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diff --git a/src/armadillo/include/armadillo_bits/newarp_SymEigsSolver_meat.hpp b/src/armadillo/include/armadillo_bits/newarp_SymEigsSolver_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.
+// ------------------------------------------------------------------------
+
+
+namespace newarp
+{
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+void
+SymEigsSolver<eT, SelectionRule, OpType>::fill_rand(eT* dest, const uword N, const uword seed_val)
+  {
+  arma_extra_debug_sigprint();
+  
+  typedef typename std::mt19937_64::result_type seed_type;
+  
+  local_rng.seed( seed_type(seed_val) );
+  
+  std::uniform_real_distribution<double> dist(-1.0, +1.0);
+  
+  for(uword i=0; i < N; ++i)  { dest[i] = eT(dist(local_rng)); }
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+void
+SymEigsSolver<eT, SelectionRule, OpType>::factorise_from(uword from_k, uword to_m, const Col<eT>& fk)
+  {
+  arma_extra_debug_sigprint();
+  
+  if(to_m <= from_k) { return; }
+
+  fac_f = fk;
+
+  Col<eT> w(dim_n, arma_zeros_indicator());
+  // Norm of f
+  eT beta = norm(fac_f);
+  // Used to test beta~=0
+  const eT beta_thresh = eps * eop_aux::sqrt(dim_n);
+  // Keep the upperleft k x k submatrix of H and set other elements to 0
+  fac_H.tail_cols(ncv - from_k).zeros();
+  fac_H.submat(span(from_k, ncv - 1), span(0, from_k - 1)).zeros();
+  for(uword i = from_k; i <= to_m - 1; i++)
+    {
+    bool restart = false;
+    // If beta = 0, then the next V is not full rank
+    // We need to generate a new residual vector that is orthogonal
+    // to the current V, which we call a restart
+    if(beta < near0)
+      {
+      // // Generate new random vector for fac_f
+      // blas_int idist = 2;
+      // blas_int iseed[4] = {1, 3, 5, 7};
+      // iseed[0] = (i + 100) % 4095;
+      // blas_int n = dim_n;
+      // lapack::larnv(&idist, &iseed[0], &n, fac_f.memptr());
+      
+      // Generate new random vector for fac_f
+      fill_rand(fac_f.memptr(), dim_n, i+1);
+      
+      // f <- f - V * V' * f, so that f is orthogonal to V
+      Mat<eT> Vs(fac_V.memptr(), dim_n, i, false); // First i columns
+      Col<eT> Vf = Vs.t() * fac_f;
+      fac_f -= Vs * Vf;
+      // beta <- ||f||
+      beta = norm(fac_f);
+
+      restart = true;
+      }
+
+    // v <- f / ||f||
+    Col<eT> v(fac_V.colptr(i), dim_n, false); // The (i+1)-th column
+    v = fac_f / beta;
+
+    // Note that H[i+1, i] equals to the unrestarted beta
+    fac_H(i, i - 1) = restart ? eT(0) : beta;
+
+    // w <- A * v, v = fac_V.col(i)
+    op.perform_op(v.memptr(), w.memptr());
+    nmatop++;
+
+    fac_H(i - 1, i) = fac_H(i, i - 1); // Due to symmetry
+    eT Hii = dot(v, w);
+    fac_H(i, i) = Hii;
+
+    // f <- w - V * V' * w = w - H[i+1, i] * V{i} - H[i+1, i+1] * V{i+1}
+    // If restarting, we know that H[i+1, i] = 0
+    if(restart)
+      {
+      fac_f = w - Hii * v;
+      }
+    else
+      {
+      fac_f = w - fac_H(i, i - 1) * fac_V.col(i - 1) - Hii * v;
+      }
+
+    beta = norm(fac_f);
+
+    // f/||f|| is going to be the next column of V, so we need to test
+    // whether V' * (f/||f||) ~= 0
+    Mat<eT> Vs(fac_V.memptr(), dim_n, i + 1, false); // First i+1 columns
+    Col<eT> Vf = Vs.t() * fac_f;
+    eT ortho_err = abs(Vf).max();
+    // If not, iteratively correct the residual
+    uword count = 0;
+    while(count < 5 && ortho_err > eps * beta)
+      {
+      // There is an edge case: when beta=||f|| is close to zero, f mostly consists
+      // of rounding errors, so the test [ortho_err < eps * beta] is very
+      // likely to fail. In particular, if beta=0, then the test is ensured to fail.
