cvxr correction

Author: kisungyou

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: Rdimtools
 Type: Package
 Title: Dimension Reduction and Estimation Methods
-Version: 1.1.3
+Version: 1.1.4
 Authors@R: c(person("Kisung", "You", role = c("aut", "cre"), email = "kisung.you@outlook.com",comment=c(ORCID="0000-0002-8584-459X")), person("Changhee","Suh",role=c("ctb"),email="cs19bc@my.fsu.edu"), person("Dennis","Shung",role=c("ctb"), email="dennis.shung@yale.edu"))
 Description: We provide linear and nonlinear dimension reduction techniques.
 	Intrinsic dimension estimation methods for exploratory analysis are also provided.
@@ -12,7 +12,7 @@ LazyData: true
 Depends: R (>= 3.0.0)
 Imports: ADMM, CVXR (>= 1.0), MASS, RANN, Rcpp (>= 0.12.15), RcppDE, Rdpack, RSpectra, graphics, maotai (>= 0.2.4), mclustcomp, stats, utils
 LinkingTo: Rcpp, RcppArmadillo, RcppDist, maotai
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 RdMacros: Rdpack
 URL: https://www.kisungyou.com/Rdimtools/
 BugReports: https://github.com/kisungyou/Rdimtools/issues

NAMESPACE

@@ -169,11 +169,19 @@ export(est.packing)
 export(est.pcathr)
 export(est.twonn)
 export(oos.linproj)
-import(CVXR)
 import(RcppDE)
 import(Rdpack)
 import(maotai)
 importFrom(ADMM,admm.sdp)
+importFrom(CVXR,Maximize)
+importFrom(CVXR,Minimize)
+importFrom(CVXR,Problem)
+importFrom(CVXR,Variable)
+importFrom(CVXR,matrix_trace)
+importFrom(CVXR,norm1)
+importFrom(CVXR,p_norm)
+importFrom(CVXR,psolve)
+importFrom(CVXR,sum_entries)
 importFrom(MASS,sammon)
 importFrom(RANN,nn2)
 importFrom(RSpectra,eigs)

NEWS.md

@@ -1,3 +1,7 @@
+# Rdimtools 1.1.4
+
+* Change of `CVXR` package-related routines per its API change.
+
 # Rdimtools 1.1.2
 
 * Fixed an error in `do.lapeig()` thanks to Frithjof Kruggel (UC Irvine).

R/Rdimtools-package.R

@@ -8,7 +8,7 @@
 #' @name package-Rdimtools
 #' @aliases Rdimtools-package
 #' @import Rdpack
-#' @import CVXR
+#' @importFrom CVXR matrix_trace Variable Problem psolve sum_entries norm1 p_norm Minimize Maximize
 #' @import RcppDE
 #' @import maotai
 #' @importFrom RANN nn2

R/linear_DSPP.R

@@ -5,8 +5,6 @@
 #' penalty graph. It follows an integration of global within-class structure into manifold learning
 #' under exploiting discriminative nature provided from label information.
 #'
-#'
-#'
 #' @param X an \eqn{(n\times p)} matrix or data frame whose rows are observations.
 #' @param label a length-\eqn{n} vector of data class labels.
 #' @param ndim an integer-valued target dimension.
@@ -186,8 +184,8 @@ dspp_compute_wi <- function(xi, Xi, lambda){
   si  = CVXR::Variable(n)
   obj = (CVXR::p_norm(xi-(Xi%*%si),p=2) + lambda*CVXR::p_norm(si,p=1))
   constr = list(si>=0)
-  prob   = CVXR::Problem(Minimize(obj), constr)
-  result = solve(prob)
+  prob   = CVXR::Problem(CVXR::Minimize(obj), constr)
+  result = CVXR::psolve(prob)
   return(as.vector(result$getValue(si)))
 }
 #' @keywords internal

R/linear_SPP.R

@@ -112,7 +112,7 @@ spp_compute_si <- function(xi, Xi, reltol){
   si  = CVXR::Variable(n)
   obj = CVXR::norm1(si)
   constr = list(CVXR::p_norm(xi-(Xi%*%si),p=2)<=reltol,(sum(si)==1))
-  prob = CVXR::Problem(Minimize(obj), constr)
-  result = solve(prob)
+  prob = CVXR::Problem(CVXR::Minimize(obj), constr)
+  result = CVXR::psolve(prob)
   return(as.vector(result$getValue(si)))
 }

R/nonlinear_MVE.R

@@ -154,7 +154,7 @@ do.mve <- function(X, ndim=2, knn=ceiling(nrow(X)/10), kwidth=1.0,
 mve_single_cvxr <- function(A, B, C){
   N = nrow(B)
   Ktmp = CVXR::Variable(N,N,PSD=TRUE)
-  obj  = Maximize(matrix_trace(Ktmp%*%B))
+  obj  = CVXR::Maximize(CVXR::matrix_trace(Ktmp%*%B))
   constr1 = list(CVXR::sum_entries(Ktmp)==0)
   constr2 = list()
   iter = 1
@@ -167,7 +167,7 @@ mve_single_cvxr <- function(A, B, C){
     }
   }
   prob = CVXR::Problem(obj, c(constr1, constr2))
-  solprob = solve(prob)
+  solprob = CVXR::psolve(prob)
   Knew = as.matrix(solprob$getValue(Ktmp), nrow=N)
   return(Knew)
 }

README.md

@@ -71,7 +71,7 @@ plot(mylap$Y, pch=19, col=lab, xlab="LS1", ylab="LS2", main="Laplacian Score")
 plot(mydfm$Y, pch=19, col=lab, xlab="DM1", ylab="DM2", main="Diffusion Maps")
 ```
 
-<img src="man/figures/README-unnamed-chunk-2-1.png" width="90%" style="display: block; margin: auto;" />
+<img src="man/figures/README-unnamed-chunk-2-1.png" alt="" width="90%" style="display: block; margin: auto;" />
 
 ## Minimal Example : Dimension Estimation
 
@@ -105,7 +105,7 @@ text(x=xtick,  par("usr")[3],
      labels = c("Ustat","correlation","made","mle1","twonn"), pos=1, xpd = TRUE)
 ```
 
-<img src="man/figures/README-unnamed-chunk-3-1.png" width="80%" style="display: block; margin: auto;" />
+<img src="man/figures/README-unnamed-chunk-3-1.png" alt="" width="80%" style="display: block; margin: auto;" />
 
 We can observe that all 5 methods we tested estimated the intrinsic
 dimension around $d=2$. It should be noted that the estimated dimension