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diff --git a/src/armadillo/include/armadillo_bits/op_logmat_meat.hpp b/src/armadillo/include/armadillo_bits/op_logmat_meat.hpp
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+++ b/src/armadillo/include/armadillo_bits/op_logmat_meat.hpp
@@ -0,0 +1,572 @@
+// 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 op_logmat
+//! @{
+
+
+// Partly based on algorithm 11.9 (inverse scaling and squaring algorithm with Schur decomposition) in:
+// Nicholas J. Higham.
+// Functions of Matrices: Theory and Computation.
+// SIAM, 2008.
+// ISBN 978-0-89871-646-7
+
+
+template<typename T1>
+inline
+void
+op_logmat::apply(Mat< std::complex<typename T1::elem_type> >& out, const mtOp<std::complex<typename T1::elem_type>,T1,op_logmat>& in)
+  {
+  arma_extra_debug_sigprint();
+  
+  const bool status = op_logmat::apply_direct(out, in.m, in.aux_uword_a);
+  
+  if(status == false)
+    {
+    out.soft_reset();
+    arma_stop_runtime_error("logmat(): transformation failed");
+    }
+  }
+
+
+
+template<typename T1>
+inline
+bool
+op_logmat::apply_direct(Mat< std::complex<typename T1::elem_type> >& out, const Op<T1,op_diagmat>& expr, const uword)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename T1::elem_type T;
+  
+  const diagmat_proxy<T1> P(expr.m);
+  
+  arma_debug_check( (P.n_rows != P.n_cols), "logmat(): given matrix must be square sized" );
+  
+  const uword N = P.n_rows;
+  
+  out.zeros(N,N);  // aliasing can't happen as op_logmat is defined as cx_mat = op(mat)
+  
+  for(uword i=0; i<N; ++i)
+    {
+    const T val = P[i];
+    
+    if(val >= T(0))
+      {
+      out.at(i,i) = std::log(val);
+      }
+    else
+      {
+      out.at(i,i) = std::log( std::complex<T>(val) );
+      }
+    }
+  
+  return true;
+  }
+
+
+
+template<typename T1>
+inline
+bool
+op_logmat::apply_direct(Mat< std::complex<typename T1::elem_type> >& out, const Base<typename T1::elem_type,T1>& expr, const uword n_iters)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename T1::elem_type       in_T;
+  typedef typename std::complex<in_T> out_T;
+  
+  const quasi_unwrap<T1> expr_unwrap(expr.get_ref());
+  const Mat<in_T>& A   = expr_unwrap.M;
+  
+  arma_debug_check( (A.is_square() == false), "logmat(): given matrix must be square sized" );
+  
+  if(A.n_elem == 0)
+    {
+    out.reset();
+    return true;
+    }
+  else
+  if(A.n_elem == 1)
+    {
+    out.set_size(1,1);
+    out[0] = std::log( std::complex<in_T>( A[0] ) );
+    return true;
+    }
+  
+  if(A.is_diagmat())
+    {
+    arma_extra_debug_print("op_logmat: detected diagonal matrix");
+    
+    const uword N = A.n_rows;
+    
+    out.zeros(N,N);  // aliasing can't happen as op_logmat is defined as cx_mat = op(mat)
+    
+    for(uword i=0; i<N; ++i)
+      {
+      const in_T val = A.at(i,i);
+      
+      if(val >= in_T(0))
+        {
+        out.at(i,i) = std::log(val);
+        }
+      else
+        {
+        out.at(i,i) = std::log( out_T(val) );
+        }
+      }
+    
+    return true;
+    }
+  
+  const bool try_sympd = arma_config::optimise_sym && sym_helper::guess_sympd(A);
+  
+  if(try_sympd)
+    {
+    arma_extra_debug_print("op_logmat: attempting sympd optimisation");
+    
+    // if matrix A is sympd, all its eigenvalues are positive
+    
+    Col<in_T> eigval;
+    Mat<in_T> eigvec;
+    
