eigen/test/tridiagonal_eigensolver.cpp

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2026 Rasmus Munk Larsen <rmlarsen@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// SPDX-License-Identifier: MPL-2.0

#include "main.h"
#include "tridiag_test_matrices.h"
#include <limits>
#include <Eigen/Eigenvalues>

// Test TridiagonalEigenSolver (SIMD Sturm-sequence spectral bisection) on the full
// structured-tridiagonal catalog: compare against the implicit-QR path of
// SelfAdjointEigenSolver::computeFromTridiagonal(), exercise index/value range subset
// selection, and verify absolute accuracy against matrices with known spectra.
template <typename RealScalar>
void tridiagonal_eigensolver_bisection() {
  typedef Matrix<RealScalar, Dynamic, Dynamic> MatrixType;
  typedef Matrix<RealScalar, Dynamic, 1> VectorType;
  const RealScalar eps = NumTraits<RealScalar>::epsilon();
  const RealScalar tiny = (std::numeric_limits<RealScalar>::min)();

  // (a) Compare bisection against the QR path over every structured matrix, and exercise
  // index- and value-range subset selection.
  test::for_all_symmetric_tridiag_test_matrices<RealScalar>([&](const VectorType& diag, const VectorType& offdiag) {
    const Index n = diag.size();

    SelfAdjointEigenSolver<MatrixType> qr;
    qr.computeFromTridiagonal(diag, offdiag, EigenvaluesOnly);
    if (qr.info() != Success) return;  // skip pathological inputs the QR path itself rejects
    const VectorType w = qr.eigenvalues();

    TridiagonalEigenSolver<RealScalar> bz;
    bz.computeEigenvalues(diag, offdiag);
    VERIFY_IS_EQUAL(bz.info(), Success);
    VERIFY_IS_EQUAL(bz.eigenvalues().size(), n);
    for (Index i = 1; i < n; ++i) VERIFY(bz.eigenvalues()(i) >= bz.eigenvalues()(i - 1));

    // Bisection has an absolute accuracy floor of ~pivmin (the smallest safe pivot),
    // so the tolerance carries an absolute term in addition to the eps*||T|| term.
    const RealScalar radius = w.cwiseAbs().maxCoeff();
    const RealScalar tol = RealScalar(64) * RealScalar(n) * (eps * radius + tiny);
    VERIFY((bz.eigenvalues() - w).cwiseAbs().maxCoeff() <= tol);

    // Index-range subsets: the k smallest and the k largest.
    if (n >= 4) {
      const Index k = n / 3;
      TridiagonalEigenSolver<RealScalar> lo, hi;
      lo.computeEigenvalues(diag, offdiag, EigenvalueRange::indices(0, k));
      VERIFY_IS_EQUAL(lo.eigenvalues().size(), k);
      VERIFY((lo.eigenvalues() - w.head(k)).cwiseAbs().maxCoeff() <= tol);
      hi.computeEigenvalues(diag, offdiag, EigenvalueRange::indices(n - k, n));
      VERIFY_IS_EQUAL(hi.eigenvalues().size(), k);
      VERIFY((hi.eigenvalues() - w.tail(k)).cwiseAbs().maxCoeff() <= tol);
    }

    // Value-range subset [vl, vu) about the spectrum center. The count is compared strictly
    // only when no eigenvalue sits within tol of a boundary (otherwise it is legitimately
    // ambiguous which side the boundary eigenvalue falls on).
    if (n >= 2 && radius > tiny) {
      const RealScalar vl = -RealScalar(0.3) * radius, vu = RealScalar(0.3) * radius;
      TridiagonalEigenSolver<RealScalar> bv;
      bv.computeEigenvalues(diag, offdiag, EigenvalueRange::values(double(vl), double(vu)));
      for (Index i = 0; i < bv.eigenvalues().size(); ++i)
        VERIFY(bv.eigenvalues()(i) >= vl - tol && bv.eigenvalues()(i) < vu + tol);
      const bool clean = (w.array() - vl).abs().minCoeff() > tol && (w.array() - vu).abs().minCoeff() > tol;
      if (clean) {
        const Index expected = (w.array() >= vl && w.array() < vu).count();
        VERIFY_IS_EQUAL(bv.eigenvalues().size(), expected);
        if (expected > 0) {
          Index first = 0;
          while (first < n && w(first) < vl) ++first;
          VERIFY((bv.eigenvalues() - w.segment(first, expected)).cwiseAbs().maxCoeff() <= tol);
        }
      }
    }
  });

