Numerical Linear Algebra
where the algebra stops being the arithmetic
Concept

Schur complement — where it appears

What is left of a block matrix after one block has been eliminated, in the form D − CA⁻¹B. Its conditioning is the conditioning the eliminating method inherits, and for a saddle-point system it carries the square of the constraint's condition number.

Named by 12 essays across 3 fields — each of them below, with the objects they name alongside it.

-1-0.582271-0.1645420.2531860.6709151.088641.506370eigenvalue4 negative10 positivecounted before it was formedpositive10negative4at zero0innermost ratio39the zero block is a theoremand so is the count either side of it

The zero that is not a missing entry

A constrained minimisation produces a matrix with a zero block, and the zero is a theorem rather than a sparsity pattern. No pivot order makes it positive definite, no precision changes that, and Cholesky does not fail somewhere on it — it fails at the first constraint row, on a number the problem already contained.

constraint · Saddle-point systems 01234510⁻¹⁷10⁻¹³10⁻⁹10⁻⁵10⁻¹10³10⁷10¹¹log₁₀ κ(A)condition number, and relative errorκ(S)κ(ZᵀHZ)range-space errornull-space erroragainst a BigInt answerκ(S) at κ(A) = 10⁵4·10¹⁰κ(ZᵀHZ), all stops21range-space forward error5.3·10⁻⁶null-space forward error5.8·10⁻¹²both are the same algebraand only one squares

Two ways to remove a constraint

A constrained system can be reduced by eliminating the multipliers or by eliminating the constrained directions. Both give the same answer in exact arithmetic and inherit different condition numbers — one of them squares the constraint's, and the other does not contain it at all.

constraint · Saddle-point systems 00.3670080.7340171.101031.468031.835040eigenvalue of P⁻¹Kwritten down, then computeddistinct3at 16φ computed1.6off the closed form2.9·10⁻¹⁴1 − φ1φthe preconditioner's effect is a theoremand the golden ratio is in it

Three eigenvalues, and two are the golden ratio

Precondition a saddle-point system by the block diagonal of its own two definite pieces and the preconditioned matrix has exactly three distinct eigenvalues — 1, and the two roots of λ² − λ − 1. A minimal polynomial of degree three means three steps, at every conditioning, and the preconditioner nobody can afford turns out to be the statement the affordable ones are measured against.

constraint · Block preconditioning -5-3-11350eigenvalueHZᵀHZK4 negative — Cholesky of H stops at row 30 negative — a minimum on the constraint(10, 4, 0) = In(ZᵀHZ) + (4, 4, 0)one factorisation, no Zpositive, LDLᵀ of K10negative, LDLᵀ of K4negative in H4negative in ZᵀHZ0the count follows the reduced Hessiannot the Hessian

A minimum the Hessian cannot see

A Hessian with four negative eigenvalues can sit at a constrained minimum, and a Cholesky of it stops at the third row. One symmetric indefinite factorisation of the saddle-point matrix settles the question anyway — ten positive pivots and four negative — without a basis for the null space ever being formed. The count is exact in the algebra and blind in floating point, in a band that grows like κ(A)²; the route through the null space is blind in one that grows like κ(A).

constraint · Saddle-point systems -1-0.500.511.50eigenvalue of P⁻¹Ktriangular [[H, Aᵀ], [0, −Ŝ]] — 1 valuediagonal blkdiag(H, Ŝ) — 3 valuessteps to a residual of 10⁻¹⁰GMRES, triangular2MINRES, diagonal3‖P⁻¹K − I‖54computed |λ − 1| at c = 18.1·10⁻⁸one copy of each value against twoand the counts follow

One eigenvalue and two steps

Put the off-diagonal block back into a block-diagonal saddle-point preconditioner and every eigenvalue of the preconditioned matrix becomes exactly one. GMRES still needs two steps, because the matrix is the identity plus a nilpotent part of norm 54, and a computed eigenvalue at one comes back as a ring of radius 8·10⁻⁸ — the square root of the rounding, not the rounding. With an approximate Schur complement the triangular form leaves one copy of each value where the diagonal form leaves two, and the step count halves.

constraint · Block preconditioning 02468101210⁻¹⁶10⁻¹³10⁻¹⁰10⁻⁷10⁻⁴10⁻¹singular value, in orderσ ⁄ σ₁eight digitsrenumberedin the separator's orderthe same cliff, from the other endσ₂ ⁄ σ₁0.062σ₄ ⁄ σ₁1.3·10⁻⁴σ₆ ⁄ σ₁1.5·10⁻⁸renumbered σ₄ ⁄ σ₁0.63rank at 10⁻⁸6the same entriesin two orders

The cliff behind the count

The fill's rank is an integer between three and six across every separator two dense half-eliminations can afford, and this field has already recorded that a handful of such integers cannot carry a law. The singular values underneath are real numbers. They say the cliff's first step is 23.0 at a separator of eleven, 19.1 at fifteen and 16.2 at twenty-three — and that a control with no differential operator behind it gives 14,672.

sparsity · Fill -1-0.500.511.520eigenvalue of P⁻¹K1 − φ1φS never formedγ1smallest ν0.056furthest from φ, 1 − φ0.57MINRES steps11an exact Schur approximation, bought by changing Hthe golden ratio without S

Where the augmentation puts the cost

Add γAᵀA to the objective block of a saddle-point system and its Schur complement tends to I/γ, so the cheapest possible approximation becomes the right one and the golden-ratio spectrum arrives — within 7.6·10⁻⁶ at γ = 10⁶. MINRES falls from 21 steps to 6. The inner solve with the augmented block rises from 14 conjugate gradient steps to 43, their product does not fall at all, and the answer loses seven and a half digits on the way.

