dual-package | R Documentation |

Automatic differentiation is achieved by using dual numbers without providing hand-coded gradient functions. The output value of a mathematical function is returned with the values of its exact first derivative (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind (2018) https://jmlr.org/papers/volume18/17-468/17-468.pdf.

Package: | dual |

Type: | Package |

Version: | 0.0.5 |

Date: | 2023-10-02 |

License: | GPL-3 |

For a complete list of exported functions, use `library(help = "dual")`

.

Luca Sartore drwolf85@gmail.com

Maintainer: Luca Sartore drwolf85@gmail.com

Baydin, A. G., Pearlmutter, B. A., Radul, A. A., & Siskind, J. M. (2018). Automatic differentiation in machine learning: a survey. *Journal of Marchine Learning Research*, **18**, 1-43.

Cheng, H. H. (1994). Programming with dual numbers and its applications in mechanisms design. *Engineering with Computers*, **10**(4), 212-229.

```
library(dual)
# Initilizing variables of the function
x <- dual(f = 1.5, grad = c(1, 0, 0))
y <- dual(f = 0.5, grad = c(0, 1, 0))
z <- dual(f = 1.0, grad = c(0, 0, 1))
# Computing the function and its gradient
exp(z - x) * sin(x)^y / x
# General use for computations with dual numbers
a <- dual(1.1, grad = c(1.2, 2.3, 3.4, 4.5, 5.6))
0.5 * a^2 - 0.1
# Johann Heinrich Lambert's W-function
lambertW <- function(x) {
w0 <- 1
w1 <- w0 - (w0*exp(w0)-x)/((w0+1)*exp(w0)-(w0+2)*(w0*exp(w0)-x)/(2*w0+2))
while(abs(w1-w0) > 1e-15) {
w0 <- w1
w1 <- w0 - (w0*exp(w0)-x)/((w0+1)*exp(w0)-(w0+2)*(w0*exp(w0)-x)/(2*w0+2))
}
return(w1)
}
lambertW(dual(1, 1))
```

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