  // (a2) Half-open value-range convention, fixed-size inputs, and input validation.
  {
    // values(vl, vu) selects the eigenvalues in the lower-closed, upper-open interval [vl, vu) (like
    // indices()): an eigenvalue exactly equal to vl is included, one exactly equal to vu is not. Use a
    // diagonal matrix (zero off-diagonal) with the exact integer spectrum {1, 2, 3} so the endpoints
    // land precisely on eigenvalues.
    VectorType d(3), e(2);
    d << RealScalar(1), RealScalar(2), RealScalar(3);
    e.setZero();
    TridiagonalEigenSolver<RealScalar> bmid;
    bmid.computeEigenvalues(d, e, EigenvalueRange::values(1.0L, 3.0L));
    VERIFY_IS_EQUAL(bmid.eigenvalues().size(), Index(2));  // [1, 3) = {1, 2}: lower end closed, upper end open
    VERIFY_IS_APPROX(bmid.eigenvalues()(0), RealScalar(1));
    VERIFY_IS_APPROX(bmid.eigenvalues()(1), RealScalar(2));
    TridiagonalEigenSolver<RealScalar> bopen;
    bopen.computeEigenvalues(d, e, EigenvalueRange::values(0.0L, 1.0L));
    VERIFY_IS_EQUAL(bopen.eigenvalues().size(), Index(0));  // [0, 1) excludes the eigenvalue at the open end 1
    TridiagonalEigenSolver<RealScalar> blow;
    blow.computeEigenvalues(d, e, EigenvalueRange::values(1.0L, 2.0L));
    VERIFY_IS_EQUAL(blow.eigenvalues().size(), Index(1));  // [1, 2) = {1}: includes the eigenvalue at the closed end 1
    VERIFY_IS_APPROX(blow.eigenvalues()(0), RealScalar(1));

    // Fixed-size input vectors work, including subset selection (the solver's own storage is
    // dynamic, so a subset shorter than the input is not a problem).
    Matrix<RealScalar, 3, 1> fd;
    fd << RealScalar(1), RealScalar(2), RealScalar(3);
    Matrix<RealScalar, 2, 1> fe;
    fe.setZero();
    TridiagonalEigenSolver<RealScalar> fz;
    fz.computeEigenvalues(fd, fe);
    VERIFY_IS_EQUAL(fz.eigenvalues().size(), Index(3));
    fz.computeEigenvalues(fd, fe, EigenvalueRange::indices(0, 1));
    VERIFY_IS_EQUAL(fz.eigenvalues().size(), Index(1));
    VERIFY_IS_APPROX(fz.eigenvalues()(0), RealScalar(1));

    // Querying an uninitialized solver is a usage error.
    TridiagonalEigenSolver<RealScalar> uninit;
    VERIFY_RAISES_ASSERT(uninit.eigenvalues());
    VERIFY_RAISES_ASSERT(uninit.info());

