Off-diagonal rank — the series
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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.
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A rank that is a number of digits
Ask a kernel block for two digits and it costs two columns; ask for fourteen and it costs nine. The curve is a straight line at 0.55 columns a decade, and the bound the geometry gives is a straight line too — at 3.32, which is the same shape and six times the price.
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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.
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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.
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The smaller cluster sets the rank
An oscillatory kernel block between two equal clusters needs a rank that grows without limit as they grow. Make the clusters unequal at the same separation ratio and the rank stops following the larger one: a target an eighth long against a source of four needs 8 columns where the square block of side four needs 33, and a target a third long against a source 130 wavelengths long needs 11. What decides the rank is the product of the two lengths over their distance — the Fresnel number, the count optics gives for the waves two apertures can exchange.