+    const bool eig_status = eig_sym_helper(eigval, eigvec, A, 'd', "logmat()");
+    
+    if(eig_status)
+      {
+      // ensure each eigenvalue is > 0
+      
+      const uword N          = eigval.n_elem;
+      const in_T* eigval_mem = eigval.memptr();
+      
+      bool all_pos = true;
+      
+      for(uword i=0; i<N; ++i)  { all_pos = (eigval_mem[i] <= in_T(0)) ? false : all_pos; }
+      
+      if(all_pos)
+        {
+        eigval = log(eigval);
+        
+        out = conv_to< Mat<out_T> >::from( eigvec * diagmat(eigval) * eigvec.t() );
+        
+        return true;
+        }
+      }
+    
+    arma_extra_debug_print("op_logmat: sympd optimisation failed");
+    
+    // fallthrough if eigen decomposition failed or an eigenvalue is <= 0
+    }
+  
+  
+  Mat<out_T> S(A.n_rows, A.n_cols, arma_nozeros_indicator());
+  
+  const  in_T* Amem = A.memptr();
+        out_T* Smem = S.memptr();
+  
+  const uword n_elem = A.n_elem;
+  
+  for(uword i=0; i<n_elem; ++i)
+    {
+    Smem[i] = std::complex<in_T>( Amem[i] );
+    }
+  
+  return op_logmat_cx::apply_common(out, S, n_iters);
+  }
+
+
+
+template<typename T1>
+inline
+void
+op_logmat_cx::apply(Mat<typename T1::elem_type>& out, const Op<T1,op_logmat_cx>& in)
+  {
+  arma_extra_debug_sigprint();
+  
+  const bool status = op_logmat_cx::apply_direct(out, in.m, in.aux_uword_a);
+  
+  if(status == false)
+    {
+    out.soft_reset();
+    arma_stop_runtime_error("logmat(): transformation failed");
+    }
+  }
+
+
+
+template<typename T1>
+inline
+bool
+op_logmat_cx::apply_direct(Mat<typename T1::elem_type>& out, const Op<T1,op_diagmat>& expr, const uword)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename T1::elem_type eT;
+  
+  const diagmat_proxy<T1> P(expr.m);
+  
+  bool status = false;
+  
+  if(P.is_alias(out))
+    {
+    Mat<eT> tmp;
+    
+    status = op_logmat_cx::apply_direct_noalias(tmp, P);
+    
+    out.steal_mem(tmp);
+    }
+  else
+    {
+    status = op_logmat_cx::apply_direct_noalias(out, P);
+    }
+  
+  return status;
+  }
+
+
+
+template<typename T1>
+inline
+bool
+op_logmat_cx::apply_direct_noalias(Mat<typename T1::elem_type>& out, const diagmat_proxy<T1>& P)
+  {
+  arma_extra_debug_sigprint();
+  
+  arma_debug_check( (P.n_rows != P.n_cols), "logmat(): given matrix must be square sized" );
+  
+  const uword N = P.n_rows;
+  
+  out.zeros(N,N);
+  
+  for(uword i=0; i<N; ++i)
+    {
+    out.at(i,i) = std::log(P[i]);
+    }
+  
+  return true;
+  }
+
+
+
+template<typename T1>
+inline
+bool
+op_logmat_cx::apply_direct(Mat<typename T1::elem_type>& out, const Base<typename T1::elem_type,T1>& expr, const uword n_iters)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename T1::pod_type   T;
+  typedef typename T1::elem_type eT;
+  
+  Mat<eT> S = expr.get_ref();
+  
+  arma_debug_check( (S.n_rows != S.n_cols), "logmat(): given matrix must be square sized" );
+  
+  if(S.n_elem == 0)
+    {
+    out.reset();
+    return true;
+    }
+  else
+  if(S.n_elem == 1)
+    {
+    out.set_size(1,1);
+    out[0] = std::log(S[0]);
+    return true;
+    }
+  
+  if(S.is_diagmat())
+    {
+    arma_extra_debug_print("op_logmat_cx: detected diagonal matrix");
+    
+    const uword N = S.n_rows;
+    
+    out.zeros(N,N);  // aliasing can't happen as S is generated
+    
+    for(uword i=0; i<N; ++i)  { out.at(i,i) = std::log( S.at(i,i) ); }
+    
+    return true;
+    }
+  
