of dimensions and for .

Two-dimensional Cigar function

Global optimum: for for

class go_benchmark.Cola(dimensions=17)

Cola test objective function.

This class defines the Cola global optimization problem. The 17-dimensional function computes indirectly the formula by setting :

Where is given by:

And is a symmetric matrix given by:

This function has bounds and for . It has a global minimum of 11.7464.

class go_benchmark.Colville(dimensions=4)

Colville test objective function.

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

Here, represents the number of dimensions and for .

Global optimum: for for

class go_benchmark.Corana(dimensions=4)

Corana test objective function.

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

Where, in this exercise:

Here, represents the number of dimensions and for .

Global optimum: for for

class go_benchmark.CosineMixture(dimensions=2)

Cosine Mixture test objective function.

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

Here, represents the number of dimensions and for .

Two-dimensional Cosine Mixture function

Global optimum: for for

class go_benchmark.CrossInTray(dimensions=2)

Cross-in-Tray test objective function.

This class defines the Cross-in-Tray global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Cross-in-Tray function

Global optimum: for for

class go_benchmark.CrossLegTable(dimensions=2)

Cross-Leg-Table test objective function.

This class defines the Cross-Leg-Table global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Cross-Leg-Table function

Global optimum: . The global minimum is found on the planes and

class go_benchmark.CrownedCross(dimensions=2)

Crowned Cross test objective function.

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

Here, represents the number of dimensions and for .

Two-dimensional Crowned Cross function

Global optimum: . The global minimum is found on the planes and

class go_benchmark.Csendes(dimensions=2)

Csendes test objective function.

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

Here, represents the number of dimensions and for .

Two-dimensional Csendes function

Global optimum: for for

class go_benchmark.Cube(dimensions=2)

Cube test objective function.

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

Here, represents the number of dimensions and for .

Two-dimensional Cube function

Global optimum: for

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