# Equivalent Algorithm with Sharman Morrison inversion

I am trying to invert a matrix using Woodbury identity. The inversion using Cholesky decomposition has the following pseudo-code:

For $$t=1,2,...$$

$$(1)\;\; \text{Read}\;x_t\in\mathbb{R}^n$$

$$(2)\;\;D_{t-1}=diag(|\theta_t^1|,...,|\theta_t^n|)$$

$$(3)\;\;A_t=A_t+x_tx_t'$$

$$(4)\;\;A_{t}^{-1}=\sqrt{D_{{t-1}}}\left(a\mathbf{I}+\sqrt{D_{{t-1}}}A_t\sqrt{D_{{t-1}}}\right)^{-1}\sqrt{D_{{t-1}}}$$

$$(5)\;\;\text{Read}\;y_t\in\mathbb{R}$$

$$(6)\;\;b=b+y_tx_t$$

$$(7)\;\;\theta_t=A_{t}^{-1}b$$

End For

The well known application of Sherman-Morrison on Recursive Least Squares is as follows:

$$A_t^{-1}=(aI+x_tx_t')^{-1}=A_{t-1}^{-1} - \frac{(A_{t-1}^{-1}x_t)(A_{t-1}^{-1}x_t)'}{1+x_t'A_{t-1}^{-1}x_t}$$

where $$A_0^{-1}=\frac{1}{a}I$$ and we can set $$A_t = A_{t-1}+\sum_{t=1}^Tx_tx_t'$$, which will lead to time complexity of $$O(n^2)$$.

The above technique is mentioned here. The two implementation in $$\texttt{R}$$ are as follows:

X <-matrix(runif(1000),20,10)
Y<-rnorm(20)
a<- 0.1

Cholsky<-function(X,Y,a){
X <- as.matrix(X)
Y <- as.matrix(Y)
T <- nrow(X)
N <- ncol(X)
aI<- diag(a,N)
bt<- matrix(0,ncol=1,nrow=N)
for (t in 1:T){
xt<-X[t,]
At <- aI + (xt %*% t(xt))
InvA<-chol2inv(chol(At))
bt <- bt + (Y[t] * xt)
theta<- InvA %*% bt
}
return(theta)
}
Cholsky(X,Y,a)

Morrison<-function(X,Y,a){
X <- as.matrix(X)
Y <- as.matrix(Y)
T <- nrow(X)
N <- ncol(X)
At<-diag(1/a,N)
bt<- matrix(0,ncol=1,nrow=N)
for (t in 1:T){
xt<-X[t,]
At <- At + (xt %*% t(xt))
InvA <- At - ((t(xt%*%At)%*%(as.matrix(xt%*%At)))
/as.numeric(xt%*%At%*%xt+1))
bt <- bt + (Y[t] * xt)
theta<- InvA %*% bt
}
return(theta)
}
Morrison(X,Y,a)


They don't give the same result. So, perhaps I should not expect the implementations to be equivalent.

I was wondering if I could invert the following (for the above case) more efficiently:

$$A_t^{-1}=\left(D_{t-1}^{\frac{1}{2}}A_tD_{t-1}^{\frac{1}{2}}+aI\right)^{-1}$$

where $$A_t=A_{t-1}+x_tx_t'$$ and $$A_0=\mathbf{0}$$.

Essentially, I want to invert:

$$M_t=\left(a\mathbf{I}+\sqrt{D_{{t-1}}}x_tx_t'\sqrt{D_{{t-1}}}\right)$$

I say:

$$M_{t}^{-1}=M_{t-1}^{-1}-\frac{(M_{t-1}D_{t-1}^{\frac{1}{2}}x_t)(M_{t-1}D_{t-1}^{\frac{1}{2}}x_t)'}{1+x_t'D_{t-1}^{\frac{1}{2}}M_{t-1}D_{t-1}^{\frac{1}{2}}x_t}$$

where $$D_0=diag(\mathbf{1}$$). So, $$M^{-1}_0=\frac{1}{a}I$$

RLS identity given above $$(aI+u_tv_t)^{-1}$$ uses $$u=A_{t-1}^{-1}x_t,v_t=u_t'$$, I am using $$u=M_{t-1}^{-1}D_{t-1}^{\frac{1}{2}}x_t,v_t=u_t'$$

One may write the implementations in $$\texttt{R}$$ as follows:

X <-matrix(runif(1000),20,10)
Y<-rnorm(20)
a<- 0.1

#Cholesky implementation
X <- as.matrix(X)
Y <- as.matrix(Y)
T <- nrow(X)
N <- ncol(X)
bt<- matrix(0,ncol=1,nrow=N)
At<- diag(0,N)
I<- diag(a,N);Mt<-diag(1/a,N)
theta0<- rep(1,N)
for (t in 1:2){
xt<-X[t,]
Dt <- diag(sqrt(abs(as.numeric(theta0))))
At <- At + (xt %*% t(xt))
Mt <-  I + (Dt%*%At%*%Dt)
InvA <- chol2inv(chol( Mt ))
AAt<- Dt %*%InvA%*% Dt
bt <- bt + (Y[t] * xt)
theta0 <- AAt %*% bt
print(theta0)
}


Above is the correct implementation of the pseudo code. If I swap Mt and InvA in the above code by the following lines, I don't get the same answer.

Mt <-  Mt + (Dt%*%At%*%Dt)
InvA<- Mt - ((t(xt%*%Dt%*%Mt)%*%(as.matrix(xt%*%Dt%*%Mt)))
/as.numeric(xt%*%Dt%*%Mt%*%t(as.matrix(xt%*%Dt))+1))


Why is that?

• Also posted on Math StackExchange one day earlier. Please do not post the same question on multiple sites. Each community should have an honest shot at answering without anybody's time being wasted. If you don't get a satisfying answer after a week or so, you may flag to request migration. – Apass.Jack Dec 26 '18 at 23:58
• I will remove this from here and migrate it after a week. Many thanks. – Waqas Dec 27 '18 at 0:01
• It's been a month and I did not get an answer at Math StcackExchange. So, I hope its alright to ask computer science community. I don't have enough reputation to migrate. – Waqas Feb 7 at 10:12
• "If I swap the following lines" where? The second one isn't in the code at all. – Peter Taylor Feb 7 at 11:42
• So, replace Mt and InvA from second one to the first one. – Waqas Feb 7 at 11:50