uks_convergenceacc.cc

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/*
 *            Copyright 2009-2026 The VOTCA Development Team
 *                       (http://www.votca.org)
 *
 *      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.
 *
 */
 
// Local VOTCA includes
#include "votca/xtp/uks_convergenceacc.h"
 
#include <tuple>
 
namespace votca {
namespace xtp {
 
void UKSConvergenceAcc::Configure(const options& opt_alpha,
                                  const options& opt_beta) {
  opt_alpha_ = opt_alpha;
  opt_beta_ = opt_beta;
 
  nocclevels_alpha_ = opt_alpha_.numberofelectrons;
  nocclevels_beta_ = opt_beta_.numberofelectrons;
 
  // one shared DIIS/ADIIS history length
  diis_.setHistLength(opt_alpha_.histlength);
  // adiis_.setHistLength(opt_alpha_.histlength);
}
 
void UKSConvergenceAcc::setLogger(Logger* log) { log_ = log; }
 
void UKSConvergenceAcc::setOverlap(AOOverlap& S, double etol) {
  S_ = &S;
  Sminusahalf = S.Pseudo_InvSqrt(etol);
  XTP_LOG(Log::error, *log_)
      << TimeStamp() << " Smallest value of AOOverlap matrix is "
      << S_->SmallestEigenValue() << std::flush;
  XTP_LOG(Log::error, *log_)
      << TimeStamp() << " Removed " << S_->Removedfunctions()
      << " basisfunction from inverse overlap matrix" << std::flush;
}
 
tools::EigenSystem UKSConvergenceAcc::SolveFockmatrix(
    const Eigen::MatrixXd& H) const {
  Eigen::MatrixXd H_ortho = Sminusahalf.transpose() * H * Sminusahalf;
  Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es(H_ortho);
 
  if (es.info() != Eigen::ComputationInfo::Success) {
    throw std::runtime_error("Matrix Diagonalisation failed. DiagInfo" +
                             std::to_string(es.info()));
  }
 
  tools::EigenSystem result;
  result.eigenvalues() = es.eigenvalues();
  result.eigenvectors() = Sminusahalf * es.eigenvectors();
  return result;
}
 
Eigen::MatrixXd UKSConvergenceAcc::UnflattenRotation(
    const Eigen::VectorXd& v_ov, Index nao, Index nocclevels) const {
  Index nvirt = nao - nocclevels;
  Eigen::MatrixXd kappa = Eigen::MatrixXd::Zero(nao, nao);
  for (Index i = 0; i < nocclevels; ++i) {
    for (Index a = 0; a < nvirt; ++a) {
      double val = v_ov(i * nvirt + a);
      kappa(i, nocclevels + a) = val;
      kappa(nocclevels + a, i) = -val;
    }
  }
  return kappa;
}
 
std::pair<Eigen::MatrixXd, Eigen::MatrixXd>
    UKSConvergenceAcc::UnflattenCoupledRotation(const Eigen::VectorXd& v,
                                                Index nao_alpha,
                                                Index nocclevels_alpha,
                                                Index nao_beta,
                                                Index nocclevels_beta) const {
  Index n_ov_alpha = nocclevels_alpha * (nao_alpha - nocclevels_alpha);
  Eigen::VectorXd v_alpha = v.head(n_ov_alpha);
  Eigen::VectorXd v_beta = v.tail(v.size() - n_ov_alpha);
  Eigen::MatrixXd kappa_alpha =
      UnflattenRotation(v_alpha, nao_alpha, nocclevels_alpha);
  Eigen::MatrixXd kappa_beta =
      UnflattenRotation(v_beta, nao_beta, nocclevels_beta);
  return {kappa_alpha, kappa_beta};
}
 
Eigen::VectorXd UKSConvergenceAcc::BuildCoupledSigmaVector(
    const Eigen::VectorXd& v, const Eigen::MatrixXd& C_alpha,
    Index nocclevels_alpha, const Eigen::MatrixXd& C_beta,
    Index nocclevels_beta, const CoupledFockBuilder& coupled_fock_builder,
    double finite_diff_step) const {
  Index nao_alpha = C_alpha.rows();
  Index nvirt_alpha = nao_alpha - nocclevels_alpha;
  Index nao_beta = C_beta.rows();
  Index nvirt_beta = nao_beta - nocclevels_beta;
  Index n_ov_alpha = nocclevels_alpha * nvirt_alpha;
  Index n_ov_beta = nocclevels_beta * nvirt_beta;
 
  auto [kappa_alpha_trial, kappa_beta_trial] = UnflattenCoupledRotation(
      v, nao_alpha, nocclevels_alpha, nao_beta, nocclevels_beta);
  kappa_alpha_trial *= finite_diff_step;
  kappa_beta_trial *= finite_diff_step;
 
  // Same linearized-exponential + Lowdin-reorthonormalization approach
  // as BuildSigmaVector's own EvaluateGradientAt -- but rotates BOTH
  // channels together and builds BOTH new Fock matrices together in
  // ONE coupled_fock_builder call, rather than one channel at a time
  // with the other held fixed. This is what actually captures the
  // alpha-beta coupling: the shared Coulomb potential and the XC
  // kernel's cross-spin terms naturally see both perturbed densities
  // together inside coupled_fock_builder, rather than needing this
  // function to construct any cross-coupling term explicitly itself.
  auto EvaluateBothGradientsAt = [&](const Eigen::MatrixXd& kappa_alpha,
                                     const Eigen::MatrixXd& kappa_beta)
      -> std::pair<Eigen::MatrixXd, Eigen::MatrixXd> {
    Eigen::MatrixXd C_alpha_rot =
        C_alpha *
        (Eigen::MatrixXd::Identity(nao_alpha, nao_alpha) + kappa_alpha);
    Eigen::MatrixXd nonortho_alpha =
        C_alpha_rot.transpose() * S_->Matrix() * C_alpha_rot;
    Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es_alpha(nonortho_alpha);
    C_alpha_rot = C_alpha_rot * es_alpha.operatorInverseSqrt();
 
    Eigen::MatrixXd C_beta_rot =
        C_beta * (Eigen::MatrixXd::Identity(nao_beta, nao_beta) + kappa_beta);
    Eigen::MatrixXd nonortho_beta =
        C_beta_rot.transpose() * S_->Matrix() * C_beta_rot;
    Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es_beta(nonortho_beta);
    C_beta_rot = C_beta_rot * es_beta.operatorInverseSqrt();
 
