Numerical Linear Algebra
where the algebra stops being the arithmetic
Concept

Quadratic eigenvalue problem — where it appears

The problem a damped structure produces instead of Ax = λx, with the matrix a quadratic in the eigenvalue. A problem of size n has 2n eigenvalues and n components, so the eigenvectors cannot be independent and nothing in the eigenbasis picture survives.

Named by 9 essays across 4 fields — each of them below, with the objects they name alongside it.

00.0939569-2-1.24498-0.4899610.2650581.020081.7751real partimaginary parttwo routes, one spectrumeigenvalues16rows8complex16against the closed form2.1·10⁻¹⁵n rows and 2n eigenvaluesso the eigenvectors are not a basis

A matrix that depends on its own eigenvalue

A damped structure does not produce Ax = λx. It produces (λ²M + λC + K)x = 0, where the matrix whose null vector is wanted is a function of the number being solved for — so there is nothing to factorise, an n × n problem has 2n answers, and the eigenvectors cannot be a basis.

polynomial · polynomial eigenvalue 0246810⁻¹⁷10⁻¹⁴10⁻¹¹10⁻⁸10⁻⁵10⁻²10¹log₁₀ γ, the change of unitsrelative errorforward errorη, the quadraticη, the linearisationagainst a closed formη(linearisation), worst7.6·10⁻¹³η(quadratic), worst1.2·10⁻⁴forward error, worst0.0013coefficient spread4.2·10¹⁵the solver is right at every stopabout a problem nobody asked

A backward-stable answer to a problem nobody asked

One quadratic eigenvalue problem, in nine systems of units, with a change of variable that is exact in both directions. The residual the solver prints stays at the rounding level at every stop. The answer loses eleven orders of magnitude, and the two facts are consistent.

polynomial · linearisation backward error 0246810⁻¹⁶10⁻¹³10⁻¹⁰10⁻⁷10⁻⁴10⁻¹log₁₀ γ, the change of unitsforward erroras writtenafter scalingone change of variableunscaled, worst0.0013scaled, worst1.7·10⁻¹³orders recovered10scaled coefficient spread4.5the answer was never the problemthe units were

The scaling that buys ten orders

Two lines computed from three norms, a change of variable that is exact in both directions, and the whole of the loss the previous essay measured comes back — flat, at every stop, because after scaling every stop is the same problem.

polynomial · polynomial scaling first · leading5.47·10⁻⁶first · trailing4.62·10⁻⁵second · leading4.27·10⁻⁵second · trailing2.23·10⁻⁴symmetric · leading5.47·10⁻⁶symmetric · trailing4.05·10⁻⁵all six are the same algebrabest route5.5·10⁻⁶worst route2.2·10⁻⁴spread across the six41condition of the linearisation8.3·10¹²the spectra agreeand the arithmetic does not

Six routes to one spectrum

Three linearisations of one quadratic, each reduced to a standard eigenvalue problem two ways. All six have exactly the same eigenvalues in exact arithmetic. On a well-scaled problem they differ by noise; on a badly scaled one by a factor of forty; and two of the six are the same matrix.

polynomial · linearisation backward error -33-28.2505-23.5009-18.7514-14.0018-9.25229-4.502750eigenvalueQ(μ) ≺ 0one Cholesky, two answersabove the certificate8below it8the gap0.67critical β for this n5.8the spectrum is real by classnot by outcome

Every eigenvalue real, and a test that says so

A quadratic eigenvalue problem has no reason to have real eigenvalues. One class does, as a property rather than an outcome, and the proof is a Cholesky that completes. The boundary of the class has a closed form, and at the boundary the arithmetic loses half its digits with nothing ill conditioned anywhere.

polynomial · hyperbolic quadratic -5-3.20611-1.412210.3816782.175573.969460log₁₀ |λ||λ| = 1λλ′ = 1, in the algebrapairs6decades of spectrum9.6pairing error, general3.3·10⁻⁹pairing error, structured2.2·10⁻¹⁶a symmetry the solver never knew aboutand the half of the answer it decides

A spectrum that comes in reciprocal pairs

A palindromic quadratic reads the same backwards, so λ is an eigenvalue exactly when 1/λ is. A general solver discards that, computes the large half of the spectrum perfectly and the small half to seven digits — and the small half is a division away from being perfect too.

polynomial · structured spectrum 1357910⁻¹⁶10⁻¹³10⁻¹⁰10⁻⁷10⁻⁴log₁₀ b, the couplingrelative errorsmall half, directstructure-preservingsmall half, by 1/λone divisionlarge half, direct5.5·10⁻¹⁵small half, direct10⁻⁷small half, by 1/λ4.9·10⁻¹⁵structure-preserving6.5·10⁻⁶the structure is not decorationit is where half the accuracy is

A perturbation that keeps the symmetry

The smallest perturbation that makes a computed answer exact is the backward error. Ask for the smallest one that also keeps the problem's structure and the number can only go up — and measured on a palindromic quadratic it goes up by 1.17, while the structure the computed spectrum has lost is not in either number.

structure · structured backward error 02468101201change of units, by exponent10⁰10¹10²10³10⁴10⁶10¹⁰10¹⁶10¹⁹10²⁰10⁴⁰10¹⁵⁰as writtenafter scalinga range questionlargest finite value3.4·10³⁸predicted boundary γ1.8·10¹⁹last γ that forms10¹⁹stops where scaling fails0not a poor answerno answer at all

The units that overflow before the answer does

A change of variable that is exact in the algebra requires γ² times a matrix to be a number the format can hold. In binary64 that is a bound nobody meets by accident. In binary32 it arrives at 10¹⁹ and in fp16 at 256, and past it there is no answer rather than a poor one.

arithmetic · overflow 33.310⁻⁴10⁻³10⁻²10⁻¹1log₁₀ numbers storeddistance to the dominant eigenvalueArnoldi, linearisedprojected quadraticper number heldstorage, linearised2080storage, second-order1040Ritz values, linearised26Ritz values, second-order52half the storageand twice the approximations

A Krylov space for a problem that is not linear

A quadratic eigenvalue problem has no matrix to build a Krylov space out of. The recurrence that builds one anyway stores half as many numbers, returns twice as many Ritz values — and stops being a basis at twenty vectors while the answer it gives keeps improving.

iterative · krylov

Named alongside it

The objects these essays reach for when they reach for this one.

LinearisationMatrix polynomialBackward errorCondition numberExact ground truthScalingCompanion formPalindromic quadraticReciprocal pairStructure preservingStructured backward errorArnoldi

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