    // A non-finite input (NaN or Inf, diagonal or sub-diagonal) is rejected up front and
    // reported via info().
    VectorType dbad = d;
    dbad(1) = std::numeric_limits<RealScalar>::quiet_NaN();
    TridiagonalEigenSolver<RealScalar> bnan;
    bnan.computeEigenvalues(dbad, e);
    VERIFY_IS_EQUAL(bnan.info(), NoConvergence);
    dbad(1) = std::numeric_limits<RealScalar>::infinity();
    bnan.computeEigenvalues(dbad, e);
    VERIFY_IS_EQUAL(bnan.info(), NoConvergence);
    VectorType ebad = e;
    ebad(0) = std::numeric_limits<RealScalar>::quiet_NaN();
    bnan.computeEigenvalues(d, ebad);
    VERIFY_IS_EQUAL(bnan.info(), NoConvergence);
  }

  // (b) Absolute-accuracy checks against matrices with known spectra (independent of QR).
  // A subnormal-magnitude matrix exercises the normalization: dividing entries by the (subnormal) max
  // magnitude keeps the scaled matrix O(1), whereas multiplying by 1/scale would overflow to infinity
  // and return wrong eigenvalues. Compare against the same matrix in a normal range, scaled back down.
  {
    const Index nn = 7;
    VectorType bd(nn), be(nn - 1);
    test::tridiag_1_2_1(bd, be);
    const RealScalar sub_scale = (std::numeric_limits<RealScalar>::min)() / RealScalar(64);  // subnormal
    TridiagonalEigenSolver<RealScalar> bref, bsub;
    bref.computeEigenvalues(bd, be);
    bsub.computeEigenvalues(VectorType(bd * sub_scale), VectorType(be * sub_scale));
    VERIFY_IS_EQUAL(bsub.info(), Success);
    // The subnormal eigenvalues, scaled back to O(1), agree with the reference to the subnormal
    // granularity at this magnitude (denorm_min / sub_scale) plus the usual bisection error.
    const RealScalar radius = bref.eigenvalues().cwiseAbs().maxCoeff();
    const RealScalar gran = (std::numeric_limits<RealScalar>::denorm_min)() / sub_scale;
    VERIFY((bsub.eigenvalues() / sub_scale - bref.eigenvalues()).cwiseAbs().maxCoeff() <=
           RealScalar(16) * (RealScalar(nn) * eps + gran) * radius);
  }
  const double pi = 3.14159265358979323846;
  for (Index n : {7, 16, 33, 64}) {
    VectorType d(n), e(n - 1), wexact(n);

    // 1-2-1 Toeplitz: lambda_k = 2 - 2 cos(k*pi/(n+1)), ascending for k = 1..n.
    test::tridiag_1_2_1(d, e);
    TridiagonalEigenSolver<RealScalar> b121;
    b121.computeEigenvalues(d, e);
    for (Index k = 0; k < n; ++k) wexact(k) = RealScalar(2.0 - 2.0 * std::cos(double(k + 1) * pi / double(n + 1)));
    VERIFY((b121.eigenvalues() - wexact).cwiseAbs().maxCoeff() <= RealScalar(64) * RealScalar(n) * eps * RealScalar(4));

    // Clement / Kac: integer spectrum -(n-1), -(n-3), ..., (n-1).
    test::tridiag_clement(d, e);
    TridiagonalEigenSolver<RealScalar> bcl;
    bcl.computeEigenvalues(d, e);
    for (Index k = 0; k < n; ++k) wexact(k) = RealScalar(-(n - 1) + 2 * k);
    VERIFY((bcl.eigenvalues() - wexact).cwiseAbs().maxCoeff() <= RealScalar(64) * RealScalar(n) * eps * RealScalar(n));

    // Spectra exactly symmetric about 0: signed Wilkinson, Hermite, Legendre, [1,0,1] Toeplitz.
    auto check_symmetric = [&](const VectorType& dd, const VectorType& ee) {
      TridiagonalEigenSolver<RealScalar> bs;
      bs.computeEigenvalues(dd, ee);
      const VectorType s = bs.eigenvalues();
      const RealScalar rad = s.cwiseAbs().maxCoeff();
      const RealScalar tol = RealScalar(64) * RealScalar(n) * (eps * rad + tiny);
      for (Index i = 0; i < n; ++i) VERIFY(numext::abs(s(i) + s(n - 1 - i)) <= tol);
    };
    test::tridiag_wilkinson_signed(d, e);
    check_symmetric(d, e);
    test::tridiag_hermite(d, e);
    check_symmetric(d, e);
    test::tridiag_legendre(d, e);
    check_symmetric(d, e);
    test::tridiag_toeplitz(d, e);  // default a = 1, b = 0
    check_symmetric(d, e);
  }
}