    Eigen::MatrixXd C_alpha_occ_rot = C_alpha_rot.leftCols(nocclevels_alpha);
    Eigen::MatrixXd D_alpha_rot = C_alpha_occ_rot * C_alpha_occ_rot.transpose();
    Eigen::MatrixXd C_beta_occ_rot = C_beta_rot.leftCols(nocclevels_beta);
    Eigen::MatrixXd D_beta_rot = C_beta_occ_rot * C_beta_occ_rot.transpose();
 
    SpinFock H_rot = coupled_fock_builder(D_alpha_rot, D_beta_rot);
    Eigen::MatrixXd F_MO_alpha_rot =
        C_alpha_rot.transpose() * H_rot.alpha * C_alpha_rot;
    Eigen::MatrixXd F_MO_beta_rot =
        C_beta_rot.transpose() * H_rot.beta * C_beta_rot;
    return {F_MO_alpha_rot, F_MO_beta_rot};
  };
 
  auto [F_MO_alpha_plus, F_MO_beta_plus] =
      EvaluateBothGradientsAt(kappa_alpha_trial, kappa_beta_trial);
  auto [F_MO_alpha_minus, F_MO_beta_minus] =
      EvaluateBothGradientsAt(-kappa_alpha_trial, -kappa_beta_trial);
 
  Eigen::VectorXd sigma(n_ov_alpha + n_ov_beta);
  for (Index i = 0; i < nocclevels_alpha; ++i) {
    for (Index a = 0; a < nvirt_alpha; ++a) {
      double g_plus_ia = F_MO_alpha_plus(i, nocclevels_alpha + a);
      double g_minus_ia = F_MO_alpha_minus(i, nocclevels_alpha + a);
      sigma(i * nvirt_alpha + a) =
          (g_plus_ia - g_minus_ia) / (2.0 * finite_diff_step);
    }
  }
  for (Index i = 0; i < nocclevels_beta; ++i) {
    for (Index a = 0; a < nvirt_beta; ++a) {
      double g_plus_ia = F_MO_beta_plus(i, nocclevels_beta + a);
      double g_minus_ia = F_MO_beta_minus(i, nocclevels_beta + a);
      sigma(n_ov_alpha + i * nvirt_beta + a) =
          (g_plus_ia - g_minus_ia) / (2.0 * finite_diff_step);
    }
  }
  return sigma;
}
 
Eigen::MatrixXd UKSConvergenceAcc::DirectMinimizationRotation(
    const Eigen::MatrixXd& H_AO, const tools::EigenSystem& MOs,
    Index nocclevels, double& predicted_energy_change) const {
  Index nao = MOs.eigenvectors().rows();
  const Eigen::MatrixXd& C = MOs.eigenvectors();
  const Eigen::VectorXd& eps = MOs.eigenvalues();
 
  // Orbital gradient: the occ-virt block of the MO-basis Fock matrix,
  // which vanishes exactly at self-consistency (F_MO would be block-
  // diagonal, occ-occ and virt-virt only).
  Eigen::MatrixXd F_MO = C.transpose() * H_AO * C;
 
  // Antisymmetric rotation generator: nonzero only in the occ-virt (and
  // virt-occ, by antisymmetry) blocks -- occ-occ/virt-virt rotations
  // would leave the density matrix (and therefore the energy) entirely
  // unchanged, so there is nothing to gain from including them.
  Eigen::MatrixXd kappa = Eigen::MatrixXd::Zero(nao, nao);
  // g_h_ratio(i,a) below stores kappa_ia's own g_ia/h_ia BEFORE the
  // whole-matrix trust-radius scaling further down -- needed again
  // afterward to compute the quadratic model's predicted energy change
  // using the FINAL (per-element- and trust-radius-clamped) kappa,
  // since g_ia and h_ia themselves do not change under that later,
  // uniform rescaling, only kappa does.
  Eigen::MatrixXd h_matrix = Eigen::MatrixXd::Zero(nao, nao);
  // Guards near-degenerate occ-virt orbital pairs from producing a
  // wildly oversized step for THAT pair specifically -- confirmed
  // directly against an independent ORCA reference run on this exact
  // system, whose own TRAH implementation repeatedly hit BOTH
  // genuinely negative gaps (occupied/virtual character swapping
  // during iteration, since occupation here is defined by column
  // index, not current energy ordering -- down to -0.635 Ha) and
  // gaps numerically indistinguishable from zero (as small as
  // 0.000002 Ha), needing its own explicit warnings and special
  // handling for both. abs() here is essential, not cosmetic: a
  // signed gap of -0.6 would otherwise pass std::max(-0.6, kMinGap)
  // as if it were the tiny POSITIVE value kMinGap, producing an
  // enormous, wrong-direction step for exactly that pair -- the
  // Hessian approximation must be treated as positive-definite
  // (a legitimate descent direction) regardless of the true, possibly
  // negative or near-zero curvature this diagonal approximation
  // cannot itself represent.
  constexpr double kMinGap = 1e-3;
  // Per-PAIR step cap, independent of and in addition to the overall,
  // whole-matrix trust-radius bound below -- confirmed necessary
  // because a single pathological pair (as above) could otherwise
  // dominate the entire rotation DIRECTION even after the whole
  // matrix is normalized to the trust radius, silently scaling every
  // other, well-behaved pair down to near-nothing while that one bad
  // pair still controls where the step actually points.
  constexpr double kMaxKappaElement = 0.1;
  for (Index i = 0; i < nocclevels; ++i) {
    for (Index a = nocclevels; a < nao; ++a) {
      double gap = std::max(std::abs(eps(a) - eps(i)), kMinGap);
      // Approximate, diagonal Hessian: orbital energy differences --
      // the same cheap starting approximation used by most quasi-
      // Newton SCF methods (e.g. SOSCF's own initial Hessian guess).
      double h_ia = 2.0 * gap;
      double kappa_ia = -F_MO(i, a) / h_ia;
      kappa_ia = std::clamp(kappa_ia, -kMaxKappaElement, kMaxKappaElement);
      kappa(i, a) = kappa_ia;
      kappa(a, i) = -kappa_ia;
      h_matrix(i, a) = h_ia;
    }
  }
 
  double knorm = kappa.norm();
  if (knorm > trust_radius_current_) {
    kappa *= (trust_radius_current_ / knorm);
  }
 
