Generator

curvature-trail

One function in the curvature library, called 4 times across 1 essay. Below: what it draws at its defaults, what it draws at every value an essay asks for, the 6 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 the curvature pᵀap/pᵀp along conjugate gradients on a matrix with one eigenvalue at −10^-1. The matrix is 40×40, positive definite apart from a single eigenvalue at -0.1. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.43, 2.72, 1.71, 0.687 before turning negative at step 6, where it is -0.02656. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 26.6 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.

curvature-trail is one function in lib/figures/curvature.js — negative curvature — the division that cannot be done, as an output. 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.

The curvature pᵀAp/pᵀp along conjugate gradients on a matrix with one eigenvalue at −10^-1The matrix is 40×40, positive definite apart from a single eigenvalue at -0.1. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.43, 2.72, 1.71, 0.687 before turning negative at step 6, where it is -0.02656. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 26.6 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.012345678-7-5-3-11357conjugate gradient steppᵀAp ⁄ pᵀpλ_min = -0.1positive: a step existsnegative: a certificate existsone matrix, two questionsstep it turns at6quotient there-0.027share of λ_min recovered0.27λ_min, by construction-0.1MINRES steps on the same system37the division that cannot be doneis the answer to a different question

The matrix is 40×40, positive definite apart from a single eigenvalue at -0.1. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.43, 2.72, 1.71, 0.687 before turning negative at step 6, where it is -0.02656. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 26.6 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.

logLambda: -1

The arguments are the ones The division that cannot be done 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.

The curvature pᵀAp/pᵀp along conjugate gradients on a matrix with one eigenvalue at −10^-1The matrix is 40×40, positive definite apart from a single eigenvalue at -0.1. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.43, 2.72, 1.71, 0.687 before turning negative at step 6, where it is -0.02656. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 26.6 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.012345678-7-5-3-11357conjugate gradient steppᵀAp ⁄ pᵀpλ_min = -0.1positive: a step existsnegative: a certificate existsone matrix, two questionsstep it turns at6quotient there-0.027share of λ_min recovered0.27λ_min, by construction-0.1MINRES steps on the same system37the division that cannot be doneis the answer to a different question

The matrix is 40×40, positive definite apart from a single eigenvalue at -0.1. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.43, 2.72, 1.71, 0.687 before turning negative at step 6, where it is -0.02656. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 26.6 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.

logLambda: 1

The arguments are the ones The division that cannot be done 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.

The curvature pᵀAp/pᵀp along conjugate gradients on a matrix with one eigenvalue at −10^1The matrix is 40×40, positive definite apart from a single eigenvalue at -10. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.09, -3.51 before turning negative at step 2, where it is -3.507. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 35.1 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.012345678-7-5-3-11357conjugate gradient steppᵀAp ⁄ pᵀppositive: a step existsnegative: a certificate existsone matrix, two questionsstep it turns at2quotient there-3.5share of λ_min recovered0.35λ_min, by construction-10MINRES steps on the same system37the division that cannot be doneis the answer to a different question

The matrix is 40×40, positive definite apart from a single eigenvalue at -10. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.09, -3.51 before turning negative at step 2, where it is -3.507. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 35.1 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.

logLambda: -3

The arguments are the ones The division that cannot be done 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.

The curvature pᵀAp/pᵀp along conjugate gradients on a matrix with one eigenvalue at −10^-3The matrix is 40×40, positive definite apart from a single eigenvalue at -0.001. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.44, 2.76, 1.83, 0.884 before turning negative at step 9, where it is -2.811·10⁻⁴. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 28.1 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 39 steps.012345678910-7-5-3-11357conjugate gradient steppᵀAp ⁄ pᵀpλ_min = -0.001positive: a step existsnegative: a certificate existsone matrix, two questionsstep it turns at9quotient there-2.8·10⁻⁴share of λ_min recovered0.28λ_min, by construction-0.001MINRES steps on the same system39the division that cannot be doneis the answer to a different question

The matrix is 40×40, positive definite apart from a single eigenvalue at -0.001. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.44, 2.76, 1.83, 0.884 before turning negative at step 9, where it is -2.811·10⁻⁴. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 28.1 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 39 steps.

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.

6 distinct claims across 4 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 negative eigenvalue inside the range the sweep covers

a power of ten rather than an exponent literal

a size the repeated products can afford

and the direction it stopped on has negative curvature

conjugate gradients meets a non-positive curvature

while MINRES, which never forms the quantity, solves the system

Against the rule

It draws a decomposition and prints its residual. It calls cgCurvature, minres, 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 113 of 219 generators — 98 print a residual and 15 are exempt with a published reason; 106 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.

The whole library · All essays · What must fail