+      // Hence when this happens, we force f to be zero, and then restart in the
+      // next iteration.
+      if(beta < beta_thresh)
+        {
+        fac_f.zeros();
+        beta = eT(0);
+        break;
+        }
+
+      // f <- f - V * Vf
+      fac_f -= Vs * Vf;
+      // h <- h + Vf
+      fac_H(i - 1, i) += Vf[i - 1];
+      fac_H(i, i - 1) = fac_H(i - 1, i);
+      fac_H(i, i) += Vf[i];
+      // beta <- ||f||
+      beta = norm(fac_f);
+
+      Vf = Vs.t() * fac_f;
+      ortho_err = abs(Vf).max();
+      count++;
+      }
+    }
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+void
+SymEigsSolver<eT, SelectionRule, OpType>::restart(uword k)
+  {
+  arma_extra_debug_sigprint();
+  
+  if(k >= ncv) { return; }
+
+  TridiagQR<eT> decomp;
+  Mat<eT> Q(ncv, ncv, fill::eye);
+
+  for(uword i = k; i < ncv; i++)
+    {
+    // QR decomposition of H-mu*I, mu is the shift
+    fac_H.diag() -= ritz_val(i);
+    decomp.compute(fac_H);
+
+    // Q -> Q * Qi
+    decomp.apply_YQ(Q);
+
+    // H -> Q'HQ
+    // Since QR = H - mu * I, we have H = QR + mu * I
+    // and therefore Q'HQ = RQ + mu * I
+    fac_H = decomp.matrix_RQ();
+    fac_H.diag() += ritz_val(i);
+    }
+
+  // V -> VQ, only need to update the first k+1 columns
+  // Q has some elements being zero
+  // The first (ncv - k + i) elements of the i-th column of Q are non-zero
+  Mat<eT> Vs(dim_n, k + 1, arma_nozeros_indicator());
+  uword nnz;
+  for(uword i = 0; i < k; i++)
+    {
+    nnz = ncv - k + i + 1;
+    Mat<eT> V(fac_V.memptr(), dim_n, nnz, false);
+    Col<eT> q(Q.colptr(i), nnz, false);
+    // OLD CODE:
+    // Vs.col(i) = V * q;
+    // NEW CODE:
+    Col<eT> v(Vs.colptr(i), dim_n, false, true);
+    v = V * q;
+    }
+  
+  Vs.col(k) = fac_V * Q.col(k);
+  fac_V.head_cols(k + 1) = Vs;
+
+  Col<eT> fk = fac_f * Q(ncv - 1, k - 1) + fac_V.col(k) * fac_H(k, k - 1);
+  factorise_from(k, ncv, fk);
+  retrieve_ritzpair();
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+uword
+SymEigsSolver<eT, SelectionRule, OpType>::num_converged(eT tol)
+  {
+  arma_extra_debug_sigprint();
+  
+  // thresh = tol * max(approx0, abs(theta)), theta for ritz value
+  const eT f_norm = norm(fac_f);
+  for(uword i = 0; i < nev; i++)
+    {
+    eT thresh = tol * (std::max)(eps23, std::abs(ritz_val(i)));
+    eT resid = std::abs(ritz_est(i)) * f_norm;
+    ritz_conv[i] = (resid < thresh);
+    }
+
+  return std::count(ritz_conv.begin(), ritz_conv.end(), true);
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+uword
+SymEigsSolver<eT, SelectionRule, OpType>::nev_adjusted(uword nconv)
+  {
+  arma_extra_debug_sigprint();
+  
+  uword nev_new = nev;
+  for(uword i = nev; i < ncv; i++)
+    {
+    if(std::abs(ritz_est(i)) < near0) { nev_new++; }
+    }
+
+  // Adjust nev_new, according to dsaup2.f line 677~684 in ARPACK
+  nev_new += (std::min)(nconv, (ncv - nev_new) / 2);
+  
+  if(nev_new >= ncv) { nev_new = ncv - 1; }
+  
+  if(nev_new == 1)
+    {
+         if(ncv >= 6)  { nev_new = ncv / 2; }
+    else if(ncv >  2)  { nev_new = 2;       }
+    }
+
+  return nev_new;
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+void
+SymEigsSolver<eT, SelectionRule, OpType>::retrieve_ritzpair()
+  {
+  arma_extra_debug_sigprint();
+  
+  TridiagEigen<eT> decomp(fac_H);
+  Col<eT> evals = decomp.eigenvalues();
+  Mat<eT> evecs = decomp.eigenvectors();
+
+  SortEigenvalue<eT, SelectionRule> sorting(evals.memptr(), evals.n_elem);
+  std::vector<uword> ind = sorting.index();
+
+  // For BOTH_ENDS, the eigenvalues are sorted according
+  // to the LARGEST_ALGE rule, so we need to move those smallest
+  // values to the left
+  // The order would be
+  // Largest => Smallest => 2nd largest => 2nd smallest => ...
