When working with a large matrix $M$, we can distinguish between algorithms that require access to arbitrary elements of $M$ and algorithms that only require the ability to multiply by $M$. The latter are called *matrix-free* algorithms; they work with $M$ as a linear operator rather than with its representation as a matrix.

The advantage of matrix-free algorithms is that for specific $M$ there may be much faster ways to compute $Mx$ than by matrix multiplication. As one extreme example, consider the centering operator $x\mapsto x-\bar x$ on a length-$n$ vector, which can be computed in linear time from its definition but would take time quadratic in $n$ if the operator were represented as multiplication by an $n\times n$ matrix. As another, consider multiplying by a diagonal matrix: the matrix can be represented in linear space and the multiplication performed in linear time by just ignoring all the zero off-diagonal elements.

One important application of `bigQF`

is the SKAT test. This involves the eigenvalues of a matrix that is the product of a sparse matrix and a projection matrix. Multiplying by the sparse component is fast for essentially the same reasons that multiplying by a diagonal matrix is fast. Multiplying by the projection component is fast for essentially the same reasons that centering is fast.

Both the stochastic SVD and Lanczos-type algorithms have matrix-free implementations, and the package provides an object-oriented mechanism to use these implementations to compute the distribution of a quadratic form. The `ssvd`

function also accepts these objects.

An object of class `matrix-free`

is a list with the following components

`mult`

Function to multiply by $M$`tmult`

Function to multiply by $M^T$`trace`

numeric, the trace of $M^TM$ (needed for`pQF`

but not`ssvd`

)`ncol`

integer, the number of columns of $M$`nrow`

integer, the number of rows of $M$

As a simple example, suppose `M`

is a sparse matrix stored using the Matrix package. We can define (see `sparse.matrixfree`

)

rval <- list( mult = function(X) M %*% X, tmult = function(X) crossprod(M, X), trace = sum(M^2), ncol = ncol(M), nrow = nrow(M) ) class(rval) <- "matrixfree"

The computations for `trace`

, `ncol`

, and `nrow`

are done at the time the object is constructed. The `mult`

and `tmult`

functions will be efficient because they use the sparse-matrix algorithms in the Matrix package.

The `SKAT.matrixfree`

objects have a more complicated implementation. The matrix is of the form $M=\Pi G W/\sqrt{2}$, where $W$ is a diagonal matrix of weights, $G$ is a sparse genotype matrix, and $\Pi$ is the projection matrix on to the residual space of a linear regression model. Since `M`

is not sparse, it is not sufficient just to use the sparse-matrix code of the previous example. Instead we specify the multiplication function as

function(X) { base::qr.resid(qr, as.matrix(spG %*% X))/sqrt(2) }

where `spG`

is a sparse Matrix object containing $GW$ and `qr`

is the QR decomposition of the design matrix from the linear model. The transpose multiplication function is

function(X) { crossprod(spG, qr.resid(qr, X))/sqrt(2) }

Multiplying by the sparse component takes time proportional to the number of non-zero entries of $GW$ and the projection takes time proportional to $np^2$ where $n$ is the number of observations and $p$ is the number of predictors in the linear model. When the genotype matrix is sparse and $p^2\ll n$, the matrix-free algorithm will be fast.

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