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Eigenvalues

Status: Stable

documented, exercised by the test suite and/or worked examples, with no known limitations recorded.

Description

Eigenvalues[m]

gives a list of the eigenvalues of the square matrix m.

Eigenvalues[{m, a}]

gives the generalized eigenvalues of m with respect to a.

Eigenvalues[m, k]

gives the first k eigenvalues (largest by absolute value).

Eigenvalues[m, -k]

gives the k eigenvalues smallest in absolute value.

Eigenvalues[m, UpTo[k]]

gives k eigenvalues, or as many as are available.

Notes Eigenvalues are computed from the roots of the characteristic polynomial Det\[m - lambda I\] (or Det\[m - lambda a\] for the generalised case). Approximate (Real / MPFR) matrices flow through the Solve rationalise -\> solve -\> numericalize pipeline and yield numerical eigenvalues sorted in order of decreasing absolute value. Repeated eigenvalues appear with their algebraic multiplicity. Options: Cubics -\> False (radicals for a general cubic; True forces them) Quartics -\> False (radicals for a general quartic; True forces them) Method -\> Automatic (numeric-matrix method dispatch) With Cubics/Quartics -\> False (the default) a general irreducible cubic or quartic characteristic polynomial is returned as held Root\[\] objects. Special always-solvable families -- binomials, quadratic-in-x^m, and biquadratic-after-depression quartics -- are always returned in radical form regardless of these options. Method values for approximate-numeric matrices: Automatic selects Direct unless k is small (-\> Arnoldi) or the matrix is Hermitian-banded (-\> Banded). "Direct" Hessenberg + implicit shifted QR (general); for Hermitian inputs tridiagonalisation + Wilkinson- shift symmetric QR. Returns all eigenvalues. "Arnoldi" Krylov-subspace iteration for the k extreme eigenvalues; accepts Method -\> {"Arnoldi", MaxIterations -\> n, Tolerance -\> t, BasisSize -\> m}. "Banded" Hermitian only; auto-detects band structure and reduces to tridiagonal before symmetric QR. "FEAST" Hermitian only; eigenvalues in a user-specified Interval -\> {a, b}; accepts Method -\> {"FEAST", "Interval" -\> {a, b}, "ContourPoints" -\> Ne, "SubspaceSize" -\> m0, "MaxIterations" -\> k, "Tolerance" -\> t}. Non-numeric matrices ignore Method and use the symbolic characteristic-polynomial pipeline. Implementation status: "Direct" runs the hand-rolled Householder tridiagonalisation + Wilkinson-shift symmetric QR kernel at machine precision for real symmetric matrices, the Hessenberg + implicit double-shift Francis QR kernel for real non-symmetric matrices, a complex Householder tridiagonalisation + diagonal- phase correction + symmetric QR kernel for complex Hermitian matrices (returns real eigenvalues sorted by |lambda| descending), and a real-block-embedding kernel for complex non-Hermitian matrices (M = \[\[Re A, -Im A\], \[Im A, Re A\]\] routed through real Hessenberg + Francis QR with grouped complex Gram-Schmidt disambiguation of M's spec to recover spec(A)). Automatic routes here too. Arbitrary-precision (MPFR) inputs go through a parallel "Direct" kernel at the input's combined precision: all four shapes -- real symmetric (step 2d-A), real non-symmetric (step 2d-B), complex Hermitian (step 2d-C), and complex non-Hermitian (step 2d-D) -- return eigenvalues / eigenvectors carrying full input precision. "Arnoldi" is implemented in Phase 3 at both machine and MPFR precision: m-step classical Gram-Schmidt with one re-orthog- onalisation pass builds the Krylov basis V\_m and the m x m upper Hessenberg H\_m; H\_m is diagonalised by reusing the "Direct" Francis QR pipeline (real machine, real MPFR, or via a 2mu x 2mu real-block embedding for complex H\_m), and Ritz vectors V\_m y\_i lift back to A-eigenvectors. Complex inputs use paired re/im storage for V\_m and H\_m. Automatic routes to Arnoldi when n \> 32 and k\_spec is given with k \<= max(20, n/10); small matrices always go through Direct (faster + exact). Default BasisSize is max(2k, 20) capped at n; on lucky breakdown (||w|| below tolerance at some step j) Arnoldi terminates early with j+1 exact eigenpairs. MPFR Arnoldi carries through the input's combined precision via the same scratch-pool discipline as the Direct MPFR kernels. "Banded" (Phase 4, machine + MPFR) handles real symmetric and complex Hermitian matrices. It auto-detects the half-bandwidth and reduces to symmetric tridiagonal form via Schwarz-style two-sided Givens rotations with bulge chasing (one off-band entry zeroed per Givens; the introduced bulge is chased b columns at a time until it falls past the matrix edge); the resulting tridiagonal eigenproblem reuses the Phase 2 symmetric QR. Complex Hermitian banded uses paired re/im Givens with a real-c / complex-s parameterisation and the same phase- correction step as the Direct Hermitian kernel. Banded refuses (returns NULL, falls back to Direct) when the matrix isn't Hermitian or when it's fully dense (b == n - 1). Automatic routes here when the matrix is Hermitian, n \> 8, and the half- bandwidth is at most max(8, n/4); narrower bands save more flops than wider ones. "FEAST" (Phase 5, machine + MPFR) handles Hermitian (real symmetric or complex Hermitian) input and returns only the eigenvalues in a user-supplied real Interval -\> {a, b} -- a spectral-slice query rather than a full decomposition. Uses Ne-point Gauss-Legendre quadrature (default Ne = 8; supported: 2, 4, 8, 16) on the upper half of the elliptic contour through (a, 0) and (b, 0) to approximate the spectral projector P\_\[a,b\](A); Schwarz symmetry halves the number of complex linear solves. A Rayleigh-Ritz reduction with Cholesky B\_q = L L^\* extracts the in-interval eigenpairs by reusing the Direct symmetric / Hermitian kernel on L^-1 A\_q L^-\*. Output is sorted by |lambda| descending so k\_spec composes naturally with the in-interval filter. Automatic never routes to FEAST (it requires the user to commit to an interval). FEAST falls back to Direct (NULL return + one-shot stderr warning tagged with the reason) on: non-Hermitian input, missing Interval, degenerate or invalid {a, b} (interval\_high \<= interval\_low), generalised eigenproblem, Cholesky failure on B\_q (subspace too small for the spectral count), LU singular at any quad- rature node, or non-convergence within MaxIterations.

