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diff --git a/src/armadillo/include/armadillo_bits/glue_mvnrnd_meat.hpp b/src/armadillo/include/armadillo_bits/glue_mvnrnd_meat.hpp
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+// SPDX-License-Identifier: Apache-2.0
+// 
+// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
+// Copyright 2008-2016 National ICT Australia (NICTA)
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// ------------------------------------------------------------------------
+
+
+//! \addtogroup glue_mvnrnd
+//! @{
+
+
+// implementation based on:
+// James E. Gentle.
+// Generation of Random Numbers.
+// Computational Statistics, pp. 305-331, 2009.
+// http://dx.doi.org/10.1007/978-0-387-98144-4_7
+
+
+template<typename T1, typename T2>
+inline
+void
+glue_mvnrnd_vec::apply(Mat<typename T1::elem_type>& out, const Glue<T1,T2,glue_mvnrnd_vec>& expr)
+  {
+  arma_extra_debug_sigprint();
+  
+  const bool status = glue_mvnrnd::apply_direct(out, expr.A, expr.B, uword(1));
+  
+  if(status == false)
+    {
+    out.soft_reset();
+    arma_stop_runtime_error("mvnrnd(): given covariance matrix is not symmetric positive semi-definite");
+    }
+  }
+
+
+
+template<typename T1, typename T2>
+inline
+void
+glue_mvnrnd::apply(Mat<typename T1::elem_type>& out, const Glue<T1,T2,glue_mvnrnd>& expr)
+  {
+  arma_extra_debug_sigprint();
+  
+  const bool status = glue_mvnrnd::apply_direct(out, expr.A, expr.B, expr.aux_uword);
+  
+  if(status == false)
+    {
+    out.soft_reset();
+    arma_stop_runtime_error("mvnrnd(): given covariance matrix is not symmetric positive semi-definite");
+    }
+  }
+
+
+
+template<typename T1, typename T2>
+inline
+bool
+glue_mvnrnd::apply_direct(Mat<typename T1::elem_type>& out, const Base<typename T1::elem_type,T1>& M, const Base<typename T1::elem_type,T2>& C, const uword N)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename T1::elem_type eT;
+  
+  const quasi_unwrap<T1> UM(M.get_ref());
+  const quasi_unwrap<T2> UC(C.get_ref());
+  
+  arma_debug_check( (UM.M.is_colvec() == false) && (UM.M.is_empty() == false),  "mvnrnd(): given mean must be a column vector"     );
+  arma_debug_check( (UC.M.is_square() == false),  "mvnrnd(): given covariance matrix must be square sized"                         );
+  arma_debug_check( (UM.M.n_rows != UC.M.n_rows), "mvnrnd(): number of rows in given mean vector and covariance matrix must match" );
+  
+  if( UM.M.is_empty() || UC.M.is_empty() )
+    {
+    out.set_size(0,N);
+    return true;
+    }
+  
+  if((arma_config::debug) && (auxlib::rudimentary_sym_check(UC.M) == false))
+    {
+    arma_debug_warn_level(1, "mvnrnd(): given matrix is not symmetric");
+    }
+  
+  bool status = false;
+  
+  if(UM.is_alias(out) || UC.is_alias(out))
+    {
+    Mat<eT> tmp;
+    
+    status = glue_mvnrnd::apply_noalias(tmp, UM.M, UC.M, N);
+    
+    out.steal_mem(tmp);
+    }
+  else
+    {
+    status = glue_mvnrnd::apply_noalias(out, UM.M, UC.M, N);
+    }
+  
+  return status;
+  }
+
+
+
+template<typename eT>
+inline
+bool
+glue_mvnrnd::apply_noalias(Mat<eT>& out, const Mat<eT>& M, const Mat<eT>& C, const uword N)
+  {
+  arma_extra_debug_sigprint();
+  
+  Mat<eT> D;
+  
+  const bool chol_status = op_chol::apply_direct(D, C, 1);  // '1' means "lower triangular"
+  
+  if(chol_status == false)
+    {
+    // C is not symmetric positive definite, so find approximate square root of C
+    
+    Col<eT> eigval;  // NOTE: eT is constrained to be real (ie. float or double) in fn_mvnrnd.hpp
+    Mat<eT> eigvec;
+    
+    const bool eig_status = eig_sym_helper(eigval, eigvec, C, 'd', "mvnrnd()");
+    
+    if(eig_status == false)  { return false; }
+    
+          eT*   eigval_mem    = eigval.memptr();
+    const uword eigval_n_elem = eigval.n_elem;
+    
+    // since we're doing an approximation, tolerate tiny negative eigenvalues
+    
+    const eT tol = eT(-100) * Datum<eT>::eps * norm(C, "fro");
+    
+    if(arma_isfinite(tol) == false)  { return false; }
+    
+    for(uword i=0; i<eigval_n_elem; ++i)
+      {
+      const eT val = eigval_mem[i];
+      
+      if( (val < tol) || (arma_isfinite(val) == false) )  { return false; }
+      }
+    
+    for(uword i=0; i<eigval_n_elem; ++i)  { if(eigval_mem[i] < eT(0))  { eigval_mem[i] = eT(0); } }
+    
+    Mat<eT> DD = eigvec * diagmat(sqrt(eigval));
+    
+    D.steal_mem(DD);
+    }
+  
+  out = D * randn< Mat<eT> >(M.n_rows, N);
+    
+  if(N == 1)
+    {
+    out += M;
+    }
+  else
+  if(N > 1)
+    {
+    out.each_col() += M;
+    }
+  
+  return true;
+  }
+
+
+
+//! @}