ordering-bars
At its defaults it draws nonzeros in the cholesky factor of the 12×12 grid laplacian, by ordering. A horizontal bar chart comparing the number of nonzero entries in the Cholesky factor under four elimination orderings, with reference marks for the matrix itself and for a dense factor.
ordering-bars is one function in lib/figures/sparse.js —
sparsity — fill counted two ways, and the ordering that decides the memory. 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.
A horizontal bar chart comparing the number of nonzero entries in the Cholesky factor under four elimination orderings, with reference marks for the matrix itself and for a dense factor.
grid: 12
The arguments are the ones A block size is a property of the machine 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.
A horizontal bar chart comparing the number of nonzero entries in the Cholesky factor under four elimination orderings, with reference marks for the matrix itself and for a dense factor.
grid: 10
The arguments are the ones Changing the condition number on purpose 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.
A horizontal bar chart comparing the number of nonzero entries in the Cholesky factor under four elimination orderings, with reference marks for the matrix itself and for a dense factor.
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.
9 distinct claims across 3 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.
and some ordering beats the natural one
minimum degree beats a dense factorisation
minimum degree still fills in
natural beats a dense factorisation
natural still fills in
nested dissection beats a dense factorisation
nested dissection still fills in
reverse Cuthill–McKee beats a dense factorisation
reverse Cuthill–McKee still fills in
Against the rule
The rule does not apply to it. It factorises nothing, so there is no residual it could be withholding. That is worth stating rather than leaving blank: a site that reported the rule as satisfied by every generator would be counting mostly generators the rule never reached.
Across the library: the rule bites on 52
of 99 generators —
37 print a residual and
15 are exempt with a published reason;
47 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 block size is a property of the machine
Three lines of counting say the best block size is √(M/3). Scanned over every integer at five fast memories, the measured optimum is √M − 2 — exactly, at all five. The count has the right scaling and the wrong constant, low by a factor of 1.56, and the wrong form: the answer is affine in √M rather than proportional to it.
Iterating, instead of factorisingChanging the condition number on purpose
Preconditioning is usually introduced as a trick that makes an iteration converge faster. It is not a trick. It is solving a different system with the same solution and a condition number chosen rather than inherited, and the new condition number is computable.
Sparsity, and what elimination costsStructure and stability stop being separable
The sparsest variable to eliminate on this matrix has a diagonal entry of 10⁻¹². Eliminating it produces the smaller factor, reproduces the matrix to 3.8·10⁻¹⁷ — better than pivoting does — and returns an answer wrong in the fifth digit.
Sparsity, and what elimination costsThe order decides the memory
Four elimination orderings on one matrix give factors of 1,739, 1,354, 1,413 and 1,026 entries. All four factorisations are exact, all four return the same answer, and the one with the better asymptotics is not the one that wins.
Sparsity, and what elimination costsTwo ends of the same arrow
One matrix, one row moved from the front of the elimination order to the back, and the factor goes from completely dense to no fill at all. Both factorisations are exact to rounding, and nothing numerical chose between them.