Simple and often used test function defined in higher dimensions and with
analytical gradients, especially suited for performance tests. Analytical
gradients, where existing, are provided with the `gr`

prefix.
The dimension is determined by the length of the input vector.

1 2 3 4 5 6 7 8 9 10 11 | ```
fnRosenbrock(x)
grRosenbrock(x)
fnRastrigin(x)
grRastrigin(x)
fnNesterov(x)
grNesterov(x)
fnNesterov1(x)
fnHald(x)
grHald(x)
fnShor(x)
grShor(x)
``` |

`x` |
numeric vector of a certain length. |

**Rosenbrock** – Rosenbrock's famous valley function from 1960. It can
also be regarded as a least-squares problem:

*∑_{i=1}^{n-1} (1-x_i)^2 + 100 (x_{i+1}-x_i^2)^2*

No. of Vars.: | n >= 2 |

Bounds: | -5.12 <= xi <= 5.12 |

Local minima: | at f(-1, 1, ..., 1) for n >= 4 |

Minimum: | 0.0 |

Solution: | xi = 1, i = 1:n |

**Nesterov** – Nesterov's smooth adaptation of Rosenbrock, based on the
idea of Chebyshev polynomials. This function is even more difficult to
optimize than Rosenbrock's:

*(x_1 - 1)^2 / 4 + ∑_{i=1}^{n-1} (1 + x_{i+1} - 2 x_i^2)*

No. of Vars.: | n >= 2 |

Bounds: | -5.12 <= xi <= 5.12 |

Local minima: ? | |

Minimum: | 0.0 |

Solution: | xi = 1, i = 1:n |

**Rastrigin** – Rastrigin's function is a famous, non-convex example from 1989 for global optimization. It is a typical example of a multimodal function with many local minima:

*10 n + ∑_1^n (x_i^2 - 10 \cos(2 π x_i))*

No. of Vars.: | n >= 2 |

Bounds: | -5.12 <= xi <= 5.12 |

Local minima: | many |

Minimum: | 0.0 |

Solution: | xi = 0, i = 1:n |

**Hald** – Hald's function is a typical example of a non-smooth test
function, from Hald and Madsen in 1981.

*\max_{1 ≤ i ≤ n} \frac{x_1 + x_2 t_i}{1 + x_3 t_i + x_4 t_i^2 + x_5 t_i^3} - \exp(t_i)*

where *t_i = -1 + (i - 1)/10* for *1 ≤ i ≤ 21*.

No. of Vars.: | n =5 |

Bounds: | -1 <= xi <= 1 |

Local minima: | ? |

Minimum: | 0.0001223713 |

Solution: | (0.99987763, 0.25358844, -0.74660757, 0.24520150, -0.03749029) |

**Shor** – Shor's function is another typical example of a non-smooth test
function, a benchmark for Shor's R-algorithm.

Returns the values of the test function resp. its gradient at that point. If an analytical gradient is not available, a function computing the gradient numerically will be provided.

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