# dikin_walk: Dikin Walk In walkr: Random Walks in the Intersection of Hyperplanes and the N-Simplex

## Description

This function implements the Dikin Walk using the Hessian of the Log barrier function. Note that a \$r\$ of 1 guarantees that the ellipsoid generated won't leave our polytope \$K\$ (see Theorems online)

## Usage

 ```1 2``` ```dikin_walk(A, b, x0 = list(), points, r = 1, thin = 1, burn = 0, chains = 1) ```

## Arguments

 `A` is the lhs of Ax <= b `b` is the rhs of Ax <= b `x0` is the starting point (a list of points) `points` is the number of points we want to sample `r` is the radius of the ellipsoid (1 by default) `thin` every thin-th point is stored `burn` the first burn points are deleted `chains` is the number of chains we run

## Value

a list of chains of the sampled points, each chain being a matrix object with each column as a point

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```A <- rbind(c(1, 0), c(0, 1)) b <- c(1, 1) sampled_points <- dikin_walk(A = A, b = b, points = 10, x0 = list(c(0.5,0.5))) ## Not run: ## note that this Ax <= b is different from Ax=b that the ## user specifies for walkr (see transformation section in vignette) dikin_walk(A = A, b = b, x0, points = 100, r = 1thin = 1, burn = 0, chains = 1) ## End(Not run) ```

walkr documentation built on June 29, 2019, 9:02 a.m.