+  const bool try_sympd = arma_config::optimise_sym && sym_helper::guess_sympd(S);
+  
+  if(try_sympd)
+    {
+    arma_extra_debug_print("op_logmat_cx: attempting sympd optimisation");
+    
+    // if matrix S is sympd, all its eigenvalues are positive
+    
+    Col< T> eigval;
+    Mat<eT> eigvec;
+    
+    const bool eig_status = eig_sym_helper(eigval, eigvec, S, 'd', "logmat()");
+    
+    if(eig_status)
+      {
+      // ensure each eigenvalue is > 0
+      
+      const uword N          = eigval.n_elem;
+      const T*    eigval_mem = eigval.memptr();
+      
+      bool all_pos = true;
+      
+      for(uword i=0; i<N; ++i)  { all_pos = (eigval_mem[i] <= T(0)) ? false : all_pos; }
+      
+      if(all_pos)
+        {
+        eigval = log(eigval);
+        
+        out = eigvec * diagmat(eigval) * eigvec.t();
+        
+        return true;
+        }
+      }
+    
+    arma_extra_debug_print("op_logmat_cx: sympd optimisation failed");
+    
+    // fallthrough if eigen decomposition failed or an eigenvalue is <= 0
+    }
+  
+  return op_logmat_cx::apply_common(out, S, n_iters);
+  }
+
+
+
+template<typename T>
+inline
+bool
+op_logmat_cx::apply_common(Mat< std::complex<T> >& out, Mat< std::complex<T> >& S, const uword n_iters)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename std::complex<T> eT;
+  
+  Mat<eT> U;
+  
+  const bool schur_ok = auxlib::schur(U,S);
+  
+  if(schur_ok == false)  { arma_extra_debug_print("logmat(): schur decomposition failed"); return false; }
+  
+  // NOTE: theta[0] and theta[1] not really used
+  double theta[] = { 1.10e-5, 1.82e-3, 1.6206284795015624e-2, 5.3873532631381171e-2, 1.1352802267628681e-1, 1.8662860613541288e-1, 2.642960831111435e-1 };
+  
+  const uword N = S.n_rows;
+  
+  uword p = 0;
+  uword m = 6;
+  
+  uword iter = 0;
+  
+  while(iter < n_iters)
+    {
+    const T tau = norm( (S - eye< Mat<eT> >(N,N)), 1 );
+    
+    if(tau <= theta[6])
+      {
+      p++;
+      
+      uword j1 = 0;
+      uword j2 = 0;
+      
+      for(uword i=2; i<=6; ++i)  { if( tau      <= theta[i])  { j1 = i; break; } }
+      for(uword i=2; i<=6; ++i)  { if((tau/2.0) <= theta[i])  { j2 = i; break; } }
+      
+      // sanity check, for development purposes only
+      arma_debug_check( (j2 > j1), "internal error: op_logmat::apply_direct(): j2 > j1" );
+      
+      if( ((j1 - j2) <= 1) || (p == 2) )  { m = j1; break; }
+      }
+    
+    const bool sqrtmat_ok = op_sqrtmat_cx::apply_direct(S,S);
+    
+    if(sqrtmat_ok == false)  { arma_extra_debug_print("logmat(): sqrtmat() failed"); return false; }
+    
+    iter++;
+    }
+  
+  if(iter >= n_iters)  { arma_debug_warn_level(2, "logmat(): reached max iterations without full convergence"); }
+  
+  S.diag() -= eT(1);
+  
+  if(m >= 1)
+    {
+    const bool helper_ok = op_logmat_cx::helper(S,m);
+    
+    if(helper_ok == false)  { return false; }
+    }
+  
+  out = U * S * U.t();
+  
+  out *= eT(eop_aux::pow(double(2), double(iter)));
+  
+  return true;
+  }
+
+
+
+template<typename eT>
+inline
+bool
+op_logmat_cx::helper(Mat<eT>& A, const uword m)
+  {
+  arma_extra_debug_sigprint();
+  
+  if(A.internal_has_nonfinite())  { return false; }
+  
+  const vec indices = regspace<vec>(1,m-1);
+  
+  mat tmp(m, m, arma_zeros_indicator());
+  
+  tmp.diag(-1) = indices / sqrt(square(2.0*indices) - 1.0);
+  tmp.diag(+1) = indices / sqrt(square(2.0*indices) - 1.0);
