The derivative of the Cholesky factor Let $A$ be a symmetric, positive definite $p\times p$ matrix, and let $f(A)$ be its Cholesky factor. That is, $f(A)$ is a lower triangular $p\times p$ matrix such that $A = f(A) f(A)^{\top}$. I am wondering if the derivative
$$
\frac{\mathrm{d}\operatorname{vech}\left(f(A)\right)}{\mathrm{d}\operatorname{vech}\left(A\right)}
$$
is known, where $\operatorname{vech}$ is the half-vectorization function.
(I am conjecturing that it is something like $L^{\top} \left(f(A)\otimes A^{-1}\right) L$, where $L$ is the elimination matrix, but I would need a reference or proof anyway.)
 A: The derivative can be found via implicit differentiation. That is, 
$$
\frac{\mathrm{d}\operatorname{vec}\left(Y\right)}{\mathrm{d}\operatorname{vec}\left(X\right)} = \left(\frac{\mathrm{d} \operatorname{vec}\left(X\right)}{\mathrm{d}\operatorname{vec}\left(Y\right)}\right)^{-1}.$$
It is relatively easy to compute the derivative of $A$ with respect to $f(A)$ since $A = f(A)f(A)^{\top}$. The only trick part is restricting $f(A)$ to be lower triangular.
For general $X$, we have
$$
\frac{\mathrm{d} \operatorname{vec}\left(XX^{\top}\right)}{\mathrm{d} \operatorname{vec}\left(X\right)} = \left(I + K\right)\left(X\otimes I\right),$$
where $K$ is the Commutation Matrix.
Now to get the derivative with respect to the $\operatorname{vech}$ requires use of the chain rule. This gives
$$
\frac{\mathrm{d} \operatorname{vech}\left(XX^{\top}\right)}{\mathrm{d} \operatorname{vech}_{\Delta}\left(X\right)} = L \left(I + K\right)\left(X\otimes I\right) D,$$
where here $L$ is the elimination matrix, and $D$ is the "lower triangular duplication matrix" which has the property that $D \operatorname{vech}\left(M\right) = \operatorname{vec}\left(M\right)$ for lower triangular matrices $M$. The sought derivative is the matrix inverse of the above expression. 
numerical confirmation:
Here is a numerical confirmation in R: (note that the chol function in R is an operator from upper triangular matrices to upper triangular matrices, thus some mucking about with transposes):
require(matrixcalc)
set.seed(2349024)
n <- 6
X <- cov(matrix(rnorm(1000*n),ncol=n))
fnc <- function(X) t(chol(X))

Y <- fnc(X)
d0 <- (diag(1,nrow=n^2) + commutation.matrix(r=n)) %*% (Y %x% diag(1,nrow=n))
L <- elimination.matrix(n)
d1 <- L %*% d0 %*% t(L)
dfin <- solve(d1)

# now compute the approximate derivative
apx.d <- matrix(rep(NA,length(dfin)),nrow=dim(dfin)[1])
my.eps <- 1e-6
low.idx <- which(lower.tri(diag(1,n),diag=TRUE))
for (iii in c(1:length(low.idx))) {
    Xalt <- X
    tweak <- low.idx[iii]
    Xalt[tweak] <- Xalt[tweak] + my.eps
    # "Note that only the upper triangular part of 'x' is used..."
    Yalt <- fnc(t(Xalt))
    dY <- (Yalt - Y) / my.eps
    apx.d[,iii] <- dY[low.idx]
}
apx.error <- apx.d - dfin
max(abs(apx.error))
apx.error

The maximum absolute error I get is 5.606e-07, on the order of the delta in the input variable, 1e-06.
A: I've written a relevant note on arXiv: http://arxiv.org/abs/1602.07527
I included the neat closed form solution pete gives in a comment, and also a messy expression (converted to the notation f = chol(A)):
$$
    \frac{\partial f_{ij}}{\partial A_{kl}} =
    \bigg(\sum_{m>j} f_{im}f_{mk}^{-1} + \tfrac{1}{2}f_{ij}f_{jk}^{-1}\bigg)f_{jl}^{-1}
    +
    (1-\delta_{kl})\bigg(\sum_{m>j} f_{im}f_{ml}^{-1} + \tfrac{1}{2}f_{ij}f_{jl}^{-1}\bigg)f_{jk}^{-1}.
$$
However, if you're interested in differentiating a larger expression, you can do that in $O(N^3)$, without computing all $O(N^4)$ derivatives in $\frac{\partial \mathrm{vech}(f)}{\partial \mathrm{vech}A}$. The note explains different ways to do that.
(pete: if you tell me who you are, I'll add a proper acknowledgement to my note in any future revision.)
A: The following may be of help. If $A=T'T$ (where $T$ is upper triangular), then you can show that (see. Thm. 2.1.9 in Aspects of Multivariate Statistical Theory by R. J. Muirhead):
\begin{equation*}
 (dA) = 2^p\prod_{i=1}^p t_{ii}^{p+1-i}(dT),
\end{equation*}
where using exterior products, we define $(dA) := \bigwedge_{i \le j}^p da_{ij}$; similarly, $(dT)=\bigwedge_{i\le j}^p dt_{ij}$.
Note: You are essentially asking for $dA$ not $(dA)$ --- if all elements of $A$ are distinct, then of course both coincide, otherwise not.
A: Let $T=\{M|M \text{ lower triangular }\},T^+=\{M\in T|\;m_{i,i}>0 \},S^+=\{M|M \text{ SPD }\}$ and $\phi:C=[c_{i,j}]\in T^+\rightarrow h(CC^T)\in h(S^+)\subset T$ where the "half" function $h$ sends to zero the strict upper part and keeps invariant the lower part of a symmetric matrix. $D\phi_C:K\in T\rightarrow H=h(KC^T+CK^T)\in T$. Then $\det(D\phi_C)=2^p\Pi_{j=1}^pc_{j,j}^{2p-2j+1}>0$ and $D\phi_C$ is an isomorphism. Here we are interested with $(D\phi_C)^{-1}:H\in T\rightarrow K\in T$. The linear equation, in the unknown $K$, $H=h(KC^T+CK^T)$ has a unique solution which is $D(vechf)_{vechA}(H)$ and we are done.