+  // We keep this order since the first k values will always be
+  // the wanted collection, no matter k is nev_updated (used in restart())
+  // or is nev (used in sort_ritzpair())
+  if(SelectionRule == EigsSelect::BOTH_ENDS)
+    {
+    std::vector<uword> ind_copy(ind);
+    for(uword i = 0; i < ncv; i++)
+      {
+      // If i is even, pick values from the left (large values)
+      // If i is odd, pick values from the right (small values)
+      
+      ind[i] = (i % 2 == 0) ? ind_copy[i / 2] : ind_copy[ncv - 1 - i / 2];
+      }
+    }
+
+  // Copy the ritz values and vectors to ritz_val and ritz_vec, respectively
+  for(uword i = 0; i < ncv; i++)
+    {
+    ritz_val(i) = evals(ind[i]);
+    ritz_est(i) = evecs(ncv - 1, ind[i]);
+    }
+  for(uword i = 0; i < nev; i++)
+    {
+    ritz_vec.col(i) = evecs.col(ind[i]);
+    }
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+void
+SymEigsSolver<eT, SelectionRule, OpType>::sort_ritzpair()
+  {
+  arma_extra_debug_sigprint();
+  
+  // SortEigenvalue<eT, EigsSelect::LARGEST_MAGN> sorting(ritz_val.memptr(), nev);
+  
+  // Sort Ritz values in ascending algebraic, to be consistent with ARPACK
+  SortEigenvalue<eT, EigsSelect::SMALLEST_ALGE> sorting(ritz_val.memptr(), nev);
+  
+  std::vector<uword> ind = sorting.index();
+  
+  Col<eT>           new_ritz_val(ncv,      arma_zeros_indicator()  );
+  Mat<eT>           new_ritz_vec(ncv, nev, arma_nozeros_indicator());
+  std::vector<bool> new_ritz_conv(nev);
+  
+  for(uword i = 0; i < nev; i++)
+    {
+    new_ritz_val(i) = ritz_val(ind[i]);
+    new_ritz_vec.col(i) = ritz_vec.col(ind[i]);
+    new_ritz_conv[i] = ritz_conv[ind[i]];
+    }
+  
+  ritz_val.swap(new_ritz_val);
+  ritz_vec.swap(new_ritz_vec);
+  ritz_conv.swap(new_ritz_conv);
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+SymEigsSolver<eT, SelectionRule, OpType>::SymEigsSolver(const OpType& op_, uword nev_, uword ncv_)
+  : op(op_)
+  , nev(nev_)
+  , dim_n(op.n_rows)
+  , ncv(ncv_ > dim_n ? dim_n : ncv_)
+  , nmatop(0)
+  , niter(0)
+  , eps(std::numeric_limits<eT>::epsilon())
+  , eps23(std::pow(eps, eT(2.0) / 3))
+  , near0(std::numeric_limits<eT>::min() * eT(10))
+  {
+  arma_extra_debug_sigprint();
+  
+  arma_debug_check( (nev_ < 1 || nev_ > dim_n - 1), "newarp::SymEigsSolver: nev must satisfy 1 <= nev <= n - 1, n is the size of matrix" );
+  arma_debug_check( (ncv_ <= nev_ || ncv_ > dim_n), "newarp::SymEigsSolver: ncv must satisfy nev < ncv <= n, n is the size of matrix" );
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+void
+SymEigsSolver<eT, SelectionRule, OpType>::init(eT* init_resid)
+  {
+  arma_extra_debug_sigprint();
+  
+  // Reset all matrices/vectors to zero
+  fac_V.zeros(dim_n, ncv);
+  fac_H.zeros(ncv, ncv);
+  fac_f.zeros(dim_n);
+  ritz_val.zeros(ncv);
+  ritz_vec.zeros(ncv, nev);
+  ritz_est.zeros(ncv);
+  ritz_conv.assign(nev, false);
+
+  nmatop = 0;
+  niter = 0;
+