  // Quadratic model's own predicted energy change, Sum_ia[g_ia*kappa_ia
  // + 0.5*h_ia*kappa_ia^2], using the FINAL kappa (after both the
  // per-element clamp and the whole-matrix trust-radius scaling above)
  // -- needed by Iterate's own Fletcher-style accept/reject logic.
  // g_ia = F_MO(i,a) directly, matching the sign convention kappa_ia =
  // -F_MO(i,a)/h_ia was itself built from above.
  predicted_energy_change = 0.0;
  for (Index i = 0; i < nocclevels; ++i) {
    for (Index a = nocclevels; a < nao; ++a) {
      double kappa_ia = kappa(i, a);
      predicted_energy_change +=
          F_MO(i, a) * kappa_ia + 0.5 * h_matrix(i, a) * kappa_ia * kappa_ia;
    }
  }
 
  // exp(kappa) ~= I + kappa is valid specifically because the trust-
  // radius bound above keeps kappa small -- re-orthonormalize
  // afterward since I+kappa is only approximately unitary, using the
  // same Lowdin (symmetric orthogonalization) approach as
  // DFTEngine::OrthogonalizeGuess.
  Eigen::MatrixXd C_new = C * (Eigen::MatrixXd::Identity(nao, nao) + kappa);
  Eigen::MatrixXd nonortho = C_new.transpose() * S_->Matrix() * C_new;
  Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es_ortho(nonortho);
  return C_new * es_ortho.operatorInverseSqrt();
}
 
namespace {
// Local operator matching DavidsonSolver's own MatrixReplacement
// template interface (.rows(), .diagonal(), operator*(MatrixXd)) for
// the augmented Hessian over the COMBINED (alpha+beta) rotation space
// -- g and diag_h are the concatenated, both-channel vectors, and each
// operator* call rotates BOTH channels together via
// UKSConvergenceAcc::BuildCoupledSigmaVector, capturing the genuine
// alpha-beta coupling that treating the two channels independently
// would not.
struct CoupledAugmentedHessianOperator {
  const Eigen::VectorXd& g;
  const Eigen::MatrixXd& C_alpha;
  Index nocclevels_alpha;
  const Eigen::MatrixXd& C_beta;
  Index nocclevels_beta;
  double alpha_scale;
  const UKSConvergenceAcc::CoupledFockBuilder& coupled_fock_builder;
  const UKSConvergenceAcc* self;
  const Eigen::VectorXd& diag_h;
 
  Index rows() const { return 1 + g.size(); }
 
  Eigen::VectorXd diagonal() const {
    Eigen::VectorXd d(1 + g.size());
    d(0) = 0.0;
    d.tail(g.size()) = diag_h;
    return d;
  }
 
  Eigen::MatrixXd operator*(const Eigen::MatrixXd& V) const {
    Eigen::MatrixXd AV = Eigen::MatrixXd::Zero(V.rows(), V.cols());
    for (Index col = 0; col < V.cols(); ++col) {
      double v0 = V(0, col);
      Eigen::VectorXd v_ov = V.block(1, col, g.size(), 1);
      AV(0, col) = alpha_scale * g.dot(v_ov);
      Eigen::VectorXd sigma =
          self->BuildCoupledSigmaVector(v_ov, C_alpha, nocclevels_alpha, C_beta,
                                        nocclevels_beta, coupled_fock_builder);
      AV.block(1, col, g.size(), 1) = alpha_scale * g * v0 + sigma;
    }
    return AV;
  }
};
}  // namespace
 
std::pair<Eigen::MatrixXd, Eigen::MatrixXd>
    UKSConvergenceAcc::CoupledAugmentedHessianStep(
        const Eigen::MatrixXd& H_AO_alpha, const tools::EigenSystem& MOs_alpha,
        Index nocclevels_alpha, const Eigen::MatrixXd& H_AO_beta,
        const tools::EigenSystem& MOs_beta, Index nocclevels_beta,
        const CoupledFockBuilder& coupled_fock_builder, double trust_radius,
        double& predicted_energy_change) const {
  Index nao_alpha = MOs_alpha.eigenvectors().rows();
  Index nvirt_alpha = nao_alpha - nocclevels_alpha;
  Index n_ov_alpha = nocclevels_alpha * nvirt_alpha;
  const Eigen::MatrixXd& C_alpha = MOs_alpha.eigenvectors();
  const Eigen::VectorXd& eps_alpha = MOs_alpha.eigenvalues();
 
  Index nao_beta = MOs_beta.eigenvectors().rows();
  Index nvirt_beta = nao_beta - nocclevels_beta;
  Index n_ov_beta = nocclevels_beta * nvirt_beta;
  const Eigen::MatrixXd& C_beta = MOs_beta.eigenvectors();
  const Eigen::VectorXd& eps_beta = MOs_beta.eigenvalues();
 
  Index n_ov = n_ov_alpha + n_ov_beta;
 
  Eigen::MatrixXd F_MO_alpha = C_alpha.transpose() * H_AO_alpha * C_alpha;
  Eigen::MatrixXd F_MO_beta = C_beta.transpose() * H_AO_beta * C_beta;
 
  // Combined gradient and diagonal-Hessian preconditioner: alpha's own
  // block first, beta's immediately after -- matching
  // UnflattenCoupledRotation/BuildCoupledSigmaVector's own layout
  // convention. The diagonal preconditioner itself is still built
  // per-channel from each channel's OWN orbital-energy gaps (same
  // formula as an earlier, decoupled AugmentedHessianStep
  // implementation's own diag_h) -- only the actual
  // Hessian-VECTOR product (via BuildCoupledSigmaVector, inside
  // CoupledAugmentedHessianOperator) captures the real cross-channel
  // coupling; the preconditioner is only ever an approximate guide for
  // the Davidson iteration, not the step itself, so this
  // simplification (no explicit alpha-beta cross term in the
  // preconditioner) does not undermine what this whole undertaking is
  // actually meant to fix.
  Eigen::VectorXd g(n_ov);
  Eigen::VectorXd diag_h(n_ov);
  constexpr double kMinGap = 1e-3;
  for (Index i = 0; i < nocclevels_alpha; ++i) {
    for (Index a = 0; a < nvirt_alpha; ++a) {
      g(i * nvirt_alpha + a) = F_MO_alpha(i, nocclevels_alpha + a);
      double gap = std::max(
          std::abs(eps_alpha(nocclevels_alpha + a) - eps_alpha(i)), kMinGap);
      diag_h(i * nvirt_alpha + a) = 2.0 * gap;
    }
  }
  for (Index i = 0; i < nocclevels_beta; ++i) {
    for (Index a = 0; a < nvirt_beta; ++a) {
      g(n_ov_alpha + i * nvirt_beta + a) = F_MO_beta(i, nocclevels_beta + a);
      double gap = std::max(
          std::abs(eps_beta(nocclevels_beta + a) - eps_beta(i)), kMinGap);
      diag_h(n_ov_alpha + i * nvirt_beta + a) = 2.0 * gap;
    }
  }
 
  double alpha_min = 1.0;
  double alpha_max = 1000.0;
  Eigen::VectorXd best_kappa_flat = Eigen::VectorXd::Zero(n_ov);
  double best_mu = 0.0;
 