// Test the inverse-iteration eigenvector stage (LAPACK xSTEIN analog): staged
// (computeEigenvalues() followed by computeEigenvectors()), one-call compute(), and the direct
// computeEigenvectors(diag, subdiag, eigenvalues) API.
template <typename RealScalar>
void tridiagonal_eigensolver_eigenvectors() {
  typedef Matrix<RealScalar, Dynamic, Dynamic> MatrixType;
  typedef Matrix<RealScalar, Dynamic, 1> VectorType;
  const RealScalar eps = NumTraits<RealScalar>::epsilon();
  const RealScalar tiny = (std::numeric_limits<RealScalar>::min)();
  const double pi = 3.14159265358979323846;

  // (a) Eigenvectors over the structured catalog: staged and direct APIs.
  test::for_all_symmetric_tridiag_test_matrices<RealScalar>([&](const VectorType& diag, const VectorType& offdiag) {
    const Index n = diag.size();

    // Dense form of T for residual / reconstruction checks.
    MatrixType T = MatrixType::Zero(n, n);
    T.diagonal() = diag;
    if (n > 1) {
      T.diagonal(-1) = offdiag;
      T.diagonal(1) = offdiag;
    }
    RealScalar scale = T.cwiseAbs().maxCoeff();
    if (!(numext::isfinite)(scale)) return;  // skip non-finite inputs, like the eigenvalue path
    if (numext::is_exactly_zero(scale)) scale = RealScalar(1);

    TridiagonalEigenSolver<RealScalar> es;
    es.computeEigenvalues(diag, offdiag);
    if (es.info() != Success) return;
    es.computeEigenvectors();  // staged inverse-iteration pass
    VERIFY_IS_EQUAL(es.info(), Success);
    const VectorType w = es.eigenvalues();
    const MatrixType V = es.eigenvectors();
    VERIFY_IS_EQUAL(V.rows(), n);
    VERIFY_IS_EQUAL(V.cols(), n);

    const RealScalar tol = RealScalar(128) * RealScalar(n) * (eps * scale + tiny);
    const RealScalar otol = RealScalar(128) * RealScalar(n) * eps;
    // Per-column residual; stableNorm() since the catalog includes near-overflow entries.
    for (Index i = 0; i < n; ++i) VERIFY((T * V.col(i) - w(i) * V.col(i)).stableNorm() <= tol);
    // Orthonormality and full-spectrum reconstruction (the latter in scaled coordinates so the
    // extreme-magnitude catalog entries cannot overflow the products).
    VERIFY((V.transpose() * V - MatrixType::Identity(n, n)).cwiseAbs().maxCoeff() <= otol);
    const RealScalar recon =
        ((V * (w / scale).asDiagonal() * V.transpose()) - (T / scale)).cwiseAbs().maxCoeff() * scale;
    VERIFY(recon <= tol);

    // The one-call compute() reproduces the staged result exactly.
    TridiagonalEigenSolver<RealScalar> onecall;
    onecall.compute(diag, offdiag);
    VERIFY_IS_EQUAL(onecall.info(), Success);
    VERIFY_IS_EQUAL((onecall.eigenvalues() - w).cwiseAbs().maxCoeff(), RealScalar(0));
    VERIFY_IS_EQUAL((onecall.eigenvectors() - V).cwiseAbs().maxCoeff(), RealScalar(0));

