Nothing
## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval=FALSE---------------------------------------------------------
# # Initialize problem size and densities
# # You can play around with these parameters
# M=100 #number of rows of sampled matrix
# N=200 #number of columns of sampled matrix
# m=50 #number of rows of dense submatrix
# n=40 #number of columns of dense submatrix
# p=0.25 # noise density
# q=0.85 #in-group density
#
# #Make binary matrix with mn-submatrix
# random<-plantedsubmatrix(M=M, N=N,m=m,n=n,p=p,q=q)
## ---- eval=FALSE---------------------------------------------------------
#
# # Plot sampled G and matrix representations.
# image(random$sampled_matrix, useRaster=TRUE, axes=FALSE, main = "Matrix A")
# image(random$dense_submatrix, useRaster=TRUE, axes=FALSE, main = "Matrix X0")
# image(random$disagreements, useRaster=TRUE, axes=FALSE, main = "Matrix Y0")
## ---- eval=FALSE---------------------------------------------------------
# #Call ADMM solver
# admm<-densub(G=random$sampled_matrix, m=m, n=n, tau = 0.35, gamma = 6/(sqrt(m*n)*(q-p)), opt_tol = 1.0e-4,maxiter=500, quiet = TRUE)
#
#
# #Plot results
# image(admm$X, useRaster=TRUE, axes=FALSE, main = "Matrix X")
# image(admm$Y, useRaster=TRUE, axes=FALSE, main = "Matrix Y")
#
#
## ----jazz, eval=FALSE----------------------------------------------------
# #Load dataset
# load(file="JAZZ.RData")
#
# #Initialize problem size and densities
# G=new #define matrix G equivalent to JAZZ dataset
# m=100 #clique size or the number of rows of the dense submatrix
# n=100 #clique size of the number of columns of the dense sumbatrix
# tau=0.85 #regularization parameter
# opt_tol=1.0e-2 #optimal tolerance
# verbose=1
# maxiter=2000 #number of iterations
# gamma=8/n #regularization parameter
#
#
#
# #call ADMM solver
# admm <- densub(G = G, m = m, n = n, tau = tau, gamma = gamma, opt_tol = opt_tol, maxiter=maxiter, quiet = TRUE)
# # Planted solution X0.
# X0=matrix(0L, nrow=198, ncol=198) #construct rank-one matrix X0
# X0[1:100,1:100]=matrix(1L, nrow=100, ncol=100)#define dense block
#
# # Planted solution Y0.
# Y0=matrix(0L, nrow=198, ncol=198) #construct matrix for counting disagreements between G and X0
# Y0[1:100,1:100]=matrix(1L,nrow=100,ncol=1000)-G[1:100,1:100]
#
# #Check primal and dual residuals.
# C=admm$X-X0
# a=norm(C, "F") #Frobenius norm of matrix C
# b=norm(X0,"F") #Frobenius norm of matrix X0
# recovery = matrix(0L,nrow=1, ncol=1)#create recovery matrix
#
# if (a/b^2<opt_tol){
# recovery=recovery+1
# } else {
# recovery=0 #Recovery condition
# }
#
#
#
#
#
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