## Intro

Mahout has a distributed implementation of QR decomposition for tall thin matrices1.

## Algorithm

For the classic QR decomposition of the form $\mathbf{A}=\mathbf{QR},\mathbf{A}\in\mathbb{R}^{m\times n}$ a distributed version is fairly easily achieved if $\mathbf{A}$ is tall and thin such that $\mathbf{A}^{\top}\mathbf{A}$ fits in memory, i.e. m is large but n < ~5000 Under such circumstances, only $\mathbf{A}$ and $\mathbf{Q}$ are distributed matrices and $\mathbf{A^{\top}A}$ and $\mathbf{R}$ are in-core products. We just compute the in-core version of the Cholesky decomposition in the form of $\mathbf{LL}^{\top}= \mathbf{A}^{\top}\mathbf{A}$. After that we take $\mathbf{R}= \mathbf{L}^{\top}$ and $\mathbf{Q}=\mathbf{A}\left(\mathbf{L}^{\top}\right)^{-1}$. The latter is easily achieved by multiplying each vertical block of $\mathbf{A}$ by $\left(\mathbf{L}^{\top}\right)^{-1}$. (There is no actual matrix inversion happening).

## Implementation

Mahout dqrThin(...) is implemented in the mahout math-scala algebraic optimizer which translates Mahout’s R-like linear algebra operators into a physical plan for both Spark and H2O distributed engines.

def dqrThin[K: ClassTag](A: DrmLike[K], checkRankDeficiency: Boolean = true): (DrmLike[K], Matrix) = {
if (drmA.ncol > 5000)
log.warn("A is too fat. A'A must fit in memory and easily broadcasted.")
implicit val ctx = drmA.context
val AtA = (drmA.t %*% drmA).checkpoint()
val inCoreAtA = AtA.collect
val ch = chol(inCoreAtA)
val inCoreR = (ch.getL cloned) t
if (checkRankDeficiency && !ch.isPositiveDefinite)
throw new IllegalArgumentException("R is rank-deficient.")
val Q = A.mapBlock() {
case (keys, block) => keys -> chol(bcastAtA).solveRight(block)
}
Q -> inCoreR
}


## Usage

The scala dqrThin(...) method can easily be called in any Spark or H2O application built with the math-scala library and the corresponding Spark or H2O engine module as follows:

import org.apache.mahout.math._
import decompositions._
import drm._

val(drmQ, inCoreR) = dqrThin(drma)