LORRotation Class Reference

#include <LORRotation.h>

Inheritance diagram for LORRotation:

Public Member Functions
	LORRotation (std::shared_ptr< Matter > matter, const Parameters &params, std::shared_ptr< Potential > pot)
	~LORRotation ()=default
void	compute (std::shared_ptr< Matter > matter, AtomMatrix initialDirection)
double	getEigenvalue ()
AtomMatrix	getEigenvector ()
Public Member Functions inherited from eonc::LowestEigenmode
	LowestEigenmode (std::shared_ptr< Potential > potPassed, const Parameters &parameters)
	~LowestEigenmode ()=default

Static Public Member Functions
static VectorXd	translateHUnitOrthoP3 (const VectorXd &N, const VectorXd &Theta, const VectorXd &P, const VectorXd &HN, const VectorXd &HTheta, const VectorXd &HP)
	Force-translation identity for H·P3 when P3 is the unit Gram-Schmidt residual of P against orthonormal {N, Θ} and H is linear: P_ortho = P - (N·P) N - (Θ·P) Θ P3 = P_ortho / ||P_ortho|| H P3 = (H P - (N·P) H N - (Θ·P) H Θ) / ||P_ortho|| (Not Gram-Schmidt on HP in ambient space.) Returns zero vector if ||P_ortho|| is negligible.

Public Attributes
std::vector< double >	curvatureHistory
bool	convergedOnResidual {false}
Public Attributes inherited from eonc::LowestEigenmode
long	totalForceCalls {0}
double	statsTorque {0.0}
double	statsCurvature {0.0}
double	statsAngle {0.0}
long	statsRotations {0}
long	totalIterations {0}

Private Attributes
eonc::log::Scoped	log
VectorXd	eigenvector
double	eigenvalue {0.0}
std::shared_ptr< Matter >	x0
std::shared_ptr< Matter >	x1

Additional Inherited Members
Static Public Attributes inherited from eonc::LowestEigenmode
static const char	MINMODE_DIMER [] = "dimer"
static const char	MINMODE_GPRDIMER [] = "gprdimer"
static const char	MINMODE_LANCZOS [] = "lanczos"
static const char	MINMODE_DAVIDSON [] = "davidson"
Protected Attributes inherited from eonc::LowestEigenmode
std::shared_ptr< Potential >	pot
const Parameters &	params

Detailed Description

Definition at line 25 of file LORRotation.h.

Constructor & Destructor Documentation

LORRotation()

LORRotation::LORRotation	(	std::shared_ptr< Matter >	matter,
		const Parameters &	params,
		std::shared_ptr< Potential >	pot )

Definition at line 29 of file LORRotation.cpp.

    : LowestEigenmode(pot, params) {
  auto x1Pot = (pot->needsPerImageInstance() && params.main_options.parallel)
                   ? eonc::helpers::makePotential(params)
                   : pot;
  x0 = std::make_shared<Matter>(pot, params);
  x1 = std::make_shared<Matter>(x1Pot, params);
  *x0 = *matter;
  *x1 = *matter;
  totalForceCalls = 0;
  statsRotations = 0;
  eigenvector.resize(3 * matter->numberOfAtoms());
  eigenvector.setZero();
}

~LORRotation()

eonc::LORRotation::~LORRotation ( )

default

Member Function Documentation

compute()

void LORRotation::compute	(	std::shared_ptr< Matter >	matter,
		AtomMatrix	initialDirection )

Definition at line 61 of file LORRotation.cpp.

                                                                 {
  const int dim = static_cast<int>(3 * matter->numberOfAtoms());
  const VectorXd freeMask = matter->getFreeV();
  totalForceCalls = 0;
  statsRotations = 0;
  curvatureHistory.clear();
  convergedOnResidual = false;
  eigenvalue = 0.0;
  eigenvector = VectorXd::Zero(dim);
 
  // Fully fixed system: no free DOF — avoid normalize() NaN.
  if (freeMask.norm() < 1e-14 || matter->numberOfFreeAtoms() == 0) {
    QUILL_LOG_WARNING(log, "[LOR] no free atoms; skipping rotation");
    return;
  }
 
  VectorXd N = VectorXd::Map(initialDirectionAtomMatrix.data(), dim);
  N = N.array() * freeMask.array();
  if (N.norm() < 1e-10) {
    N.setRandom();
    N = N.array() * freeMask.array();
  }
  if (N.norm() < 1e-14) {
    QUILL_LOG_WARNING(log, "[LOR] free mask yields zero direction; skip");
    return;
  }
  N.normalize();
 
  *x0 = *matter;
  *x1 = *matter;
  const VectorXd x0_r = x0->getPositionsV();
  const double delta = params.main_options.finiteDifference;
 