    // The direct API reproduces the staged result exactly (same eigenvalues -> same deterministic vectors).
    TridiagonalEigenSolver<RealScalar> dir;
    dir.computeEigenvectors(diag, offdiag, w);
    VERIFY_IS_EQUAL(dir.eigenvectors().rows(), n);
    VERIFY_IS_EQUAL(dir.eigenvectors().cols(), n);
    VERIFY_IS_EQUAL((dir.eigenvectors() - V).cwiseAbs().maxCoeff(), RealScalar(0));

    // Index-subset eigenvectors: the bisection range selects a band, inverse iteration produces just
    // those columns; check residual and that they are orthonormal among themselves.
    if (n >= 4) {
      const Index il = n / 4, iu = n - n / 4;
      TridiagonalEigenSolver<RealScalar> sub;
      sub.compute(diag, offdiag, ComputeEigenvectors, EigenvalueRange::indices(il, iu));
      const MatrixType Vs = sub.eigenvectors();
      const VectorType ws = sub.eigenvalues();
      VERIFY_IS_EQUAL(Vs.rows(), n);
      VERIFY_IS_EQUAL(Vs.cols(), iu - il);
      for (Index k = 0; k < iu - il; ++k) VERIFY((T * Vs.col(k) - ws(k) * Vs.col(k)).stableNorm() <= tol);
      VERIFY((Vs.transpose() * Vs - MatrixType::Identity(iu - il, iu - il)).cwiseAbs().maxCoeff() <= otol);
    }
  });

  // (b) Closed-form 1-2-1 Toeplitz eigenvectors: v_k(j) = sin(j*(n-k)*pi/(n+1)) for the k-th
  // (ascending) eigenvalue. An analytic, solver-independent check of the inverse-iteration vectors.
  for (Index n : {5, 16, 33, 64}) {
    VectorType d(n), e(n - 1);
    test::tridiag_1_2_1(d, e);
    TridiagonalEigenSolver<RealScalar> es(d, e);
    const MatrixType V = es.eigenvectors();
    for (Index k = 0; k < n; ++k) {
      VectorType exact(n);
      for (Index j = 0; j < n; ++j) exact(j) = RealScalar(std::sin(double(j + 1) * double(n - k) * pi / double(n + 1)));
      exact.normalize();
      const RealScalar err = (std::min)((V.col(k) - exact).norm(), (V.col(k) + exact).norm());
      VERIFY(err <= RealScalar(256) * RealScalar(n) * eps);
    }
  }

  // (c) Edge cases: fixed-size inputs, eigenvalues-only requests, subnormal-magnitude matrices,
  // and convergence reporting.
  {
    // Fixed-size input vectors work for the full eigendecomposition and for subsets (the solver's
    // own storage is dynamic).
    Matrix<RealScalar, 4, 1> fdiag = Matrix<RealScalar, 4, 1>::Random();
    Matrix<RealScalar, 3, 1> fsub = Matrix<RealScalar, 3, 1>::Random();
    TridiagonalEigenSolver<RealScalar> fz;
    fz.compute(fdiag, fsub);
    VERIFY_IS_EQUAL(fz.info(), Success);
    VERIFY_IS_EQUAL(fz.eigenvectors().cols(), Index(4));
    {
      MatrixType Tf = MatrixType::Zero(4, 4);
      Tf.diagonal() = fdiag;
      Tf.diagonal(-1) = fsub;
      Tf.diagonal(1) = fsub;
      const MatrixType Vf = fz.eigenvectors();
      VERIFY((Vf.transpose() * Vf - MatrixType::Identity(4, 4)).cwiseAbs().maxCoeff() <= RealScalar(64) * eps);
      VERIFY((Tf * Vf - Vf * fz.eigenvalues().asDiagonal()).cwiseAbs().maxCoeff() <=
             RealScalar(64) * eps * Tf.cwiseAbs().maxCoeff());
    }

