# optR: Optimization & predictive modelling Toolsets In optR: Optimization Toolbox for Solving Linear Systems

## Description

optR function for solving linear systems using numerical approaches. Current toolbox supports Gauss Elimination, LU decomposition, Conjugate Gradiant Decent and Gauss-Sideal methods for solving the system of form AX=b For optimization using numerical methods cgm method performed faster in comparision with gaussseidel. For decomposition LU is utilized for multiple responses to enhance the speed of computation.

## Usage

 `1` ```optR(x, ...) ```

## Arguments

 `x` : Input matrix `...` : S3 method

## Value

optR : Return optR class

PKS Prakash

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```# Solving equation Ax=b A<-matrix(c(6,-4,1, -4,6,-4,1,-4,6), nrow=3,ncol=3, byrow = TRUE) b<-matrix(c(-14,36, 6), nrow=3,ncol=1,byrow=TRUE) Z<-optR(A, b, method="gauss") # Solve Linear model using Gauss Elimination # Solve Linear model using LU decomposition (Supports Multi-response) Z<-optR(A, b, method="LU") # Solve the matrix using Gauss Elimination (1, -1, 2) A<-matrix(c(2,-2,6, -2,4,3,-1,8,4), nrow=3,ncol=3, byrow = TRUE) b<-matrix(c(16,0, -1), nrow=3,ncol=1,byrow=TRUE) Z<-optR(A, b, method="gauss") # Solve Linear model using Gauss Elimination require(utils) set.seed(129) n <- 10 ; p <- 4 X <- matrix(rnorm(n * p), n, p) # no intercept! y <- rnorm(n) Z<-optR(X, y, method="cgm") ```

optR documentation built on May 29, 2017, 2:10 p.m.