Some progress on BACH, added pseudocode for better understanding

pseudocode/bach_1.typ

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+#import "@preview/lovelace:0.3.0": *
+
+
+#pseudocode-list(booktabs: true, numbered-title: [Center Point Approximation])[
+  + *input*: $bold(cal(o))_"first", bold(x), t, M$
+  + *lists*: optimal weights $bold(h)_"opt"$
+  + $bold(cal(o)) arrow.l bold(cal(o))_"first"$
+  + *repeat* t times: 
+    + *perform* @alg:best_appr for all input values with $bold(cal(o))$:
+      + *update* $bold(h)_"opt"$ with returned weights
+      + $bold(z)_"opt" arrow.l$ all returned linear combinations 
+    + *sort* $bold(z)_"opt"$ in ascending order
+    + *define* new quantizer $cal(Q)^*$ using the @ecdf based on $bold(z)_"opt"$
+    + *update* $bold(cal(o))$ with newly found quantizer step centers
+  + *return* $bold(h)_"opt"$ 
+]

pseudocode/bach_find_best_appr.typ

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+#import "@preview/lovelace:0.3.0": *
+
+#pseudocode-list(booktabs: true, numbered-title: [Find best approximation])[
+  + *inputs*:
+    + $bold(y)$ input values for linear combinations
+    + $bold(cal(o))$ list of optimal points
+  + *output*: $(bold(h), z_"opt")$
+    //+ n number of summands in linear combination
+  + *calculate* all possible linear combinations $bold(z)$ with @eq:z_eq
+  + *calculate* matrix $bold(cal(A))$ with $a_"ij" = abs(z_i - cal(o)_j)$
+  + *return* weights $bold(h)$ for $z_"opt" = op("argmin")(bold(cal(A)))$ and $z_"opt"$
+]