mgw-spectrum
At its defaults it draws the spectrum of p⁻¹k with s = ah⁻¹aᵀ, exactly, at 10 unknowns and 4 constraints. P = blkdiag(H, S). With S the exact Schur complement AH⁻¹Aᵀ the preconditioned matrix has exactly three distinct eigenvalues — 1 with multiplicity n − m = 6, and (1 ± √5)/2 with multiplicity 4 each. Those are 1 − φ = -0.618034 and φ = 1.61803, the golden ratio, which arrives from λ² − λ − 1 = 0 rather than from anything anybody chose. The dashed lines are that closed form and the marks are the computed spectrum; here they are 3 distinct values and the largest distance from the closed form anywhere is 2.909·10⁻¹⁴. A minimal polynomial of degree three means a Krylov method finishes in three steps, which is what the next figure measures.
mgw-spectrum is one function in lib/figures/blockprec.js —
preconditioning a saddle point — three eigenvalues, and a spectrum with no constraint in it. Everything below came out of it during this build, at
arguments taken from the essays rather than invented for this page. A figure here is the
figure a reader meets in an essay, and if the generator changes, this page changes with it.
At its defaults
Drawn even though every essay passes arguments — which on this site is every essay, at 100% of placements since the standard pass. A default nothing exercises is a trap for the next essay to call this with none, and this is the page where a default that has drifted from the figures around it becomes visible.
P = blkdiag(H, S). With S the exact Schur complement AH⁻¹Aᵀ the preconditioned matrix has exactly three distinct eigenvalues — 1 with multiplicity n − m = 6, and (1 ± √5)/2 with multiplicity 4 each. Those are 1 − φ = -0.618034 and φ = 1.61803, the golden ratio, which arrives from λ² − λ − 1 = 0 rather than from anything anybody chose. The dashed lines are that closed form and the marks are the computed spectrum; here they are 3 distinct values and the largest distance from the closed form anywhere is 2.909·10⁻¹⁴. A minimal polynomial of degree three means a Krylov method finishes in three steps, which is what the next figure measures.
kappaH: 100
The arguments are the ones A condition number sent to infinity passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.
P = blkdiag(H, S). With S the exact Schur complement AH⁻¹Aᵀ the preconditioned matrix has exactly three distinct eigenvalues — 1 with multiplicity n − m = 6, and (1 ± √5)/2 with multiplicity 4 each. Those are 1 − φ = -0.618034 and φ = 1.61803, the golden ratio, which arrives from λ² − λ − 1 = 0 rather than from anything anybody chose. The dashed lines are that closed form and the marks are the computed spectrum; here they are 3 distinct values and the largest distance from the closed form anywhere is 2.909·10⁻¹⁴. A minimal polynomial of degree three means a Krylov method finishes in three steps, which is what the next figure measures.
kind: "diagonal"
The arguments are the ones A preconditioner that need not know the constraint passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.
P = blkdiag(H, S). With S the exact Schur complement AH⁻¹Aᵀ the preconditioned matrix has exactly three distinct eigenvalues — 1 with multiplicity n − m = 6, and (1 ± √5)/2 with multiplicity 4 each. Those are 1 − φ = -0.618034 and φ = 1.61803, the golden ratio, which arrives from λ² − λ − 1 = 0 rather than from anything anybody chose. The dashed lines are that closed form and the marks are the computed spectrum; here they are 9 distinct values and the largest distance from the closed form anywhere is 2.955. A minimal polynomial of degree three means a Krylov method finishes in three steps, which is what the next figure measures.
m: 2
The arguments are the ones Three eigenvalues, and two are the golden ratio passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.
P = blkdiag(H, S). With S the exact Schur complement AH⁻¹Aᵀ the preconditioned matrix has exactly three distinct eigenvalues — 1 with multiplicity n − m = 8, and (1 ± √5)/2 with multiplicity 2 each. Those are 1 − φ = -0.618034 and φ = 1.61803, the golden ratio, which arrives from λ² − λ − 1 = 0 rather than from anything anybody chose. The dashed lines are that closed form and the marks are the computed spectrum; here they are 3 distinct values and the largest distance from the closed form anywhere is 1.354·10⁻¹⁴. A minimal polynomial of degree three means a Krylov method finishes in three steps, which is what the next figure measures.
kind: "identity"
The arguments are the ones Three eigenvalues, and two are the golden ratio passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.