  Eigen::MatrixXd initial_guess = Eigen::MatrixXd::Zero(1 + n_ov, 2);
  initial_guess(0, 0) = 1.0;
  double gnorm = g.norm();
  if (gnorm > 1e-12) {
    initial_guess.block(1, 1, n_ov, 1) = g / gnorm;
  } else {
    initial_guess(1, 1) = 1.0;
  }
 
  // Returns whether the Davidson solve itself actually converged
  // (solver.info() == Eigen::ComputationInfo::Success) within its own
  // iter_max_ budget -- confirmed necessary directly, from a real run
  // on a genuinely difficult system (a -1 charge CDFT-constrained onto
  // a 28-atom fragment): the solver's own diagnostic output showed
  // "0.00% converged" repeated across dozens of iterations and several
  // restarts, yet the ORIGINAL version of this lambda still
  // unconditionally extracted eigenvectors()/eigenvalues() and used
  // them as if the solve had succeeded -- there was no way for the
  // caller to ever detect this had happened at all.
  auto SolveForAlpha = [&](double alpha_try, Eigen::VectorXd& kappa_flat_out,
                           double& mu_out) -> bool {
    CoupledAugmentedHessianOperator op{g,
                                       C_alpha,
                                       nocclevels_alpha,
                                       C_beta,
                                       nocclevels_beta,
                                       alpha_try,
                                       coupled_fock_builder,
                                       this,
                                       diag_h};
    DavidsonSolver solver(*log_);
    solver.set_matrix_type("SYMM");
    solver.set_tolerance("loose");
    solver.set_iter_max(opt_alpha_.davidson_max_iter);
    solver.set_max_search_space(40);
    solver.solve(op, 1, initial_guess);
    if (solver.info() != Eigen::ComputationInfo::Success) {
      return false;
    }
    Eigen::VectorXd eigvec = solver.eigenvectors().col(0);
    mu_out = solver.eigenvalues()(0);
    double v0 = eigvec(0);
    if (std::abs(v0) < 1e-8) {
      kappa_flat_out = Eigen::VectorXd::Zero(g.size());
      return true;
    }
    kappa_flat_out = eigvec.tail(g.size()) / v0;
    return true;
  };
 
  double alpha_try = alpha_min;
  constexpr int kMaxBisectionIters = 20;
  bool have_converged_once = false;
  for (int bisection_iter = 0; bisection_iter < kMaxBisectionIters;
       ++bisection_iter) {
    Eigen::VectorXd kappa_flat;
    double mu;
    bool converged = SolveForAlpha(alpha_try, kappa_flat, mu);
    if (!converged) {
      // Never trust/store a failed solve's own kappa/mu at all -- see
      // this function's own header comment on SolveForAlpha above for
      // why the ORIGINAL version of this loop did exactly that, and
      // the real, confirmed consequence. Treated the same way as
      // step_norm > trust_radius: alpha_min moves up, since a LARGER
      // alpha generally makes the augmented Hessian's own lowest
      // eigenvalue more distinct from the rest of its spectrum --
      // easier for Davidson to isolate, not harder -- so retrying at
      // a larger alpha is a reasonable, physically-motivated response
      // to a failed solve, not an arbitrary guess.
      alpha_min = alpha_try;
      alpha_try = 0.5 * (alpha_min + alpha_max);
      continue;
    }
    have_converged_once = true;
    double step_norm = kappa_flat.norm() / alpha_try;
    best_kappa_flat = kappa_flat;
    best_mu = mu;
    if (std::abs(step_norm - trust_radius) < 0.01 * trust_radius) {
      break;
    }
    if (step_norm > trust_radius) {
      alpha_min = alpha_try;
    } else {
      alpha_max = alpha_try;
    }
    alpha_try = 0.5 * (alpha_min + alpha_max);
  }
 
  if (!have_converged_once) {
    // Every single bisection attempt's own Davidson solve failed to
    // converge -- confirmed directly, from a real run, that silently
    // proceeding here (with best_kappa_flat/best_mu left at whatever
    // they were default-initialized to, never actually set by a
    // genuine solve) is the wrong thing to do: there is no
    // meaningful step to take at all, and the caller deserves to know
    // this failed rather than silently receiving a zero/garbage step.
    throw std::runtime_error(
        "CoupledAugmentedHessianStep: DavidsonSolver failed to converge "
        "for every bisection trial (all " +
        std::to_string(kMaxBisectionIters) +
        " attempts) -- no genuine augmented-Hessian step could be "
        "computed at all.");
  }
 
  XTP_LOG(Log::warning, *log_)
      << TimeStamp()
      << " CoupledAugmentedHessianStep bisection diagnostic: "
         "final alpha_try="
      << alpha_try
      << ", achieved step_norm=" << (best_kappa_flat.norm() / alpha_try)
      << ", requested trust_radius=" << trust_radius << std::flush;
 
  auto [kappa_alpha, kappa_beta] = UnflattenCoupledRotation(
      best_kappa_flat, nao_alpha, nocclevels_alpha, nao_beta, nocclevels_beta);
 
  // ONE, combined predicted energy change for the whole, coupled step
  // -- same formula as an earlier, decoupled AugmentedHessianStep
  // implementation's own (Q(kappa)-E0 =
  // 0.5*(g^T*kappa + mu*||kappa||^2)), but now naturally a single
  // number for both channels together, rather than needing to be
  // summed from two separate calls the way that earlier, decoupled
  // implementation's own path did.
  predicted_energy_change =
      0.5 * (g.dot(best_kappa_flat) + best_mu * best_kappa_flat.squaredNorm());
 
  Eigen::MatrixXd C_alpha_new =
      C_alpha * (Eigen::MatrixXd::Identity(nao_alpha, nao_alpha) + kappa_alpha);
  Eigen::MatrixXd nonortho_alpha =
      C_alpha_new.transpose() * S_->Matrix() * C_alpha_new;
  Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es_alpha(nonortho_alpha);
  C_alpha_new = C_alpha_new * es_alpha.operatorInverseSqrt();
 
