# matrixcomplete: MM Algorithm - Matrix Completion In gettingtothebottom: Learning Optimization and Machine Learning for Statistics

## Description

`matrixcomplete` Function for performing matrix completion using a majorization-minimization algorithm given data matrix X

## Usage

 ```1 2``` ```matrixcomplete(X, Z, omega, lambda, maxiter = 100, tol = 1e-04, liveupdates = TRUE) ```

## Arguments

 `X` Data matrix to be completed `Z` Matrix containing last iterates `omega` Vector containing indices of unobserved entries `lambda` Softhreshold parameter `maxiter` (Optional) Max number of iterations (Default: 100) `tol` (Optional) Tolerance for convergence (Default: 1e-4) `liveupdates` (Optional) If FALSE, no notification will be given upon completion of each iteration. (Default: TRUE)

Jocelyn T. Chi

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```# (Examples not run) # Generate an m-by-n test matrix of rank r # seed <- 12345 # m <- 1000 # n <- 1000 # r <- 5 # T <- testmatrix(m,n,r,seed=seed) # Add some noise to the test matrix # E <- 0.1*matrix(rnorm(m*n),m,n) # A <- T + E # Obtain a vector of unobserved entries # temp <- makeOmega(m,n,percent=0.5) # omega <- temp\$omega # Remove unobserved entries from test matrix # X <- A # X[omega] <- NA # Make initial model matrix Z and find initial lambda # Z <- matrix(0,m,n) # lambda <- init.lambda(X,omega) # Example (Not run) # Sol <- matrixcomplete(X,Z,omega,lambda) ```

gettingtothebottom documentation built on May 29, 2017, 8:28 p.m.