P = blkdiag(H, S). With S the exact Schur complement AH⁻¹Aᵀ the preconditioned matrix has exactly three distinct eigenvalues — 1 with multiplicity n − m = 6, and (1 ± √5)/2 with multiplicity 4 each. Those are 1 − φ = -0.618034 and φ = 1.61803, the golden ratio, which arrives from λ² − λ − 1 = 0 rather than from anything anybody chose. The dashed lines are that closed form and the marks are the computed spectrum; here they are 9 distinct values and the largest distance from the closed form anywhere is 2.64. A minimal polynomial of degree three means a Krylov method finishes in three steps, which is what the next figure measures.
kappaH: 1
The arguments are the ones Three eigenvalues, and two are the golden ratio passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.
P = blkdiag(H, S). With S the exact Schur complement AH⁻¹Aᵀ the preconditioned matrix has exactly three distinct eigenvalues — 1 with multiplicity n − m = 6, and (1 ± √5)/2 with multiplicity 4 each. Those are 1 − φ = -0.618034 and φ = 1.61803, the golden ratio, which arrives from λ² − λ − 1 = 0 rather than from anything anybody chose. The dashed lines are that closed form and the marks are the computed spectrum; here they are 3 distinct values and the largest distance from the closed form anywhere is 6.617·10⁻¹⁴. A minimal polynomial of degree three means a Krylov method finishes in three steps, which is what the next figure measures.
What it checked while drawing
Every figure above asserted its own claims on the way to being drawn, and a claim that failed
would have failed the build rather than drawn a wrong picture. Those assertions used to leave
no trace at all: a passing one returned true and the only evidence the figure had
checked anything was that nothing crashed. The list below is what they actually said, collected
by running this generator with an observer installed — not a description of
what it is believed to check.
13 distinct claims across 6 sets of arguments, grouped below by shape — because most of them are one sentence with a different number in it, and how many separate times that sentence was put to the test is the informative part.
a constraint no larger than the problem
a positive definite preconditioner, since MINRES needs one
a problem with something left to minimise
a Schur approximation the library implements
a Schur approximation this file implements
an approximate Schur complement moves them off the closed form
and they are 1 − φ, 1 and φ
fewer constraints than unknowns, so something is left to minimise
Jacobi needs a symmetric matrix
LU is for square matrices
matmul shapes agree
the exact Schur complement leaves three distinct eigenvalues
with multiplicity n − m at one
Against the rule
It draws a decomposition and prints its residual. It calls
blockDiagonalRun,
and every figure above carries the badge — which residualcheck verifies by looking
for it in the emitted SVG rather than by finding the call that builds one. A badge that is
constructed and then left out of the body is the failure that check exists for.
Across the library: the rule bites on 165
of 306 generators —
150 print a residual and
15 are exempt with a published reason;
141 factorise nothing.
Read from lib/residual-rule.js, which is the same body the gate enforces from,
and the gate's last check fails the build if this page and it disagree about any generator.
Where it is called
Changing this generator changes every figure on this list. That is what makes the list worth publishing rather than keeping in a check script.
A condition number sent to infinity
An interior-point method manufactures an ill-conditioned matrix on every iteration, deliberately, because the separating of a diagonal is how it discovers which constraints are active. Written one way the answer keeps fifteen digits at a condition number of 3·10¹⁵. Written the other way — the way almost every code writes it — it has none left.
The matrix a constraint makesA preconditioner that need not know the constraint
Keep the constraint block exactly and replace the objective block by anything positive definite on the null space. The preconditioned matrix then has 2m eigenvalues at exactly one, and its remaining n − m are the generalised eigenvalues of a pencil in which the constraint does not appear. Sweep its condition number over six decades and they do not move in six digits.
The matrix a constraint makesThe 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.
The matrix a constraint makesThree 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.
The matrix a constraint makesTwo 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.