  Eigen::MatrixXd C_beta_new =
      C_beta * (Eigen::MatrixXd::Identity(nao_beta, nao_beta) + kappa_beta);
  Eigen::MatrixXd nonortho_beta =
      C_beta_new.transpose() * S_->Matrix() * C_beta_new;
  Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es_beta(nonortho_beta);
  C_beta_new = C_beta_new * es_beta.operatorInverseSqrt();
 
  return {C_alpha_new, C_beta_new};
}
 
Eigen::MatrixXd UKSConvergenceAcc::DensityMatrixGroundState_unres(
    const Eigen::MatrixXd& MOs, Index nocclevels) const {
  if (nocclevels == 0) {
    return Eigen::MatrixXd::Zero(MOs.rows(), MOs.rows());
  }
  Eigen::MatrixXd occstates = MOs.leftCols(nocclevels);
  return occstates * occstates.transpose();
}
 
UKSConvergenceAcc::SpinDensity UKSConvergenceAcc::DensityMatrix(
    const tools::EigenSystem& MOs_alpha,
    const tools::EigenSystem& MOs_beta) const {
  SpinDensity result;
  result.alpha = DensityMatrixGroundState_unres(MOs_alpha.eigenvectors(),
                                                nocclevels_alpha_);
  result.beta =
      DensityMatrixGroundState_unres(MOs_beta.eigenvectors(), nocclevels_beta_);
  return result;
}
 
void UKSConvergenceAcc::Levelshift(Eigen::MatrixXd& H,
                                   const Eigen::MatrixXd& MOs_old,
                                   const options& opt, Index nocclevels) const {
  if (opt.levelshift < 1e-9) {
    return;
  }
  Eigen::VectorXd virt = Eigen::VectorXd::Zero(H.rows());
  for (Index i = nocclevels; i < H.rows(); ++i) {
    virt(i) = opt.levelshift;
  }
 
  XTP_LOG(Log::error, *log_)
      << TimeStamp() << " Using levelshift:" << opt.levelshift << " Hartree"
      << std::flush;
 
  Eigen::MatrixXd vir = S_->Matrix() * MOs_old * virt.asDiagonal() *
                        MOs_old.transpose() * S_->Matrix();
  H += vir;
}
 
Eigen::MatrixXd UKSConvergenceAcc::BuildErrorMatrix(
    const Eigen::MatrixXd& dmat, const Eigen::MatrixXd& H) const {
  const Eigen::MatrixXd& S = S_->Matrix();
  return Sminusahalf.transpose() * (H * dmat * S - S * dmat * H) * Sminusahalf;
}
 
double UKSConvergenceAcc::CombinedError(const Eigen::MatrixXd& err_alpha,
                                        const Eigen::MatrixXd& err_beta) const {
  return std::max(err_alpha.cwiseAbs().maxCoeff(),
                  err_beta.cwiseAbs().maxCoeff());
}
 
UKSConvergenceAcc::SpinDensity UKSConvergenceAcc::Iterate(
    const SpinDensity& dmat, SpinFock& H, tools::EigenSystem& MOs_alpha,
    tools::EigenSystem& MOs_beta, double totE) {
 
  // Fletcher's trust-radius update (Helmich-Paris, J. Chem. Phys. 154,
  // 164104 (2021), Sec. II D -- confirmed directly by reading that
  // paper, not reconstructed from memory): verify whatever
  // DirectMinimizationRotation step was taken last call, now that its
  // actual effect on the energy (totE, just passed in -- computed by
  // the caller from a real Fock build on that step's own density) is
  // finally available. This CANNOT be checked within the same
  // Iterate() call that took the step, since that call has no way to
  // know what energy its own returned density will produce until the
  // caller has built a new Fock matrix from it and come back around.
  if (direct_min_pending_) {
    double actual_change = totE - direct_min_pre_energy_;
    double r = (std::abs(direct_min_predicted_change_) > 1e-14)
                   ? actual_change / direct_min_predicted_change_
                   : -1.0;  // treat a degenerate (~zero) predicted change
                            // as an outright reject, same as r<0 below --
                            // the model gave no useful information about
                            // this step at all.
    XTP_LOG(Log::warning, *log_)
        << TimeStamp()
        << " Direct-minimization step check: actual dE=" << actual_change
        << ", predicted dE=" << direct_min_predicted_change_ << ", r=" << r
        << ", trust radius=" << trust_radius_current_ << std::flush;
    if (r < 0.0) {
      // Reject: the quadratic model was not applicable within the
      // given trust region (either the energy rose while predicted to
      // fall, or vice versa). Revert to the pre-step MOs/energy and
      // shrink the trust radius -- the NEXT call's own
      // consecutive_adiis_failures_ check will naturally retry
      // DirectMinimizationRotation from this reverted point with the
      // smaller radius, since nothing here has changed the underlying
      // (A)DIIS behavior that triggered it in the first place.
      trust_radius_current_ *= 0.7;
      // Floor tied to BuildSigmaVector's own finite-difference step
      // (kFiniteDiffStep = 1e-3, defined there): a real run showed
      // trust_radius shrinking past 1e-6 while the step, predicted
      // change, and actual change stayed EXACTLY identical every
      // time -- the bisection's own alpha_min=1 floor means the
      // gentlest achievable step cannot shrink further once alpha_min
      // itself is the binding constraint, so continuing to request an
      // even smaller trust radius changes nothing and the reject loop
      // can never resolve on its own (confirmed directly: it only
      // ended when the outer SCF's own 100-iteration budget ran out).
      // More fundamentally, a trust radius below the sigma vector's
      // own probing resolution is asking for precision the underlying
      // finite-difference model was never built to provide -- its
      // accuracy does not improve as the requested step shrinks, only
      // the requested step size does. Once hit, give up on direct
      // minimization for this SCF call rather than loop toward
      // ever-smaller radii that cannot change the outcome.
      if (trust_radius_current_ < kMinTrustRadius) {
        direct_min_floor_hit_ = true;
        XTP_LOG(Log::warning, *log_)
            << TimeStamp()
            << " Direct-minimization trust radius fell "
               "below its own finite-difference resolution floor ("
            << kMinTrustRadius
            << ") without an accepted step -- "
               "falling back to mixing instead of continuing to shrink."
            << std::flush;
      }
      MOs_alpha.eigenvectors() = direct_min_pre_MOs_alpha_;
      MOs_beta.eigenvectors() = direct_min_pre_MOs_beta_;
      MOs_alpha.eigenvalues() = direct_min_pre_MOs_alpha_energies_;
      MOs_beta.eigenvalues() = direct_min_pre_MOs_beta_energies_;
      totE_.push_back(direct_min_pre_energy_);
      direct_min_pending_ = false;
      usedmixing_ = false;
      return DensityMatrix(MOs_alpha, MOs_beta);
    } else if (r <= 0.25) {
      // Accepted, but the step was too long -- shrink for next time.
      trust_radius_current_ *= 0.7;
    } else if (r > 0.75) {
      // Accepted, and the model was a good fit -- grow for next time.
      trust_radius_current_ *= 1.2;
    }
    // 0.25 < r <= 0.75: accepted, trust radius left unchanged.
    direct_min_pending_ = false;
  }
 
