Generator

block-partition

One function in the hodlr library, called 23 times across 11 essays. Below: what it draws at its defaults, what it draws at every value an essay asks for, the 7 claims it put to the test while drawing them, and where it stands against the rule this site is named for.

At its defaults it draws a 256 × 256 kernel matrix partitioned by the strong rule, with each compressed block's rank. 112 blocks: 46 kept dense and 66 stored as two thin factors, whose ranks run from 4 to 5. The rule decides one thing — whether a pair of index clusters may be compressed — and it decides it from four numbers about where those clusters sit, before any entry of the matrix is read. The strong rule refuses any pair whose clusters touch, and subdivides instead, so the diagonal is fringed with small dense blocks and no rank on the picture exceeds 5. The whole thing stores 27,008 numbers against 65,536 entries, and reproduces the matrix to 3.41·10⁻¹⁰.

block-partition is one function in lib/figures/hodlr.js — the partition — a rule that reads four numbers a pair and no entry of the matrix. 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 256 × 256 kernel matrix partitioned by the strong rule, with each compressed block's rank112 blocks: 46 kept dense and 66 stored as two thin factors, whose ranks run from 4 to 5. The rule decides one thing — whether a pair of index clusters may be compressed — and it decides it from four numbers about where those clusters sit, before any entry of the matrix is read. The strong rule refuses any pair whose clusters touch, and subdivides instead, so the diagonal is fringed with small dense blocks and no rank on the picture exceeds 5. The whole thing stores 27,008 numbers against 65,536 entries, and reproduces the matrix to 3.41·10⁻¹⁰.545545545455554545545545545454555555454545545545545455554545545545rows and columns, in the order the points arrivedark: kept dense · light: two thin factors, rank printedthe strong partitionblocks112kept dense46largest rank5numbers stored2.7·10⁴‖A − A_H‖ ⁄ ‖A‖3.4·10⁻¹⁰the picture is decidedbefore a number is read

112 blocks: 46 kept dense and 66 stored as two thin factors, whose ranks run from 4 to 5. The rule decides one thing — whether a pair of index clusters may be compressed — and it decides it from four numbers about where those clusters sit, before any entry of the matrix is read. The strong rule refuses any pair whose clusters touch, and subdivides instead, so the diagonal is fringed with small dense blocks and no rank on the picture exceeds 5. The whole thing stores 27,008 numbers against 65,536 entries, and reproduces the matrix to 3.41·10⁻¹⁰.

admissibility: "strong"

The arguments are the ones A block nobody can call sparse 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 256 × 256 kernel matrix partitioned by the strong rule, with each compressed block's rank112 blocks: 46 kept dense and 66 stored as two thin factors, whose ranks run from 4 to 5. The rule decides one thing — whether a pair of index clusters may be compressed — and it decides it from four numbers about where those clusters sit, before any entry of the matrix is read. The strong rule refuses any pair whose clusters touch, and subdivides instead, so the diagonal is fringed with small dense blocks and no rank on the picture exceeds 5. The whole thing stores 27,008 numbers against 65,536 entries, and reproduces the matrix to 3.41·10⁻¹⁰.545545545455554545545545545454555555454545545545545455554545545545rows and columns, in the order the points arrivedark: kept dense · light: two thin factors, rank printedthe strong partitionblocks112kept dense46largest rank5numbers stored2.7·10⁴‖A − A_H‖ ⁄ ‖A‖3.4·10⁻¹⁰the picture is decidedbefore a number is read

112 blocks: 46 kept dense and 66 stored as two thin factors, whose ranks run from 4 to 5. The rule decides one thing — whether a pair of index clusters may be compressed — and it decides it from four numbers about where those clusters sit, before any entry of the matrix is read. The strong rule refuses any pair whose clusters touch, and subdivides instead, so the diagonal is fringed with small dense blocks and no rank on the picture exceeds 5. The whole thing stores 27,008 numbers against 65,536 entries, and reproduces the matrix to 3.41·10⁻¹⁰.

admissibility: "weak"

The arguments are the ones Built from products alone 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 256 × 256 kernel matrix partitioned by the weak rule, with each compressed block's rank46 blocks: 16 kept dense and 30 stored as two thin factors, whose ranks run from 8 to 12. The rule decides one thing — whether a pair of index clusters may be compressed — and it decides it from four numbers about where those clusters sit, before any entry of the matrix is read. The weak rule compresses every off-diagonal block there is, including the two touching halves at the top level, whose rank is the largest number on the picture and is the one that climbs as the matrix grows. The whole thing stores 24,064 numbers against 65,536 entries, and reproduces the matrix to 1.08·10⁻⁹.121210109988889988881010998888998888rows and columns, in the order the points arrivedark: kept dense · light: two thin factors, rank printedthe weak partitionblocks46kept dense16largest rank12numbers stored2.4·10⁴‖A − A_H‖ ⁄ ‖A‖1.1·10⁻⁹the picture is decidedbefore a number is read