constraint · Block preconditioning -18-15-12-9-6-300log₁₀ ‖PKPᵀ − LDLᵀ‖ / ‖K‖share of orderingsbestworstexistence and stabilityfactorise1unregularised0.67worst growth5·10⁷growth × γ0.5the ordering is free to chooseand not free of consequence

The perturbation that does the work

A saddle-point matrix made quasi-definite is perturbed in both blocks, and the laws measured for it moved both together. Moved apart, the laws all belong to one block. The zero block's perturbation γ decides whether every ordering factorises, sets the worst ordering's growth at 0.51/γ, and costs the answer 1,451 per unit — the reciprocal of the smallest eigenvalue of AH⁻¹Aᵀ to three figures. The perturbation of H moves none of the first two and costs 19 per unit. Refinement removes each block's perturbation at the rate its own Schur complement sets, so γ's limit sits fifty times nearer than δ's.

constraint · Quasi-definite 05101520253035059118177236295vertices eliminatededges of fill so farminDegree: 71natural: 125reverse: 125random: 160maxDegree: 293fill, by orderingminDegree71natural125reverse125random160maxDegree293edges to start60eliminating a vertex makes a cliqueand the order decides how big

Eliminating a vertex is a graph operation

Gaussian elimination on a Laplacian deletes a vertex and joins its neighbours into a clique with conductances wᵢwⱼ over Σw. The matrix that remains is still a graph — symmetric, zero row sums, nonpositive off the diagonal — and the ordering decides whether the fill is thirty-one edges or four hundred and sixty-five.

graph · Graph elimination 10⁻¹⁷10⁻¹⁶10⁻¹⁵10⁻¹⁴10⁻¹³10⁻¹²10⁻¹¹10⁻¹⁰10⁻⁹10⁻⁸10⁻⁷10⁻⁶|h|, the curvature of H along the weak directionσ at which the count is first wrong10⁻⁴10⁻³10⁻²10⁻¹110¹10²no interchangesLDLᵀ of Krank tolerancethrough Z: right to 10⁻¹⁶10 × 4, eight draws per curvatureh = -1·10⁻⁴: LDLᵀ first wrong at σ1.8·10⁻¹¹h = -0.01: LDLᵀ first wrong at σ1.8·10⁻¹⁰h = -1: LDLᵀ first wrong at σ1.4·10⁻⁹h = -100: LDLᵀ first wrong at σ1.8·10⁻⁸the pair's small eigenvalue is σ²/|h|the count loses it long before the rank does

A constraint the count stops seeing

Let one constraint drift towards being a combination of the others and the inertia of the saddle-point matrix keeps its promise only while σ²/|h| can be resolved — σ the constraint's smallest singular value, h the curvature along the direction it barely constrains. At h = −1 the count stops seeing the constraint at σ = 1.4·10⁻⁹, six decades before any rank test would drop it, and below that it reports a genuine minimum as a saddle on three to six draws in eight. No shift of H brings the constraint back: the correction loop shifts a problem that needed nothing by as much as 2,620. A perturbation of the constraint block does not bring it back either — it decides, at σ = √(|h|δ).

constraint · Saddle-point systems norms all onesmallest least-work γ0.032largest3.2·10⁵10⁻²10⁻¹110¹10²10³10⁴10⁵10⁶the constraint's condition numberleast-work γ10100100010⁴Hessian κ 10Hessian κ 100Hessian κ 10⁴two seeds at every pair of condition numbersseven decades, and no norm to read them from

An augmentation read in the smallest eigenvalue

An augmented Lagrangian preconditioner's least work sat at γ = 1, 10⁴ and 1 on three systems, and two of the three optima sat where the smallest generalised eigenvalue was about 0.06 — which suggested a rule written in ν rather than γ. On twenty-four systems whose norms are all one, the least-work γ spans seven decades and ν at the optimum spans a factor of thirty, so there is no one ν. There is still a rule: take the smallest γ at which ν reaches 0.03, and the median system pays 7 per cent over its least work and the worst 29, where the best single γ pays 61. Its price is digits — twelve times the best forward error on the median system, 4,561 times on the worst.

constraint · Block preconditioning 10⁻¹⁴10⁻¹²10⁻¹⁰10⁻⁸10⁻⁶10⁻⁴10⁻²10⁻¹⁶10⁻¹⁴10⁻¹²10⁻¹⁰10⁻⁸10⁻⁶10⁻⁴10⁻²1inner accuracy τdistance from one, or residualJordan pairs: √τresidual, step 2: τthe rest: τdashed: slope ½ and slope 1 through the measurementsthe split is a square root, the residual is not

A square root the residual does not pay

With the exact Schur complement, the triangular saddle-point preconditioner puts every eigenvalue at one in Jordan blocks of size two, and GMRES finishes in two steps. Solve the first block only to a relative accuracy τ and the blocks split: their eigenvalues leave one like √τ — 0.42 to 0.62 times √τ on eighteen systems across six decades, each pair exactly where a five-by-five eigenproblem puts it — while the rest move like τ. The question left open was whether that square root reaches the iteration. It does not. GMRES's residual after two steps grows like τ, with a slope of 0.86 to 1.27 on every system, because its polynomial has a double root at one and cancels each split pair to second order; every further pair of steps divides the residual by about the same factor; and a rule written in τ alone predicts the step count exactly on 179 of 234 runs and within one on 228.

constraint · Block preconditioning

Named alongside it

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

Saddle-point systemsBlock preconditionerMINRESCondition numberExact ground truthReduced hessianConstrained minimisationForward errorIndefinite matrixInertiaLDLᵀ factorisationMinimal polynomial

All concepts