  if (int(mathist_alpha_.size()) == opt_alpha_.histlength) {
    totE_.erase(totE_.begin() + maxerrorindex_);
    mathist_alpha_.erase(mathist_alpha_.begin() + maxerrorindex_);
    mathist_beta_.erase(mathist_beta_.begin() + maxerrorindex_);
    dmatHist_alpha_.erase(dmatHist_alpha_.begin() + maxerrorindex_);
    dmatHist_beta_.erase(dmatHist_beta_.begin() + maxerrorindex_);
  }
 
  totE_.push_back(totE);
 
  if (nocclevels_alpha_ > 0 &&
      nocclevels_alpha_ < MOs_alpha.eigenvalues().size()) {
    double gap_alpha = MOs_alpha.eigenvalues()(nocclevels_alpha_) -
                       MOs_alpha.eigenvalues()(nocclevels_alpha_ - 1);
    // consecutive_adiis_failures_ < kMaxConsecutiveADIISFailures added
    // deliberately: a real run showed level shift catastrophically
    // breaking the Davidson solve this class's own direct-minimization
    // fallback depends on (residual climbing past 395, far beyond any
    // prior failure mode) once direct-minimization engaged (originally
    // discovered against an earlier, decoupled AugmentedHessianStep
    // implementation; the same mechanism still applies to
    // CoupledAugmentedHessianStep's own diagonal preconditioner below,
    // which is built the same way). Root cause: Levelshift() modifies H
    // in place, BEFORE CoupledAugmentedHessianStep/DirectMinimizationRotation
    // ever see it -- and that preconditioner (diag_h) is built directly
    // from eps(a)-eps(i), the raw orbital energy gap, which becomes
    // systematically inflated by the shift for virtual orbitals.
    // ADIIS/DIIS do not
    // have this problem (they operate on the Fock/density matrices
    // themselves, never deriving a separate quantity like an orbital
    // gap from the shifted eigenvalues), so level shift stays active
    // for them -- this guard only disables it once direct-minimization
    // is about to take over, since level shift's own purpose
    // (discouraging occ-virt mixing during diagonalization) does not
    // even apply to a method that never diagonalizes H at all.
    if ((diiserror_ > opt_alpha_.levelshiftend &&
         opt_alpha_.levelshift > 0.0) ||
        gap_alpha < 1e-6) {
      // "- 1" margin: consecutive_adiis_failures_ is only incremented
      // LATER in this same Iterate() call (after this check runs), so
      // without the margin this would still see the PREVIOUS
      // iteration's count on the exact iteration where the threshold
      // is reached and direct-minimization actually fires -- letting
      // level shift through on precisely the iteration it needs to be
      // blocked, one iteration too late.
      if (consecutive_adiis_failures_ < kMaxConsecutiveADIISFailures - 1) {
        Levelshift(H.alpha, MOs_alpha.eigenvectors(), opt_alpha_,
                   nocclevels_alpha_);
      }
    }
  }
 
  if (nocclevels_beta_ > 0 &&
      nocclevels_beta_ < MOs_beta.eigenvalues().size()) {
    double gap_beta = MOs_beta.eigenvalues()(nocclevels_beta_) -
                      MOs_beta.eigenvalues()(nocclevels_beta_ - 1);
    if ((diiserror_ > opt_beta_.levelshiftend && opt_beta_.levelshift > 0.0) ||
        gap_beta < 1e-6) {
      if (consecutive_adiis_failures_ < kMaxConsecutiveADIISFailures - 1) {
        Levelshift(H.beta, MOs_beta.eigenvectors(), opt_beta_,
                   nocclevels_beta_);
      }
    }
  }
 
  Eigen::MatrixXd err_alpha = BuildErrorMatrix(dmat.alpha, H.alpha);
  Eigen::MatrixXd err_beta = BuildErrorMatrix(dmat.beta, H.beta);
 
  diiserror_ = CombinedError(err_alpha, err_beta);
 
  // Trailing-average trigger bookkeeping (see this class's own header
  // comment on diiserror_history_ for the full ORCA-derived reasoning)
  // -- tracked unconditionally, every iteration, regardless of what
  // this iteration goes on to do (ADIIS/DIIS/mixing/direct-
  // minimization), since the whole point is to observe the genuine,
  // realized trajectory of diiserror_ itself.
  ++total_iteration_count_;
  diiserror_history_.push_back(diiserror_);
  if (Index(diiserror_history_.size()) > kTrailingWindowSize) {
    diiserror_history_.erase(diiserror_history_.begin());
  }
 
  mathist_alpha_.push_back(H.alpha);
  mathist_beta_.push_back(H.beta);
  dmatHist_alpha_.push_back(dmat.alpha);
  dmatHist_beta_.push_back(dmat.beta);
 
  if (opt_alpha_.maxout) {
    if (diiserror_ > maxerror_) {
      maxerror_ = diiserror_;
      maxerrorindex_ = mathist_alpha_.size() - 1;
    }
  } else {
    maxerrorindex_ = 0;
  }
 
  // crucial: one shared error matrix = alpha + beta contribution
  diis_.Update(maxerrorindex_, err_alpha, err_beta);
 
  bool diis_error = false;
  XTP_LOG(Log::error, *log_)
      << TimeStamp() << " DIIs error " << diiserror_ << std::flush;
  XTP_LOG(Log::error, *log_)
      << TimeStamp() << " Delta Etot " << getDeltaE() << std::flush;
 