    // An EigenvaluesOnly compute() does not produce eigenvectors; querying them is a usage error,
    // and the staged computeEigenvectors() supplies them afterwards.
    TridiagonalEigenSolver<RealScalar> staged;
    staged.compute(fdiag, fsub, EigenvaluesOnly);
    VERIFY_IS_EQUAL(staged.info(), Success);
    VERIFY_RAISES_ASSERT(staged.eigenvectors());
    staged.computeEigenvectors();
    VERIFY_IS_EQUAL(staged.info(), Success);
    VERIFY_IS_EQUAL((staged.eigenvectors() - fz.eigenvectors()).cwiseAbs().maxCoeff(), RealScalar(0));

    // A matrix whose entries are all subnormal must still yield genuine unit-norm, orthonormal
    // eigenvectors rather than all-zero columns. Inverse iteration normalizes the tridiagonal by
    // dividing its entries directly by the largest magnitude; doing so (rather than multiplying by its
    // reciprocal) keeps the normalization finite even when that magnitude is subnormal, where 1/scale
    // overflows to infinity and would otherwise let the iterate underflow to zero. The eigenvalues are
    // taken from the same matrix in a normal magnitude range (then scaled back down), so this exercises
    // the eigenvector normalization in isolation from the eigenvalue solver.
    {
      const Index n = 6;
      VectorType base_d(n), base_e(n - 1);
      test::tridiag_1_2_1(base_d, base_e);
      // sub_scale is subnormal and small enough that 1/sub_scale overflows to infinity.
      const RealScalar sub_scale = (std::numeric_limits<RealScalar>::min)() / RealScalar(64);
      const VectorType d = base_d * sub_scale, e = base_e * sub_scale;

      TridiagonalEigenSolver<RealScalar> ref;
      ref.computeEigenvalues(base_d, base_e);
      const VectorType w_sub = ref.eigenvalues() * sub_scale;

      TridiagonalEigenSolver<RealScalar> se;
      se.computeEigenvectors(d, e, w_sub);
      VERIFY_IS_EQUAL(se.info(), Success);
      const MatrixType V = se.eigenvectors();
      for (Index i = 0; i < n; ++i)
        VERIFY(numext::abs(V.col(i).norm() - RealScalar(1)) <= RealScalar(64) * eps);  // unit norm, not all-zero
      VERIFY((V.transpose() * V - MatrixType::Identity(n, n)).cwiseAbs().maxCoeff() <=
             RealScalar(64) * RealScalar(n) * eps);
      // Residual in normal-range coordinates (the eigenvectors are invariant under the uniform scale).
      MatrixType Tn = MatrixType::Zero(n, n);
      Tn.diagonal() = base_d;
      Tn.diagonal(-1) = base_e;
      Tn.diagonal(1) = base_e;
      for (Index i = 0; i < n; ++i)
        VERIFY((Tn * V.col(i) - ref.eigenvalues()(i) * V.col(i)).norm() <= RealScalar(256) * RealScalar(n) * eps);
    }

    // Convergence is reported through info() rather than hard-coded to Success: inverse iteration
    // counts the eigenvectors that fail to converge within its step limit (cf. LAPACK xSTEIN), and a
    // well-conditioned spectrum converges fully, so that count is zero and info() is Success.
    {
      const Index n = 32;
      VectorType d = VectorType::Random(n), e = VectorType::Random(n - 1);
      TridiagonalEigenSolver<RealScalar> es2(d, e);
      VERIFY_IS_EQUAL(es2.info(), Success);
    }
  }
}

EIGEN_DECLARE_TEST(tridiagonal_eigensolver) {
  for (int i = 0; i < g_repeat; i++) {
    CALL_SUBTEST_1(tridiagonal_eigensolver_bisection<double>());
    CALL_SUBTEST_2(tridiagonal_eigensolver_bisection<float>());
    CALL_SUBTEST_3(tridiagonal_eigensolver_eigenvectors<double>());
    CALL_SUBTEST_4(tridiagonal_eigensolver_eigenvectors<float>());
  }
}