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Commita438f0a

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passing R CMD check
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‎R/rTensor_Decomp.R‎

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@@ -525,7 +525,7 @@ t_svd_reconstruct <- function(L){
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#'@param ranks an integer vector of length \code{K} specifying the modes sizes for the output core tensor \code{Z}
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#'@param core_nonneg constrain core tensor \code{Z} to be nonnegative
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#'@param tol relative Frobenius norm error tolerance
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#'@param hosvd If TRUE, apply High Order SVD to improve initial U and Z.
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#'@param hosvd If TRUE, apply High Order SVD to improve initial U and Z
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#'@param max_iter maximum number of iterations if error stays above \code{tol}
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#'@param max_time max running time
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#'@param lambda \code{K+1} vector of sparsity regularizer coefficients for the factor matrices and the core tensor
@@ -552,7 +552,7 @@ t_svd_reconstruct <- function(L){
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#'
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#'@details The function uses the alternating proximal gradient method to solve the following optimization problem:
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#' \deqn{\min 0.5 \|tnsr - Z \times_1 U_1 \ldots \times_K U_K \|_{F^2} +
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#' \sum_{n=1}^{K} \lambda_n \|U_n\|_1 + \lambda_{K+1} \|Z\|_1, \;\text{where}\; Z \geq 0, \, U_i \geq 0.}
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#' \sum_{n=1}^{K} \lambda_n \|U_n\|_1 + \lambda_{K+1} \|Z\|_1, \;\textit{where}\; Z \geq 0, \, U_i \geq 0.}
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#' If \code{core_nonneg} is \code{FALSE}, core tensor \code{Z} is allowed to have negative
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#' elements and \eqn{z_{i,j}=max(0,z_{i,j}-\lambda_{K+1}/L_{K+1})} rule is replaced by \eqn{z_{i,j}=sign(z_{i,j})max(0,|z_{i,j}|-\lambda_{K+1}/L_{K+1})}.
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#' The method stops if either the relative improvement of the error is below the tolerance \code{tol} for 3 consequitive iterations or

‎man/tucker.nonneg.Rd‎

Lines changed: 2 additions & 2 deletions
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