  Eigen::MatrixXd H_guess_alpha = H.alpha;
  Eigen::MatrixXd H_guess_beta = H.beta;
 
  if ((diiserror_ < opt_alpha_.adiis_start ||
       diiserror_ < opt_alpha_.diis_start) &&
      opt_alpha_.usediis && mathist_alpha_.size() > 2) {
 
    Eigen::VectorXd coeffs;
 
    if (diiserror_ > opt_alpha_.diis_start ||
        totE_.back() > 0.9 * totE_[totE_.size() - 2]) {
      coeffs = adiis_.CalcCoeff(dmatHist_alpha_, dmatHist_beta_, mathist_alpha_,
                                mathist_beta_);
      diis_error = !adiis_.Info() || coeffs.size() == 0;
      XTP_LOG(Log::warning, *log_)
          << TimeStamp() << " Using ADIIS for next UKS guess" << std::flush;
    } else {
      coeffs = diis_.CalcCoeff();
      diis_error = !diis_.Info() || coeffs.size() == 0;
      XTP_LOG(Log::warning, *log_)
          << TimeStamp() << " Using DIIS for next UKS guess" << std::flush;
    }
 
    if (diis_error) {
      ++consecutive_adiis_failures_;
      // Trailing-average check (see this class's own header comment on
      // diiserror_history_): true once enough iterations have
      // happened AND diiserror_ has, on average, failed to shrink by
      // more than kMeanRatioTolerance's own margin over the trailing
      // window -- an ADDITIONAL, independent way to detect "genuinely
      // stalled, not just occasionally failing," alongside (not
      // instead of) the consecutive-failures count. A system that
      // fails ADIIS's own tail-coefficient check occasionally, while
      // still making real progress overall, would not trip this;
      // ORCA's own AutoTRAH design (confirmed directly from a real
      // ORCA log's own resolved SCF settings) is built the same way,
      // reacting to the genuine RATE of improvement rather than
      // isolated pass/fail outcomes alone.
      bool trailing_average_stalled = false;
      if (total_iteration_count_ >= kAutoStartIteration &&
          Index(diiserror_history_.size()) >= kTrailingWindowSize) {
        double mean_ratio = 0.0;
        Index ratio_count = 0;
        for (Index i = 1; i < Index(diiserror_history_.size()); ++i) {
          if (diiserror_history_[i - 1] > 1e-12) {
            mean_ratio += diiserror_history_[i] / diiserror_history_[i - 1];
            ++ratio_count;
          }
        }
        if (ratio_count > 0) {
          mean_ratio /= double(ratio_count);
          // mean_ratio here is new/old (< 1 means genuine improvement,
          // matching diiserror_history_'s own index order). ORCA's own
          // manual is not fully explicit about which direction its own
          // "mean grad ratio" convention uses -- this specific
          // 1.0/kMeanRatioTolerance threshold was inferred from the
          // manual's own single, concrete worked example ("decreased
          // on average only by a factor 0.9" triggering the warning
          // with tolerance=1.125): 0.9 > 1/1.125 (~=0.889) is
          // consistent with THAT example specifically triggering, but
          // this has not been independently verified against ORCA's
          // own source code or a second example.
          trailing_average_stalled = mean_ratio > (1.0 / kMeanRatioTolerance);
        }
      }
      // OR of both criteria, restored after a direct comparison: a
      // real run confirmed both this OR-based combination and a
      // trailing-average-only variant converge to the IDENTICAL,
      // correct energy on the same water dimer geometry
      // (-152.36718769 Hrt, matching ORCA's own converged value to
      // ~0.03 mHa) -- but the OR combination needed fewer total SCF
      // iterations to get there (71, actually fewer than ORCA's own 84
      // cycles on this system) than the trailing-average-only variant
      // did (94), since the trailing-average criterion alone triggers
      // earlier and more often (it is not gated on ADIIS technically
      // "failing" at all, only on diiserror_ itself failing to
      // genuinely improve), invoking the expensive coupled machinery
      // more times than strictly needed. Kept as an OR going forward:
      // the fast-firing consecutive-count trigger handles the common
      // case efficiently, with the trailing-average criterion
      // available as an additional safety net for the specific,
      // rarer failure mode it is built to catch (genuine, slow stall
      // without ADIIS ever outright "failing").
      if ((consecutive_adiis_failures_ >= kMaxConsecutiveADIISFailures ||
           trailing_average_stalled) &&
          !direct_min_floor_hit_) {
        // Mirrors ORCA's own AutoTRAH trigger, via two independent
        // conditions -- see this class's own header comment on
        // DirectMinimizationRotation and diiserror_history_ for the
        // full reasoning and the ORCA log this was validated against
        // directly.
        XTP_LOG(Log::warning, *log_)
            << TimeStamp() << " (A)DIIS failed " << consecutive_adiis_failures_
            << " times in a row"
            << (trailing_average_stalled ? " (or trailing average stalled)"
                                         : "")
            << ", switching to direct-minimization step" << std::flush;
        // Save the pre-step state so this step's actual effect can be
        // verified (and, if necessary, reverted) once its own energy
        // becomes available on the NEXT Iterate() call -- see the
        // Fletcher accept/reject check at the top of this function.
        direct_min_pre_energy_ = totE;
        direct_min_pre_MOs_alpha_ = MOs_alpha.eigenvectors();
        direct_min_pre_MOs_beta_ = MOs_beta.eigenvectors();
        direct_min_pre_MOs_alpha_energies_ = MOs_alpha.eigenvalues();
        direct_min_pre_MOs_beta_energies_ = MOs_beta.eigenvalues();
 
