# gsisvdsolve: Internal function: Solves singular and non square equation... In compositions: Compositional Data Analysis

## Description

Based on the singular value decomposition, a singular equation system ax=b is solved.

## Usage

 `1` ```gsi.svdsolve(a,b,...,cond=1E-10) ```

## Arguments

 `a` the matrix of ax=b (a.k.a. left-hand side matrix) `b` the vector or matrix b of ax=b (a.k.a right-hand side, independent element) `cond` the smallest-acceptable condition of the matrix. Smaller singular values are truncate `...` additional arguments to svd

## Value

The "smallest" vector or matrix solving this system with minimal joint error among all vectors.

## Note

Do not use gsi.* functions directly since they are internal functions of the package

## Author(s)

K.Gerald v.d. Boogaart http://www.stat.boogaart.de

## Examples

 ```1 2 3 4 5 6``` ```#A <- matrix(c(0,1,0,0,0,0),ncol=2) #b <- diag(3) #erg <- gsi.svdsolve(A,b) #erg #A %*% erg #diag(c(0,1,0)) # richtig ```

### Example output

```Loading required package: tensorA

Attaching package: 'tensorA'

The following object is masked from 'package:base':

norm

Welcome to compositions, a package for compositional data analysis.
Find an intro with "? compositions"

Attaching package: 'compositions'

The following objects are masked from 'package:stats':

cor, cov, dist, var

The following objects are masked from 'package:base':

%*%, scale, scale.default
```

compositions documentation built on Jan. 5, 2022, 5:09 p.m.