  // rotations_max <= 0 → Parameters default (10); no silent upper clamp.
  const long rotBudget = params.dimer_options.rotations_max > 0
                             ? params.dimer_options.rotations_max
                             : 10;
  const int rotmax = static_cast<int>(std::max<long>(1, rotBudget));
 
  // Dedicated LOR residual tolerance (not classical torque_min).
  const double residualTol =
      std::max(1e-3, params.dimer_options.lor_residual_tol);
  auto relativeResidual = [](double fnorm, double cn) {
    return fnorm / (std::abs(cn) + 1.0);
  };
 
  double bestCN = std::numeric_limits<double>::infinity();
  VectorXd bestN = N;
  VectorXd bestHN = VectorXd::Zero(dim);
 
  // Cache center forces once (paper: FD products reuse F(R0)).
  const VectorXd F0 = x0->getForcesV();
  totalForceCalls += 1;
 
  auto applyMask = [&](VectorXd &v) { v = v.array() * freeMask.array(); };
 
  auto unitize = [&](VectorXd &v) -> double {
    applyMask(v);
    const double n = v.norm();
    if (n > 1e-14) {
      v /= n;
    }
    return n;
  };
 
  // One new force at R0+δ·dir; F0 cached (Algorithm I: ≤1 new FD per rotation).
  auto hessianAlong = [&](const VectorXd &v) -> VectorXd {
    VectorXd dir = v.array() * freeMask.array();
    const double nrm = dir.norm();
    if (nrm < 1e-14) {
      return VectorXd::Zero(dim);
    }
    dir /= nrm;
    x1->setPositionsV(x0_r + delta * dir);
    const VectorXd F1 = x1->getForcesV();
    totalForceCalls += 1;
    VectorXd Hv = -(F1 - F0) / delta;
    applyMask(Hv);
    return Hv * nrm;
  };
 
  auto trackBest = [&](double cn, const VectorXd &nVec, const VectorXd &hnVec) {
    if (cn < bestCN) {
      bestCN = cn;
      bestN = nVec;
      bestHN = hnVec;
    }
  };
  // Record Ritz C only when non-increasing (paper quadratic + translation).
  // Strictly non-increasing (1e-4 float). Returns whether the sample was kept
  // (callers must only trackBest on kept samples so ev == min(history)).
  auto appendHistory = [&](double cn) -> bool {
    if (curvatureHistory.empty() || cn <= curvatureHistory.back() + 1e-4) {
      curvatureHistory.push_back(cn);
      return true;
    }
    return false;
  };
 
  // --- Algorithm I start: H N, F_⊥ ---
  VectorXd HN = hessianAlong(N);
  applyMask(HN);
  double CN = N.dot(HN);
  if (appendHistory(CN)) {
    trackBest(CN, N, HN);
  }
 
  VectorXd F = HN - CN * N;
  applyMask(F);
  double Fnorm = F.norm();
 
  QUILL_LOG_INFO(
      log, "[LOR] iter=0 ||F_perp||={:.6e} C_N={:.6f} (Algorithm I start)",
      Fnorm, CN);
 
  if (relativeResidual(Fnorm, CN) < residualTol) {
    convergedOnResidual = true;
    eigenvalue = CN;
    eigenvector = N;
    QUILL_LOG_INFO(log, "[LOR] converged on residual at start");
    return;
  }
 
  VectorXd Theta = F / Fnorm;
  VectorXd HTheta = hessianAlong(Theta); // iteration-1 second FD (H Θ)
  applyMask(HTheta);
 
  // 2×2 Ritz in span{N, Θ}
  Eigen::Matrix2d A2;
  A2(0, 0) = N.dot(HN);
  A2(0, 1) = N.dot(HTheta);
  A2(1, 0) = A2(0, 1);
  A2(1, 1) = Theta.dot(HTheta);
  Eigen::SelfAdjointEigenSolver<Eigen::Matrix2d> es2(A2);
  Eigen::Vector2d coeffs = es2.eigenvectors().col(0);
  double a = coeffs(0);
  double b = coeffs(1);
 
  VectorXd Nlin = a * N + b * Theta;
  const double nN = unitize(Nlin);
  VectorXd HNlin = a * HN + b * HTheta;
  if (nN > 1e-14) {
    HNlin /= nN;
  }
  N = Nlin;
  HN = HNlin;
 
  VectorXd P = Theta;
  VectorXd HP = HTheta;
 
  CN = N.dot(HN);
  if (appendHistory(CN)) {
    trackBest(CN, N, HN);
  }
  F = HN - CN * N;
  applyMask(F);
  Fnorm = F.norm();
  statsRotations = 1;
 
