From b4b87ebfe54833870f9df2ef4234ce36cfa3e426 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Tue, 19 Nov 2024 15:18:01 +0000 Subject: [PATCH] Update generated HTML files [skip ci] --- .../linop-5.pdf | Bin 12561 -> 12561 bytes .../linop-16_01.pdf | Bin 22006 -> 22006 bytes .../linop-7_00.pdf | Bin 58677 -> 58677 bytes .../linop-18_02.pdf | Bin 21402 -> 21402 bytes .../linop-17_01.pdf | Bin 22274 -> 22274 bytes .../sampler-1_01_00.pdf | Bin 22618 -> 22135 bytes .../linop-8_00.pdf | Bin 27056 -> 27056 bytes .../linop-9_02.pdf | Bin 12597 -> 12597 bytes .../linop-14.pdf | Bin 39783 -> 39783 bytes .../linop-18_03.pdf | Bin 23017 -> 23017 bytes .../util-2.pdf | Bin 158397 -> 158397 bytes .../linop-9_00.pdf | Bin 13185 -> 13185 bytes .../util-1.pdf | Bin 54648 -> 54648 bytes .../linop-1.pdf | Bin 12263 -> 12263 bytes .../linop-15.pdf | Bin 71788 -> 71788 bytes .../abc-1.pdf | Bin 11893 -> 11893 bytes .../linop-8_01.pdf | Bin 12811 -> 12811 bytes .../sampler-1_01_00.png | Bin 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Partial Derivatives \[\mathbf{D} f [n] = f[n+1] - f[n]\]

As we will see below, this is the forward finite difference approximation. This could be implemented in matrix-form, in which case it would look like this:

-

9e72b53961dd4586bbc911d507631a5e

+

9d344b6c252640558552ce2e39096e97

Or, it could be instead implemented via a for loop, in which the case of large input signals, would not require storing a large matrix:

[1]:
diff --git a/plot_directive/api/abc-1.pdf b/plot_directive/api/abc-1.pdf
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diff --git a/plot_directive/api/experimental/sampler-1_00_00.pdf b/plot_directive/api/experimental/sampler-1_00_00.pdf
index f8a002b2ead2b6a0c057d3431f5444e46264a495..38ce6e2813b141ce9a8cae1b35d893aa2697d31b 100644
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