Examples (13)

Every input below was run against the current Mathilda build and its output recorded.

Scope (4)

In[1]:= Eigenvalues[{{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}]
Out[1]= {1/2 (15 + 3 Sqrt[33]), 1/2 (15 - 3 Sqrt[33]), 0}

In[2]:= Eigenvalues[{{7/2, 0, 1/2, 0}, {0, 3, 0, 1}, {1/2, 0, 7/2, 0}, {0, 1, 0, 3}}]
Out[2]= {4, 4, 3, 2}

In[3]:= Eigenvalues[{{{1, 1, 1}, {1, 0, 1}, {0, 0, 1}}, {{0, 1, 1}, {0, 1, 1}, {1, 0, 0}}}]
Out[3]= {Infinity, 1/2 (1 + Sqrt[5]), 1/2 (1 - Sqrt[5])}

In[4]:= Eigenvalues[IdentityMatrix[12]]
Out[4]= {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}

Options (2)

In[5]:= Eigenvalues[N[{{2, -1, 0, 0, 0}, {-1, 2, -1, 0, 0}, {0, -1, 2, -1, 0}, {0, 0, -1, 2, -1}, {0, 0, 0, -1, 2}}], Method -> {"FEAST", "Interval" -> {2.5, 4}}]
Out[5]= {3.73205, 3.0}

In[6]:= Eigenvalues[N[{{2, -1, 0, 0, 0}, {-1, 2, -1, 0, 0}, {0, -1, 2, -1, 0}, {0, 0, -1, 2, -1}, {0, 0, 0, -1, 2}}], 1, Method -> {"FEAST", "Interval" -> {0, 4}}]
Out[6]= {3.73205}

Worked examples (1)

In[7]:= Eigenvalues[{{0,-1},{1,0}}]
Out[7]= {I, -I}

Applications (6)

