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

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

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.