Series

Quasi-definite — the series

6 essays on one idea, from the one that introduces it to the one that assumes the rest.
  1. -18-15-12-9-6-300log₁₀ ‖PKPᵀ − LDLᵀ‖ / ‖K‖share of orderingsbestworstexistence and stabilityfactorise1unregularised0.69worst growth6.4·10⁵growth × γ0.64the ordering is free to chooseand not free of consequence

    The regularisation that legalises every order

    Perturb a saddle-point matrix's two blocks in opposite directions and it acquires a factorisation with a diagonal D under every symmetric permutation — not under a good one, under all of them. Five hundred random orderings, five hundred successes, and a growth factor that spans six orders across them.

    part 1 · constraint
  2. -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.

    part 2 · constraint
  3. 01234567910δ added to Hpositive pivots−λmin(H) = 4.67|μ| = 1minimumsaddlewhat the signs are countingminimum, true count10saddle, true count9saddle read as minimum from1.1guarantee needs δ past5.1the signs count ZᵀHZ + δInot the curvature the problem has

    A shift that certifies a saddle

    On a constrained problem whose Hessian has four negative eigenvalues, a saddle-point matrix is quasi-definite only once H + δI is positive definite — past δ = 5.08 here. Its pivot signs then count the curvature of ZᵀHZ + δI rather than of ZᵀHZ, so a saddle with a negative curvature of −1 is certified a minimum from δ = 1.05 on, and every saddle shallower than δ goes the same way. Iterative refinement against the unregularised matrix keeps the second-order test the count gave up: it contracts on the minimum at δ/(μ + δ), 0.980 a step at δ = 5, and on the saddle it grows at exactly 1.25.

    part 3 · constraint
  4. μ = −0.01, δ = 7saddle's growth a step0.0014minimum's fall a step0.014residual verdict, step6205010015020010⁻¹10¹refinement steprelative residual, relative errorsaddle, residualsaddle, errorminimum, residualminimum, errorthe pivot signs call both of these a minimumthe residual does not

    The residual turns before the error doubles

    Regularise a saddle-point matrix past the Hessian's most negative eigenvalue and its pivot signs certify every shallow saddle as a minimum; refinement against the unregularised matrix keeps the test, as a rate, and a saddle at a hundredth of the regularisation grows by only 1.0014 a step — 485 steps to double. That was read as hundreds of steps before the history says anything. The residual, which is what a solver actually has, says it at step 62: a fit of its logarithm over the last ten steps turns positive there and stays positive, while the minimum's is negative from step 10. Across five regularisations the verdict comes at an eighth of the doubling time.

    part 4 · constraint
  5. rounding finds every hidden saddleμ = −0.01: share zero, verdict at step2723μ = −0.1: share zero, verdict at step1102μ = −1: share zero, verdict at step18110¹10²10³decades of the growing direction removed from the right-hand sidestep the verdict settles0246810121416allμ = −0.01μ = −0.1μ = −1dashed: the growth against the slowest contractiona decade of hiding is a fixed number of steps

    The right-hand side that hides the saddle

    Refinement against an unregularised saddle-point matrix gives the second-order verdict the pivot signs cannot, from step 62 on the shallowest saddle — for one right-hand side. The verdict depends on how much of the growing direction the right-hand side contains, and it can contain none. Each decade removed delays the verdict by a fixed number of steps, 147 on the shallowest saddle, set by the growth against the minimum's slowest contraction rather than by the growth alone, which predicted 1,611. Rounding stops the hiding at about 2,700 steps — but by then refinement has solved the system to a residual of 2.6 times ten to the minus thirteen, and any stopping test has already accepted it. A random kick to the starting point costs nothing and finds it.

    part 5 · constraint
  6. μ = −0.1 and −0.01both hidden: steps a decade81the faster direction's race81the slower direction's race14710¹10²10³decades of the hidden directions removedstep the verdict settles0246810121416allboth directions hiddenonly the faster hiddenonly the slower hiddenμ = −0.1 aloneμ = −0.01 alonedashed: a single saddle of each curvatureone hyperplane hides nothing

    One hyperplane hides nothing

    Iterative refinement against a saddle-point matrix gives its verdict when the growing direction overtakes the minimum's slowest decay, and a right-hand side with that direction removed delays it by a fixed number of steps a decade. With two negative curvatures, the prediction was that hiding both is paid for by the faster, and that hiding only the faster leaves the slower one's race to run. The first half holds to half a per cent on three pairs, and to one and a half on two whose curvatures differ by a factor of two and of 1.2. The second does not: hiding either direction alone moves the verdict by a fixed handful of steps, the same at two decades as at sixteen, because the direction left in view is already growing. Only the intersection of the two hyperplanes hides anything — and it hides less than either curvature can alone, 1,127 steps at worst against 2,907, with random right-hand sides' slowest at 79 against 417.

    part 6 · constraint

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