In[8]:= Eigenvalues[{{2, 1}, {0, 3}}]
Out[8]= {3, 2}

In[9]:= Eigenvalues[{{2, 0}, {0, 5}}]
Out[9]= {5, 2}

In[10]:= Eigenvalues[{{0, 1}, {-1, 0}}]
Out[10]= {-I, I}

In[11]:= Eigenvalues[{{a, b}, {c, d}}]
Out[11]= {1/2 (a + d + Sqrt[(-a - d)^2 - 4 (-b c + a d)]), 1/2 (a + d - Sqrt[(-a - d)^2 - 4 (-b c + a d)])}

In[12]:= Eigenvalues[{{0, 1, 0}, {0, 0, 1}, {1, 0, 0}}]
Out[12]= {1, -(-1)^(1/3), (-1)^(2/3)}

In[13]:= Eigenvalues[{{5, 4, 2}, {4, 5, 2}, {2, 2, 2}}]
Out[13]= {10, 1, 1}

Options & behaviour

Method -> "FEAST" (interval slice)

For Hermitian (real symmetric or complex Hermitian) inexact numeric input, Method -> "FEAST" returns only the eigenpairs whose eigenvalues lie in a user-supplied real interval [a, b] — a spectral-slice query rather than a full decomposition. Implements the contour-integral spectral-projector algorithm of Polizzi (2009): Ne-point Gauss-Legendre quadrature on the upper half of the elliptic contour through (a, 0) and (b, 0) approximates P_[a, b](A) Y (Schwarz symmetry halves the number of complex linear solves), then a Rayleigh–Ritz reduction with Cholesky B_q = L L^* extracts the in-interval eigenpairs.

Sub-options:

Key Default Meaning
"Interval" -> {a, b} required Real interval to slice. Auto-swapped if a > b.
"ContourPoints" -> Ne 8 Gauss-Legendre order. Supported: 2, 4, 8, 16.
"SubspaceSize" -> m0 Max[20, n/4] (capped at n) Working subspace dimension; must be >= spectral count in [a, b].
"MaxIterations" -> k 20 Outer iteration cap.
"Tolerance" -> t precision-aware Residual stopping criterion.

Automatic never routes to FEAST; explicit Method -> "FEAST" is the only way in. The output is sorted by |lambda| descending so the optional k_spec (Eigenvalues[m, k], Eigenvalues[m, -k], Eigenvalues[m, UpTo[k]]) composes naturally with the in-interval filter. MPFR inputs run a parallel kernel at the input's combined precision.

Fail-soft cascade

FEAST returns NULL (and the call falls through to Direct) on any of: non-Hermitian input, missing "Interval", interval_high <= interval_low (catches degenerate {c, c} and NaN coercion failures), generalised eigenproblem (Eigenvalues[{m, a}]), Cholesky failure on B_q (subspace too small for the spectral count in the interval), LU singular at any quadrature node, or non-convergence within MaxIterations. The first such fall-back per process emits a single Eigenvalues::feast: ... falling back to the Direct method. stderr line tagged with the reason, so an explicit FEAST call always returns some sensible answer — at worst the full Direct spectrum.

Implementation notes

Algorithm. builtin_eigenvalues (dispatcher in eigen.c; kernels in eigen_common.c, eigen_direct.c, eigen_banded.c, eigen_arnoldi.c, eigen_feast.c) chooses between an exact/symbolic path and numerical kernels based on whether the matrix is inexact.

Exact / symbolic path (eigen_compute_eigenvalues_full): the characteristic polynomial is formed by the Faddeev–Leverrier–Souriau recurrence (eigen_char_poly_faddeev), which builds the coefficients in O(n^4) matrix multiplications — far cheaper than Laplace expansion of det(m − λI) over a polynomial-entry matrix (O(n!)) once n grows. Its roots are found by eigen_solve_poly (radical formulas for cubics/quartics controlled by Cubics/Quartics, otherwise Root/Solve), extracted with multiplicity, and sorted by descending Abs. The generalised problem Eigenvalues[{m, a}] instead forms det(m − λa) by Laplace expansion (only used for small pencils) and pads the short result with Infinity for the degree-drop branch.

