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

optR(x, ...)

Arguments

x	: Input matrix
...	: S3 method

Value

optR : Return optR class

Author(s)

PKS Prakash

Examples

# 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 1, 2019, 10:32 p.m.