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xV*$45AI-4-BN_0&VxbRwYU26-hYue~7U=n1)5~rZgRaIXB{?w-~x__dMF{9kZga!5hZmb4% delta 35 jcmZo@U~Fh$oFFYI!N9=40>w-~x_6?EF{8xBga!5hZQcfF diff --git a/guide/diff_ops.html b/guide/diff_ops.html index 5cc166a1..56ac7813 100644 --- a/guide/diff_ops.html +++ b/guide/diff_ops.html @@ -292,7 +292,7 @@

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:

-

33c1483ab4b94ec79bbb98e78d23eb82

+

77f79eb0c7a3458e9025bedd035f0aae

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
index 49aeaae617c584e8075fdb0795e4f6bf1752771e..202fe72df7c9d0eb6901f9f3de380a8be3aefb66 100644
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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 a4a0a3d0c3be823b7720f1aae8e79d581c00f3d4..3f66b1aa4ad5b2ed362f39c80a4bab8bdee54e43 100644
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