Numerical path (inexact input): dispatched by Method. Direct (direct_dispatch) uses Householder reduction to tridiagonal (symmetric, Golub & Van Loan Alg. 8.3.1) followed by implicit-shift symmetric tridiagonal QR with Wilkinson shift (direct_symtridiag_qr) for symmetric matrices, and Householder reduction to upper Hessenberg + Francis implicit double-shift QR for non-symmetric matrices. Automatic prefers Banded for narrow-band Hermitian input and Arnoldi when only a small k is requested, with FEAST available for Hermitian interval problems; each dispatcher returns NULL for shapes it doesn't support and falls through to Direct, then ultimately to the symbolic pipeline. Numerical-noise imaginary parts are chopped (eigen_chop). A {k}/-k/UpTo[k] spec trims the sorted result (eigen_apply_k_spec).

Data structures. Symbolic side: Expr trees for the polynomial and roots. Numerical kernels operate on dense row-major double (and MPFR) matrices; the source carries LAPACK-HOOK annotations marking where dsytrd/dsteqr/dgehrd/dhseqr would drop in under a USE_LAPACK build (the hooks are present but no LAPACK backend is currently wired).

Complexity / limits. Faddeev–Leverrier is O(n^4); symmetric QR is O(n^3) with cubic per-eigenvalue convergence under the Wilkinson shift. The generalised path is restricted to small n (Laplace expansion). Closed-form eigenvalues are limited by the degree-≤4 radical solver; higher degrees come back as Root objects.

  • Protected.
  • Implemented via the characteristic polynomial Det[m - lambda I] (or Det[m - lambda a]) followed by Solve. The ordinary case uses a Faddeev–Leverrier–Souriau fast path in O(n^4) matrix multiplications, so eigenvalues of large rational / diagonal matrices return instantly.
  • Approximate (Real / MPFR) input flows through Solve's rationalise → solve → numericalize pipeline; tiny imaginary noise introduced by the Cardano formula on real cubics is chopped automatically.
  • Repeated eigenvalues appear with their algebraic multiplicity.
  • Eigenvalues are sorted in order of decreasing absolute value. On an exact modulus tie — the case that matters is a complex-conjugate pair a ± b I — the +imag member is listed first, matching Mathematica (Eigenvalues[{{0,-1},{1,0}}] → {I, -I}). This holds for both the numeric Direct kernel and the symbolic (Root[] / radical) path, so N[Eigenvalues[m]] and Eigenvalues[N[m]] agree on the sign order (e.g. Eigenvalues[{{0,1,0},{0,0,1},{1,1,0}}] returns the characteristic roots as {Root[…,1], Root[…,3], Root[…,2]}, whose N is {1.32472, -0.662+0.562 I, -0.662-0.562 I}). Symbolic eigenvalues that carry free variables (no concrete modulus) retain Solve's natural order.
  • When m, a have a shared null space, Eigenvalues[{m, a}] returns Infinity for each degree drop in the characteristic polynomial.
  • Options: Cubics -> False, Quartics -> False (defaults), Method. With the defaults a general irreducible cubic/quartic characteristic polynomial is returned as held Root[] objects (matching Mathematica); Cubics -> True / Quartics -> True force explicit radicals for the general case. The special always-solvable families — binomials (a x^n + b), quadratic-in-x^m, and biquadratic-after-depression quartics — are always returned in radical form regardless of these options, so a matrix whose eigenvalues are compact nested radicals keeps its closed form. Root[] objects numericalize (including after numeric substitution), so N and the numeric optimizers work either way.
  • For approximate-numeric (Real / MPFR) matrices Method selects the numerical kernel. Accepted values: Automatic (default; routes among Direct / Arnoldi / Banded based on shape and k), "Direct", "Arnoldi", "Banded", and "FEAST". Each accepts a sub-option list form (Method -> {"Name", "Key" -> value, ...}); see the per-method sections in the ?Eigenvalues REPL docstring and the weekly changelog entries under docs/spec/changelog/.

Attributes: Protected.

References

See also: Solve, N

Notes & additional examples

Notes

Eigenvalues of an exact matrix are computed as the roots of the characteristic polynomial Det[m - lambda I], so triangular matrices return their diagonal entries directly and a rotation matrix returns the complex conjugate pair {-I, I}. Results are ordered by decreasing absolute value, and repeated eigenvalues appear with their full algebraic multiplicity. Symbolic matrices return closed-form roots (the 2x2 case uses the quadratic formula). Approximate Real / MPFR matrices flow through dedicated numerical kernels (Householder + QR for the symmetric path, Hessenberg + Francis QR for the general path) selectable through the Method option.