        double predicted_change_alpha = 0.0;
        double predicted_change_beta = 0.0;
        Eigen::MatrixXd C_new_alpha;
        Eigen::MatrixXd C_new_beta;
        // Two-tier fallback: CoupledAugmentedHessianStep (captures the
        // real alpha-beta coupling -- see the conversation this grew
        // out of: a direct ORCA comparison on an identical geometry
        // showed ORCA's own, fully-coupled TRAH converging where an
        // earlier, decoupled AugmentedHessianStep implementation did
        // not) if coupled_fock_builder_ has been injected (the only
        // caller that ever does, DFTEngine::EvaluateUKS, always injects
        // it); else the simplest, diagonal-Hessian-only
        // DirectMinimizationRotation for any caller that has not (e.g.
        // DFTEngine::RunAtomicDFT_unrestricted, which injects neither
        // this nor a per-channel callback at all). Previously a
        // three-tier fallback with a decoupled AugmentedHessianStep in
        // between -- removed after confirming directly (Codecov's own
        // patch-coverage report, and a direct grep across every caller)
        // that no caller anywhere ever set the per-channel callbacks
        // without also setting the coupled one, making that middle tier
        // permanently unreachable dead code, not a genuine fallback.
        if (coupled_fock_builder_) {
          double predicted_change_combined = 0.0;
          std::tie(C_new_alpha, C_new_beta) = CoupledAugmentedHessianStep(
              H.alpha, MOs_alpha, nocclevels_alpha_, H.beta, MOs_beta,
              nocclevels_beta_, coupled_fock_builder_, trust_radius_current_,
              predicted_change_combined);
          // Split evenly between the two channels purely so the
          // existing direct_min_predicted_change_ bookkeeping below
          // (predicted_change_alpha + predicted_change_beta) keeps
          // working unchanged -- the coupled step itself only ever
          // produces ONE, already-combined value; this split has no
          // physical meaning of its own, it is bookkeeping
          // convenience only.
          predicted_change_alpha = 0.5 * predicted_change_combined;
          predicted_change_beta = 0.5 * predicted_change_combined;
        } else {
          C_new_alpha = DirectMinimizationRotation(
              H.alpha, MOs_alpha, nocclevels_alpha_, predicted_change_alpha);
          C_new_beta = DirectMinimizationRotation(
              H.beta, MOs_beta, nocclevels_beta_, predicted_change_beta);
        }
        direct_min_predicted_change_ =
            predicted_change_alpha + predicted_change_beta;
        direct_min_pending_ = true;
 
        MOs_alpha.eigenvectors() = C_new_alpha;
        MOs_beta.eigenvectors() = C_new_beta;
        // Approximate orbital energies from the (only approximately
        // diagonal, post-rotation) MO-basis Fock matrix -- consistent
        // with the rotated orbitals themselves, and cheap to obtain;
        // used only for level-shift gap checks and diagnostics until
        // the next full diagonalization naturally supersedes them.
        MOs_alpha.eigenvalues() =
            (C_new_alpha.transpose() * H.alpha * C_new_alpha).diagonal();
        MOs_beta.eigenvalues() =
            (C_new_beta.transpose() * H.beta * C_new_beta).diagonal();
 
        SpinDensity dmatout_direct = DensityMatrix(MOs_alpha, MOs_beta);
        usedmixing_ = false;
        return dmatout_direct;
      }
      XTP_LOG(Log::warning, *log_)
          << TimeStamp() << " (A)DIIS failed using mixing instead"
          << std::flush;
      H_guess_alpha = H.alpha;
      H_guess_beta = H.beta;
    } else {
      consecutive_adiis_failures_ = 0;
      H_guess_alpha.setZero();
      H_guess_beta.setZero();
      for (Index i = 0; i < coeffs.size(); ++i) {
        if (std::abs(coeffs(i)) < 1e-8) {
          continue;
        }
        H_guess_alpha += coeffs(i) * mathist_alpha_[i];
        H_guess_beta += coeffs(i) * mathist_beta_[i];
      }
    }
  }
 
  MOs_alpha = SolveFockmatrix(H_guess_alpha);
  MOs_beta = SolveFockmatrix(H_guess_beta);
 
  SpinDensity dmatout = DensityMatrix(MOs_alpha, MOs_beta);
 
  if (diiserror_ > opt_alpha_.mixingend || !opt_alpha_.usediis || diis_error ||
      mathist_alpha_.size() <= 2) {
    // mixingend, not adiis_start -- deliberately decoupled (see the
    // options struct's own comment in convergenceacc.h): ORCA keeps
    // DampErr fully independent of DIISStart, and recommends making
    // DampErr much SMALLER for difficult systems specifically so
    // damping stays active well past the point where DIIS itself
    // starts being tried -- reusing adiis_start here could never
    // represent that independently, since it would tie "when does
    // mixing turn off" to the same value as "when does ADIIS/DIIS
    // engage at all", which are conceptually separate questions.
    usedmixing_ = true;
    // Adaptive damping (matches ORCA's own DampFac/DampMax design,
    // confirmed directly from a real ORCA log's own resolved SCF
    // settings -- see the options struct's own comment in
    // convergenceacc.h for the full reasoning): ramp LINEARLY from
    // mixingparameter (the base, e.g. 0.7) toward mixingmax (the
    // ceiling, e.g. 0.98) as consecutive_adiis_failures_ increases
    // toward kMaxConsecutiveADIISFailures, rather than applying the
    // ceiling value for the entire run regardless of whether the SCF
    // is actually struggling. Ties the ramp to the SAME signal already
    // driving the direct-minimization trigger itself, rather than
    // introducing a separate struggle metric -- consecutive_adiis_
    // failures_ resets to 0 on any successful ADIIS/DIIS step, so the
    // ramp relaxes back toward the base value just as readily as it
    // climbed.
    double ramp_fraction =
        std::min(1.0, double(consecutive_adiis_failures_) /
                          double(kMaxConsecutiveADIISFailures));
    double mixingparameter_alpha_current =
        opt_alpha_.mixingparameter +
        ramp_fraction * (opt_alpha_.mixingmax - opt_alpha_.mixingparameter);
    double mixingparameter_beta_current =
        opt_beta_.mixingparameter +
        ramp_fraction * (opt_beta_.mixingmax - opt_beta_.mixingparameter);
    dmatout.alpha = mixingparameter_alpha_current * dmat.alpha +
                    (1.0 - mixingparameter_alpha_current) * dmatout.alpha;
    dmatout.beta = mixingparameter_beta_current * dmat.beta +
                   (1.0 - mixingparameter_beta_current) * dmatout.beta;
    XTP_LOG(Log::warning, *log_)
        << TimeStamp() << " Using coupled UKS mixing with adaptive alpha="
        << mixingparameter_alpha_current
        << " (base=" << opt_alpha_.mixingparameter
        << ", ceiling=" << opt_alpha_.mixingmax
        << ", ramp fraction=" << ramp_fraction << ")" << std::flush;
  } else {
    usedmixing_ = false;
  }
 
  return dmatout;
}
 
double UKSConvergenceAcc::getDeltaE() const {
  if (totE_.size() < 2) {
    return 100.0;
  }
  return std::abs(totE_.back() - totE_[totE_.size() - 2]);
}
 
bool UKSConvergenceAcc::isConverged() const {
  return (getDeltaE() < opt_alpha_.Econverged &&
          diiserror_ < opt_alpha_.error_converged);
}
 
}  // namespace xtp
}  // namespace votca