# GradMod: Gradient method for function minimum approximation. In BarBorGradient: Function Minimum Approximator

## Description

Gradient method for approximating a functions minimum value. The purpose of this method is to compare its result with the BarBor method.

## Usage

 `1` ```Gradmod(exp,eps,G,B,m,x,v,n) ```

## Arguments

 `exp` Expression of the function to be minimized. `eps` Precision of the approximation, recommended value is 10^-10. `G` Inner approximation coefficient, recommended value is 10^-2. `B` Inner approximation coefficient, recommended value is 0.5. `m` Inner steps, recommended value is 20. `x` Starting point of the approximation. `v` A character vector of the functions variables. Exmaple: the two dimension fuction x1*x1+10*x2*x2 needs a c("x1","x2") vector. `n` Maximum setps to make while approximating, if the calculation reaches this number it exits with the current value and point. Recommended to be 10000.

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```test1 = expression(x1*x1+10*x2*x2) eps = 10^-10 G = 10^-2 B = 0.5 m = 20 x = c(3,4) v = c("x1","x2") n = 10000 Gradmod(test1,eps,G,B,m,x,v,n) ```

BarBorGradient documentation built on May 2, 2019, 6:11 a.m.