46 blocks: 16 kept dense and 30 stored as two thin factors, whose ranks run from 8 to 12. The rule decides one thing — whether a pair of index clusters may be compressed — and it decides it from four numbers about where those clusters sit, before any entry of the matrix is read. The weak rule compresses every off-diagonal block there is, including the two touching halves at the top level, whose rank is the largest number on the picture and is the one that climbs as the matrix grows. The whole thing stores 24,064 numbers against 65,536 entries, and reproduces the matrix to 1.08·10⁻⁹.

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.

7 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.

a partition rule this file defines

a partition rule this site defines

an accuracy, not a rank

an admissibility constant inside the range the expansion converges over

and it has blocks to draw

matmul shapes agree

the partition still represents the matrix

Against the rule

It draws a decomposition and prints its residual. It calls represent, 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 141 of 264 generators — 126 print a residual and 15 are exempt with a published reason; 123 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.

Neither sparse nor dense

A block nobody can call sparse

A 96 × 96 block of a kernel matrix has ninety-six nonzero singular values and five that matter. It has no zero entries, it is not described by fewer numbers than it contains, and neither of the two ways this collection already knows to make a large matrix affordable applies to it.

Randomised, and the guarantee that changes kind

Built from products alone

A 512-square hierarchical representation, at a relative error of 4·10⁻⁷, from 256 applications of an operator that is never assembled. The compression route reads 262,144 entries; this one reads none, and pays for it with a factor of seven against the representation the entries would have given.

Neither sparse nor dense

The accuracy worth paying for

Used as a preconditioner, a hierarchical representation gets better at every accuracy — the iteration count falls monotonically all the way to the tightest tolerance. The total work does not. Its minimum sits at a rank-one preconditioner on an easy problem and six decades further along on a hard one.

Sparsity, and what elimination costs

The fill that is not independent

Eliminate both halves of a grid and what is left on the separator is 100 per cent nonzero — the sparsity field's result, unchanged. Its off-diagonal block is 11 by 12 and six columns describe it to eight digits. Renumber the separator and the same block needs all eleven.

Neither sparse nor dense

The kernel with nothing to compress

Hold the geometry fixed at q = ½, fix the wavelength, and scale the picture up by sixteen. A smooth kernel needs six columns at every scale. An oscillatory one needs twelve, sixteen, twenty-two, thirty-three, fifty-three, and there is no scale at which it stops.

Sparsity, and what elimination costs

The order that was right last time

A pivot order computed once and reused across a sequence saves the symbolic phase, and the price is that a pivot which was large may now be small. Replacing it with √u·‖A‖ costs eight orders of backward error and iterative refinement recovers a factor of 8.8 of them. Divide each row by its largest entry first and the same reuse costs nothing at all.

Neither sparse nor dense

The same matrix, numbered twice

One symmetric permutation. The condition number is 24.3948 either way to eight digits and the Frobenius norm is 6.13996414·10³ either way to twelve. The partition that stored 27,008 numbers now finds no admissible pair anywhere and stores all 65,536, and the format that compresses regardless stores 118,208.

Neither sparse nor dense

The size the rank does not notice

Sample a kernel block at 32, 64, 128 and 256 points a side and it needs five columns, five, five and five. Sample the touching block next to it at the same four sizes and it needs nine, eleven, twelve and thirteen. Same kernel, same accuracy, one number and a logarithm.

Neither sparse nor dense

The test that costs what it saves

The partition that refuses to compress a touching pair keeps every rank at five while the other lets them climb from nine to thirteen. It also stores more numbers at every size measured — 67,968 against 61,440 at n = 512 — and which of those two facts matters is a question about how large the problem is going to get.

Sparsity, and what elimination costs

What the symbolic phase can only bound

Without pivoting, the fill can be computed from the graph and the count is exact — 233 predicted, 233 measured. With pivoting it is 233 predicted and 242 measured, and what survives is a bound that is right at every threshold and loose by 1.7 times at the largest grid drawn.

Neither sparse nor dense

Which pairs are allowed to be small

A hierarchical representation is a partition of the matrix into blocks, and the rule that produces it reads four numbers per pair of index clusters and not one entry of the matrix. On a 256-square it yields 112 blocks, 66 of them stored as two thin factors, none of rank above five.

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