  QUILL_LOG_INFO(
      log, "[LOR] iter=1 (2x2) ||F_perp||={:.6e} C_N={:.6f} a={:.4f} b={:.4f}",
      Fnorm, CN, a, b);
 
  for (int k = 2; k <= rotmax; ++k) {
    if (relativeResidual(Fnorm, CN) < residualTol) {
      convergedOnResidual = true;
      QUILL_LOG_INFO(log,
                     "[LOR] converged residual iter={} ||F||={:.6e} rel={:.6e}",
                     k, Fnorm, relativeResidual(Fnorm, CN));
      break;
    }
 
    Theta = F / Fnorm;
    // Exactly one new FD this iteration: H · Θ (force translation for N, P)
    HTheta = hessianAlong(Theta);
    applyMask(HTheta);
 
    // Orthonormalize P vs N,Θ for stable 3×3 (intentional vs paper GEP on B≠I)
    VectorXd P3 = P - N.dot(P) * N - Theta.dot(P) * Theta;
    applyMask(P3);
    const double pNrm = P3.norm();
 
    auto applyRitz2 = [&]() {
      Eigen::Matrix2d A2b;
      A2b(0, 0) = N.dot(HN);
      A2b(0, 1) = N.dot(HTheta);
      A2b(1, 0) = A2b(0, 1);
      A2b(1, 1) = Theta.dot(HTheta);
      Eigen::SelfAdjointEigenSolver<Eigen::Matrix2d> es2b(A2b);
      Eigen::Vector2d c2 = es2b.eigenvectors().col(0);
      a = c2(0);
      b = c2(1);
      VectorXd Nnew = a * N + b * Theta;
      VectorXd HNnew = a * HN + b * HTheta;
      const double nn = unitize(Nnew);
      if (nn > 1e-14) {
        HNnew /= nn;
      }
      const double cNew = Nnew.dot(HNnew);
      if (!curvatureHistory.empty() && cNew > curvatureHistory.back() + 1e-4) {
        QUILL_LOG_INFO(log,
                       "[LOR] curvature stall (2x2 fallback) iter={} "
                       "C_prev={:.6f} C_new={:.6f} (reject)",
                       k, curvatureHistory.back(), cNew);
        N = bestN;
        HN = bestHN;
        CN = bestCN;
        F = HN - CN * N;
        applyMask(F);
        Fnorm = F.norm();
        statsRotations = k;
        return false;
      }
      P = Theta;
      HP = HTheta;
      N = Nnew;
      HN = HNnew;
      CN = cNew;
      if (appendHistory(CN)) {
        trackBest(CN, N, HN);
      }
      F = HN - CN * N;
      applyMask(F);
      Fnorm = F.norm();
      statsRotations = k;
      return true;
    };
 
    if (pNrm < 1e-8) {
      if (!applyRitz2()) {
        break;
      }
      continue;
    }
    P3 /= pNrm;
    // H·P3 by force-translation linearity (not ambient GS on HP):
    // H P3 = (HP - (N·P) HN - (Θ·P) HΘ) / ||P_ortho|| with P3 = P_ortho/||...||
    VectorXd HP3 = translateHUnitOrthoP3(N, Theta, P, HN, HTheta, HP);
    applyMask(HP3);
    if (HP3.norm() < 1e-14) {
      if (!applyRitz2()) {
        break;
      }
      continue;
    }
 
    Eigen::Matrix3d A3 = Eigen::Matrix3d::Zero();
    const VectorXd basis[3] = {N, Theta, P3};
    const VectorXd Hbasis[3] = {HN, HTheta, HP3};
    for (int i = 0; i < 3; ++i) {
      for (int j = i; j < 3; ++j) {
        A3(i, j) = basis[i].dot(Hbasis[j]);
        A3(j, i) = A3(i, j);
      }
    }
    A3 = 0.5 * (A3 + A3.transpose());
    Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> ges(A3);
    if (ges.info() != Eigen::Success) {
      QUILL_LOG_WARNING(log, "[LOR] 3x3 eigen failed at iter={}; stop", k);
      break;
    }
    const Eigen::Vector3d c3 = ges.eigenvectors().col(0);
    a = c3(0);
    b = c3(1);
    const double c = c3(2);
 
    // Save pre-update for stall revert without FD
    const VectorXd Nprev = N;
    const VectorXd HNprev = HN;
    const double CNprev = CN;
 
    VectorXd Nnew = a * N + b * Theta + c * P3;
    VectorXd Pnew = b * Theta + c * P3;
    VectorXd HNnew = a * HN + b * HTheta + c * HP3;
    VectorXd HPnew = b * HTheta + c * HP3;
 
    const double nNew = unitize(Nnew);
    if (nNew > 1e-14) {
      HNnew /= nNew;
    }
    const double pNew = unitize(Pnew);
    if (pNew > 1e-14) {
      HPnew /= pNew;
    }
 
    const double CNnew = Nnew.dot(HNnew);
    if (!curvatureHistory.empty() && CNnew > curvatureHistory.back() + 1e-4) {
      QUILL_LOG_INFO(log,
                     "[LOR] curvature stall iter={} C_prev={:.6f} C_new={:.6f} "
                     "(reject update; keep prior mode, no extra FD)",
                     k, curvatureHistory.back(), CNnew);
      N = bestN;
      HN = bestHN;
      CN = bestCN;
      F = HN - CN * N;
      applyMask(F);
      Fnorm = F.norm();
      statsRotations = k;
      break;
    }
 
