Numerical Linear Algebra
where the algebra stops being the arithmetic
Concept

Moment matching — where it appears

Building a reduced model by projecting onto subspaces of shifted solves, so that it matches the original exactly at chosen points. It costs a few solves and bounds nothing between the points, which is the opposite trade from balanced truncation.

Named by 5 essays across one field — each of them below, with the objects they name alongside it.

10⁻¹110¹10²10³10⁻¹⁷10⁻¹⁴10⁻¹¹10⁻⁸10⁻⁵s, on the real axis|H − Hᵣ| ÷ |H|exact where askedpoints4conditions bought8worst at a point5.3·10⁻¹⁶worst away from one8.4·10⁻⁴4 points, 8 conditionsand no bound in between

Exact at the points that were named

Balanced truncation asks for nothing and bounds everything, at a cost no large model can pay. The other kind of reduction asks for r numbers, costs r solves, is exact at every one of them — and bounds nothing anywhere else. That trade is the whole of large-scale model reduction.

reduction · Moment matching 2345678110²10⁴10⁶10⁸10¹⁰vectors in the basisκ₂ of the basisas derivedsolves spread outone subspace, two spanning setseight moments at one point7.7·10⁹eight points, spread1growth per vector661/u4.5·10¹⁵the same subspaceand only one of them usable

A basis that is the same subspace and not the same thing

The interpolation conditions are conditions on a subspace, so any basis of it will do. The one a derivation writes down reaches a condition number of 7.7·10⁹ by its eighth vector, and the rate at which it gets there is set by a number the user chose with no information.

reduction · Moment matching 01234567891011121310⁻¹110¹10²10³iterationinterpolation point σa fixed point that is a conditionorder4iterations13σ against −λ(Aᵣ)2.8·10⁻¹²H₂ error4.3·10⁻⁵it stopped movingand the condition holds there

Interpolating at the model’s own poles

One choice of interpolation points is not arbitrary — the mirrored poles of the model about to be built. It is a fixed point rather than a guess, and when it is reached it beats a method costing O(n³) — by 0.4 per cent, which is the honest size of the whole contest.

reduction · Moment matching 10⁻¹110¹10²-45-35-25-15-551525s, the smallest of the three interpolation pointsrightmost pole of the reduced modelunstable above this linebalanced truncation, order 3exact, and unusablefull system's pole-3.5placements swept19unstable models4worst pole24their interpolation1.3·10⁻¹⁴balanced truncation-0.84the conditions all holdand the model cannot be run

A model that cannot be run

A stable system, reduced by matching its transfer function at three points exactly, comes back with a pole in the right half plane at four of nineteen placements — and matches at all three points to 1.3·10⁻¹⁴ while doing it. The construction did what it promised.

reduction · Reduced stability 10⁻¹110¹10²s, the smallest interpolation pointrightmost pole of the reduced model−10−10+1+10unstable above zeronumerical range edge, -0.986system's abscissa, -3.49two-sided, order 3one-sided, order 3one-sided, order 6Pe 10, nineteen placementssystem's rightmost eigenvalue-3.5edge of the numerical range-0.99two-sided, order 3: unstable placements4one-sided, order 3: unstable placements0one-sided, order 6: unstable placements0signed logarithmic axisshaded: poles in the right half plane

Half the conditions and a certificate

A two-sided interpolatory reduction of a stable convection–diffusion system came back unstable at four placements of nineteen. The one-sided reduction built from the same kind of solves came back stable at all 152 placements measured across eight Péclet numbers, and not by luck: every pole of a Galerkin model lies inside the numerical range of the operator, whose edge here is exactly the diffusion term's largest eigenvalue, −0.986 at Péclet 10. The price is the slope conditions, and spent as six one-sided shifts instead of three two-sided ones it beats the stable two-sided model at seven placements of ten.

reduction · Reduced stability

Named alongside it

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

Rational krylovBalanced truncationPetrov–GalerkinTransfer functionCondition numberConvection diffusionFlop countHermite interpolationKrylov subspaceNon-normalityReduced stabilityA-priori bound

All concepts