+  Col<eT> r(init_resid, dim_n, false);
+  // The first column of fac_V
+  Col<eT> v(fac_V.colptr(0), dim_n, false);
+  eT rnorm = norm(r);
+  arma_check( (rnorm < near0), "newarp::SymEigsSolver::init(): initial residual vector cannot be zero" );
+  v = r / rnorm;
+
+  Col<eT> w(dim_n, arma_zeros_indicator());
+  op.perform_op(v.memptr(), w.memptr());
+  nmatop++;
+
+  fac_H(0, 0) = dot(v, w);
+  fac_f = w - v * fac_H(0, 0);
+
+  // In some cases f is zero in exact arithmetics, but due to rounding errors
+  // it may contain tiny fluctuations. When this happens, we force f to be zero
+  if(abs(fac_f).max() < eps)  { fac_f.zeros(); }
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+void
+SymEigsSolver<eT, SelectionRule, OpType>::init()
+  {
+  arma_extra_debug_sigprint();
+  
+  // podarray<eT> init_resid(dim_n);
+  // blas_int idist = 2;                // Uniform(-1, 1)
+  // blas_int iseed[4] = {1, 3, 5, 7};  // Fixed random seed
+  // blas_int n = dim_n;
+  // lapack::larnv(&idist, &iseed[0], &n, init_resid.memptr());
+  // init(init_resid.memptr());
+  
+  podarray<eT> init_resid(dim_n);
+  
+  fill_rand(init_resid.memptr(), dim_n, 0);
+  
+  init(init_resid.memptr());
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+uword
+SymEigsSolver<eT, SelectionRule, OpType>::compute(uword maxit, eT tol)
+  {
+  arma_extra_debug_sigprint();
+  
+  // The m-step Arnoldi factorisation
+  factorise_from(1, ncv, fac_f);
+  retrieve_ritzpair();
+  // Restarting
+  uword i, nconv = 0, nev_adj;
+  for(i = 0; i < maxit; i++)
+    {
+    nconv = num_converged(tol);
+    if(nconv >= nev) { break; }
+
+    nev_adj = nev_adjusted(nconv);
+    restart(nev_adj);
+    }
+  // Sorting results
+  sort_ritzpair();
+
+  niter = i + 1;
+
+  return (std::min)(nev, nconv);
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+Col<eT>
+SymEigsSolver<eT, SelectionRule, OpType>::eigenvalues()
+  {
+  arma_extra_debug_sigprint();
+  
+  uword nconv = std::count(ritz_conv.begin(), ritz_conv.end(), true);
+  Col<eT> res(nconv, arma_zeros_indicator());
+  
+  if(nconv > 0)
+    {
+    uword j = 0;
+    
+    for(uword i=0; i < nev; i++)
+      {
+      if(ritz_conv[i])  { res(j) = ritz_val(i); j++; }
+      }
+    }
+  
+  return res;
+  }
+
+
+
+template<typename eT, int SelectionRule, typename OpType>
+inline
+Mat<eT>
+SymEigsSolver<eT, SelectionRule, OpType>::eigenvectors(uword nvec)
+  {
+  arma_extra_debug_sigprint();
+  
+  uword nconv = std::count(ritz_conv.begin(), ritz_conv.end(), true);
+  nvec = (std::min)(nvec, nconv);
+  Mat<eT> res(dim_n, nvec);
+  
+  if(nvec > 0)
+    {
+    Mat<eT> ritz_vec_conv(ncv, nvec, arma_zeros_indicator());
+    
+    uword j = 0;
+    
+    for(uword i=0; i < nev && j < nvec; i++)
+      {
+      if(ritz_conv[i])  { ritz_vec_conv.col(j) = ritz_vec.col(i); j++; }
+      }
+    
+    res = fac_V * ritz_vec_conv;
+    }
+  
+  return res;
+  }
+
+
+}  // namespace newarp