    N = Nnew;
    P = Pnew;
    HN = HNnew;
    HP = HPnew;
    CN = CNnew;
    if (appendHistory(CN)) {
      trackBest(CN, N, HN);
    }
    (void)Nprev;
    (void)HNprev;
    (void)CNprev;
 
    F = HN - CN * N;
    applyMask(F);
    Fnorm = F.norm();
    statsRotations = k;
 
    QUILL_LOG_INFO(log,
                   "[LOR] iter={} (3x3) ||F_perp||={:.6e} C_N={:.6f} a={:.4f} "
                   "b={:.4f} c={:.4f}",
                   k, Fnorm, CN, a, b, c);
 
    if (relativeResidual(Fnorm, CN) < residualTol) {
      convergedOnResidual = true;
      break;
    }
  }
 
  // Return best softest mode from accepted Ritz steps (no mandatory final FD).
  N = bestN;
  HN = bestHN;
  CN = bestCN;
  eigenvalue = CN;
  eigenvector = N;
  QUILL_LOG_INFO(log,
                 "[LOR] done rotations={} force_calls={} C_N={:.6f} "
                 "converged_residual={}",
                 statsRotations, totalForceCalls, eigenvalue,
                 convergedOnResidual);
}

getEigenvalue()

double eonc::LORRotation::getEigenvalue ( )

inline

Definition at line 39 of file LORRotation.h.

{ return eigenvalue; }

getEigenvector()

AtomMatrix eonc::LORRotation::getEigenvector ( )

inline

Definition at line 40 of file LORRotation.h.

                              {
    return AtomMatrix::Map(eigenvector.data(), eigenvector.size() / 3, 3);
  }

translateHUnitOrthoP3()

VectorXd LORRotation::translateHUnitOrthoP3 ( const VectorXd & N, const VectorXd & Theta, const VectorXd & P, const VectorXd & HN, const VectorXd & HTheta, const VectorXd & HP )

staticnodiscard

Force-translation identity for H·P3 when P3 is the unit Gram-Schmidt residual of P against orthonormal {N, Θ} and H is linear: P_ortho = P - (N·P) N - (Θ·P) Θ P3 = P_ortho / ||P_ortho|| H P3 = (H P - (N·P) H N - (Θ·P) H Θ) / ||P_ortho|| (Not Gram-Schmidt on HP in ambient space.) Returns zero vector if ||P_ortho|| is negligible.

Shipped LOR 3×3 path uses this helper.

Definition at line 47 of file LORRotation.cpp.

                                                                               {
  // P_ortho = (I - N Nᵀ - Θ Θᵀ) P  with {N, Θ} orthonormal
  const VectorXd P_ortho = P - N.dot(P) * N - Theta.dot(P) * Theta;
  const double pNrm = P_ortho.norm();
  if (pNrm < 1e-14) {
    return VectorXd::Zero(P.size());
  }
  // H P_ortho = HP - (N·P) HN - (Θ·P) HΘ  (linearity of H — not GS on HP)
  const VectorXd HPortho = HP - N.dot(P) * HN - Theta.dot(P) * HTheta;
  return HPortho / pNrm; // = H (P_ortho / ||P_ortho||) = H P3
}

Member Data Documentation

convergedOnResidual

bool eonc::LORRotation::convergedOnResidual {false}

Definition at line 46 of file LORRotation.h.

{false};

curvatureHistory

std::vector<double> eonc::LORRotation::curvatureHistory

Definition at line 45 of file LORRotation.h.

eigenvalue

double eonc::LORRotation::eigenvalue {0.0}

private

Definition at line 29 of file LORRotation.h.

{0.0};

eigenvector

VectorXd eonc::LORRotation::eigenvector

private

Definition at line 28 of file LORRotation.h.

log

eonc::log::Scoped eonc::LORRotation::log

private

Definition at line 27 of file LORRotation.h.

x0

std::shared_ptr<Matter> eonc::LORRotation::x0

private

Definition at line 30 of file LORRotation.h.

x1

std::shared_ptr<Matter> eonc::LORRotation::x1

private

Definition at line 31 of file LORRotation.h.

---

The documentation for this class was generated from the following files:

• /home/runner/work/eOn/eOn/include/eon/LORRotation.h
• /home/runner/work/eOn/eOn/client/LORRotation.cpp