+  
+  vec eigval;
+  mat eigvec;
+  
+  const bool eig_ok = eig_sym_helper(eigval, eigvec, tmp, 'd', "logmat()");
+  
+  if(eig_ok == false)  { arma_extra_debug_print("logmat(): eig_sym() failed"); return false; }
+  
+  const vec nodes   = (eigval + 1.0) / 2.0;
+  const vec weights = square(eigvec.row(0).t());
+  
+  const uword N = A.n_rows;
+  
+  Mat<eT> B(N, N, arma_zeros_indicator());
+  
+  Mat<eT> X;
+  
+  for(uword i=0; i < m; ++i)
+    {
+    // B += weights(i) * solve( (nodes(i)*A + eye< Mat<eT> >(N,N)), A );
+    
+    //const bool solve_ok = solve( X, (nodes(i)*A + eye< Mat<eT> >(N,N)), A, solve_opts::fast );
+    const bool solve_ok = solve( X, trimatu(nodes(i)*A + eye< Mat<eT> >(N,N)), A, solve_opts::no_approx );
+    
+    if(solve_ok == false)  { arma_extra_debug_print("logmat(): solve() failed"); return false; }
+    
+    B += weights(i) * X;
+    }
+  
+  A = B;
+  
+  return true;
+  }
+
+
+
+template<typename T1>
+inline
+void
+op_logmat_sympd::apply(Mat<typename T1::elem_type>& out, const Op<T1,op_logmat_sympd>& in)
+  {
+  arma_extra_debug_sigprint();
+  
+  const bool status = op_logmat_sympd::apply_direct(out, in.m);
+  
+  if(status == false)
+    {
+    out.soft_reset();
+    arma_stop_runtime_error("logmat_sympd(): transformation failed");
+    }
+  }
+
+
+
+template<typename T1>
+inline
+bool
+op_logmat_sympd::apply_direct(Mat<typename T1::elem_type>& out, const Base<typename T1::elem_type,T1>& expr)
+  {
+  arma_extra_debug_sigprint();
+  
+  #if defined(ARMA_USE_LAPACK)
+    {
+    typedef typename T1::pod_type   T;
+    typedef typename T1::elem_type eT;
+    
+    const unwrap<T1>   U(expr.get_ref());
+    const Mat<eT>& X = U.M;
+    
+    arma_debug_check( (X.is_square() == false), "logmat_sympd(): given matrix must be square sized" );
+    
+    if((arma_config::debug) && (arma_config::warn_level > 0) && (is_cx<eT>::yes) && (sym_helper::check_diag_imag(X) == false))
+      {
+      arma_debug_warn_level(1, "logmat_sympd(): imaginary components on diagonal are non-zero");
+      }
+    
+    if(is_op_diagmat<T1>::value || X.is_diagmat())
+      {
+      arma_extra_debug_print("op_logmat_sympd: detected diagonal matrix");
+      
+      out = X;
+      
+      eT* colmem = out.memptr();
+      
+      const uword N = X.n_rows;
+      
+      for(uword i=0; i<N; ++i)
+        {
+        eT& out_ii      = colmem[i];
+         T  out_ii_real = access::tmp_real(out_ii);
+        
+        if(out_ii_real <= T(0))  { return false; }
+        
+        out_ii = std::log(out_ii);
+        
+        colmem += N;
+        }
+      
+      return true;
+      }
+    
+    Col< T> eigval;
+    Mat<eT> eigvec;
+    
+    const bool status = eig_sym_helper(eigval, eigvec, X, 'd', "logmat_sympd()");
+    
+    if(status == false)  { return false; }
+    
+    const uword N          = eigval.n_elem;
+    const T*    eigval_mem = eigval.memptr();
+    
+    bool all_pos = true;
+    
+    for(uword i=0; i<N; ++i)  { all_pos = (eigval_mem[i] <= T(0)) ? false : all_pos; }
+    
+    if(all_pos == false)  { return false; }
+    
+    eigval = log(eigval);
+    
+    out = eigvec * diagmat(eigval) * eigvec.t();
+    
+    return true;
+    }
+  #else
+    {
+    arma_ignore(out);
+    arma_ignore(expr);
+    arma_stop_logic_error("logmat_sympd(): use of LAPACK must be enabled");
+    return false;
+    }
+  #endif
+  }
+
+
+
+//! @}