cholesky-stops
At its defaults it draws the pivots cholesky computes on a saddle-point matrix, and the one it stops at — row 11 of 14. Every pivot Cholesky forms on K = [[H, Aᵀ], [A, 0]], in order, with the 10 taken inside H drawn as positive and the 11-th, which is not, drawn as the failure. It is not a near miss and it is not a rounding accident: the leading n × n block is positive definite, so the first 10 pivots are positive at any conditioning, and the next one is 0 − a₁ᵀH⁻¹a₁ where a₁ᵀ is the first row of A. That equals −(AH⁻¹Aᵀ)₁₁ = -328.56, and the pivot the routine actually reports is -328.56 — the same number to 13 digits. The failure has an address and a value, and both were available before the factorisation started.
cholesky-stops is one function in lib/figures/kkt.js —
the matrix a constraint makes — an inertia known before assembly, and a failure with an address. 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.
Every pivot Cholesky forms on K = [[H, Aᵀ], [A, 0]], in order, with the 10 taken inside H drawn as positive and the 11-th, which is not, drawn as the failure. It is not a near miss and it is not a rounding accident: the leading n × n block is positive definite, so the first 10 pivots are positive at any conditioning, and the next one is 0 − a₁ᵀH⁻¹a₁ where a₁ᵀ is the first row of A. That equals −(AH⁻¹Aᵀ)₁₁ = -328.56, and the pivot the routine actually reports is -328.56 — the same number to 13 digits. The failure has an address and a value, and both were available before the factorisation started.
kappaH: 100
The arguments are the ones A constraint is a weight at 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.
Every pivot Cholesky forms on K = [[H, Aᵀ], [A, 0]], in order, with the 10 taken inside H drawn as positive and the 11-th, which is not, drawn as the failure. It is not a near miss and it is not a rounding accident: the leading n × n block is positive definite, so the first 10 pivots are positive at any conditioning, and the next one is 0 − a₁ᵀH⁻¹a₁ where a₁ᵀ is the first row of A. That equals −(AH⁻¹Aᵀ)₁₁ = -3.7312, and the pivot the routine actually reports is -3.7312 — the same number to 15 digits. The failure has an address and a value, and both were available before the factorisation started.
kappaH: 10000
The arguments are the ones The zero that is not a missing entry 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.
Every pivot Cholesky forms on K = [[H, Aᵀ], [A, 0]], in order, with the 10 taken inside H drawn as positive and the 11-th, which is not, drawn as the failure. It is not a near miss and it is not a rounding accident: the leading n × n block is positive definite, so the first 10 pivots are positive at any conditioning, and the next one is 0 − a₁ᵀH⁻¹a₁ where a₁ᵀ is the first row of A. That equals −(AH⁻¹Aᵀ)₁₁ = -328.56, and the pivot the routine actually reports is -328.56 — the same number to 13 digits. The failure has an address and a value, and both were available before the factorisation started.
kappaH: 1
The arguments are the ones The zero that is not a missing entry 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.
Every pivot Cholesky forms on K = [[H, Aᵀ], [A, 0]], in order, with the 10 taken inside H drawn as positive and the 11-th, which is not, drawn as the failure. It is not a near miss and it is not a rounding accident: the leading n × n block is positive definite, so the first 10 pivots are positive at any conditioning, and the next one is 0 − a₁ᵀH⁻¹a₁ where a₁ᵀ is the first row of A. That equals −(AH⁻¹Aᵀ)₁₁ = -0.082076, and the pivot the routine actually reports is -0.082076 — the same number to 16 digits. The failure has an address and a value, and both were available before the factorisation started.
kappaH: 100000000
The arguments are the ones The zero that is not a missing entry 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.
Every pivot Cholesky forms on K = [[H, Aᵀ], [A, 0]], in order, with the 10 taken inside H drawn as positive and the 11-th, which is not, drawn as the failure. It is not a near miss and it is not a rounding accident: the leading n × n block is positive definite, so the first 10 pivots are positive at any conditioning, and the next one is 0 − a₁ᵀH⁻¹a₁ where a₁ᵀ is the first row of A. That equals −(AH⁻¹Aᵀ)₁₁ = -3.1551·10⁶, and the pivot the routine actually reports is -3.1551·10⁶ — the same number to 9 digits. The failure has an address and a value, and both were available before the factorisation started.
kappaH: 1000000
The arguments are the ones The zero that is not a missing entry 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.
Every pivot Cholesky forms on K = [[H, Aᵀ], [A, 0]], in order, with the 10 taken inside H drawn as positive and the 11-th, which is not, drawn as the failure. It is not a near miss and it is not a rounding accident: the leading n × n block is positive definite, so the first 10 pivots are positive at any conditioning, and the next one is 0 − a₁ᵀH⁻¹a₁ where a₁ᵀ is the first row of A. That equals −(AH⁻¹Aᵀ)₁₁ = -3.189·10⁴, and the pivot the routine actually reports is -3.189·10⁴ — the same number to 11 digits. The failure has an address and a value, and both were available before the factorisation started.
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.
8 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 size whose pivots fit along the axis
and the pivot it stops on is −(AH⁻¹Aᵀ)₁₁, a number the problem already held
Cholesky stops on the first constraint row
fewer constraints than unknowns
fewer constraints than unknowns, so something is left to minimise
LU is for square matrices
matmul shapes agree
Against the rule
It draws a decomposition and prints its residual. It calls
choleskyPivots, schurComplement,
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 constraint is a weight at infinity
Stack an equality constraint on top of a least-squares problem with a large weight and the answer approaches the constrained one like 1/τ². The limit is takeable to any accuracy — and how far it can be taken is a property of the solver, not of the problem. One of them stops at the square root of the precision, and one of them does not stop.
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 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.
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.
Two errors, and whose fault they areTwo condition numbers of one matrix
κ₂ is a worst case over perturbations of a given norm, and a normwise perturbation may put its whole budget on the smallest entry. The componentwise number is a worst case over perturbations proportional to the entries, which is what a backward-stable factorisation actually makes. On one matrix they are 3·10¹³ and 13.3, and the error obeys the second.
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.