Type:	Package
Title:	Matrix Reconstruction from a Few Entries
Version:	0.2.4
Description:	Matrix reconstruction, also known as matrix completion, is the task of inferring missing entries of a partially observed matrix. This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.
License:	MIT + file LICENSE
Encoding:	UTF-8
Imports:	Rcpp, Rdpack, stats, utils
LinkingTo:	Rcpp, RcppArmadillo
RdMacros:	Rdpack
RoxygenNote:	7.3.2
NeedsCompilation:	yes
Packaged:	2025-09-21 00:02:03 UTC; kyou
Author:	Kisung You [aut, cre]
Maintainer:	Kisung You <kisung.you@outlook.com>
Repository:	CRAN
Date/Publication:	2025-09-22 05:11:01 UTC

OptSpace : an algorithm for matrix reconstruction from a partially revealed set

Description

Let's assume an ideal matrix M with (m\times n) entries with rank r and we are given a partially observed matrix M\_E which contains many missing entries. Matrix reconstruction - or completion - is the task of filling in such entries. OptSpace is an efficient algorithm that reconstructs M from |E|=O(rn) observed elements with relative root mean square error (RMSE)

RMSE \le C(\alpha)\sqrt{nr/|E|}

Usage

OptSpace(A, ropt = NA, niter = 50, tol = 1e-06, showprogress = TRUE)

Arguments

Value

a named list containing

X

an (n \times r) matrix as left singular vectors.

S

an (r \times r) matrix as singular values.

Y

an (m \times r) matrix as right singular vectors.

dist

a vector containing reconstruction errors at each successive iteration.

References

Keshavan RH, Montanari A, Oh S (2010). “Matrix Completion From a Few Entries.” IEEE Transactions on Information Theory, 56(6), 2980–2998. ISSN 0018-9448.

Examples

## Parameter Settings
n = 1000;
m = 100;
r = 3;
tolerance = 1e-7
eps = 10*r*log10(n)

## Generate a matrix with given data
U = matrix(rnorm(n*r),nrow=n)
V = matrix(rnorm(m*r),nrow=m)
Sig = diag(r)
M0 = U%*%Sig%*%t(V)

## Set some entries to be NA with probability eps/sqrt(m*n)
E = 1 - ceiling(matrix(rnorm(n*m),nrow=n) - eps/sqrt(m*n))
M_E = M0
M_E[(E==0)] = NA

## Create a noisy version
noiselevel = 0.1
M_E_noise  = M_E + matrix(rnorm(n*m),nrow=n)*noiselevel

## Use OptSpace for reconstruction
res1 = OptSpace(M_E,tol=tolerance)
res2 = OptSpace(M_E_noise,tol=tolerance)

## Compute errors for both cases using Frobenius norm
err_clean = norm(res1$X%*%res1$S%*%t(res1$Y)-M0,'f')/sqrt(m*n)
err_noise = norm(res2$X%*%res2$S%*%t(res2$Y)-M0,'f')/sqrt(m*n)

## print out the results
m1 = sprintf('RMSE without noise         : %e',err_clean)
m2 = sprintf('RMSE with noise of %.2f    : %e',noiselevel,err_noise)
print(m1)
print(m2)