README.md - Removed junk after error+erasure section

README.md

@@ -1514,140 +1514,3 @@ And some caveats:
        src="https://latex.codecogs.com/svg.image?C%28x%29%20%3d%20C%27%28x%29%20%2d%20%5csum_%7bj%20%5cin%20E%20%5ccup%20F%7d%20Y_j%20x%5ej"
    >
    </p>
-
-
-    vvv TODO vvv
-
-the algorithm as normal
-
-, which tells
-   us everything we need to know to find the location of errors and
-   erasures:
-
-
-   We can then continue the algorithm as normal, finding errors/erasures
-   where $\Lambda(X_j^{-1}) = 0$, and repairing them with Forney's
-   algorithm.
-
-
-
-
-
-
-   vvv TODO vvv
-
-   Which has all of the properties of t
-
-
-
-
-
-   Use Berlekamp-Massey to find the error-locator polynomial
-   $\Lambda_E(x)$ from the Forney syndromes $S_{Ei}$.
-
-
-
-
-
-
-   need to sort of hide the effects of our erasures from the 
-
-   In order to find the error-locator polynomial $\Lambda_E(x)$, 
-
-
-
-
-
-   Renaming $\Lambda(x)$ to the error-and-erasure-locator polynomial, we
-   can 
-
-   First note we can split the error-locator polynomial $\Lambda(x)$ into
-   separate 
-
-
-
-
-
-
-
-
-
-   vvv TODO vvv
-
-
-
-   With Reed-Solomon, each unknown-location error requires 2 bytes of ECC
-   to find and repair, but each known-location error, usually called
-   "erasures", require only 1 byte of ECC to correct.
-
-
-
-
-
-
-   All of the above math assumes we don't know the location of errors,
-   since this is usually the case for block devices, and requires $2e$
-   bytes of `ecc_size` to find $e$ errors.
-
-   But if we know the location of errors, via parity bits or other
-   side-channels, we can actually do a bit better and find $e$ errors
-   with only $e$ bytes of `ecc_size`. We usually call these "erasures".
-
-   You can even mix and match errors and erasures as long as you have
-   enough `ecc_size`, with each error needing 2 bytes of `ecc_size`, and
-   each erasure needing 1 bytes of `ecc_size`.
-
-   TODO
-
-   Find the erasure-locator polynomial $\Lambda_F(x)$:
-
-   <p align="center">
-   <img
-       alt="\Lambda_F(x) = \prod_{j \in F} \left(1 - X_j x\right)"
-       src="https://latex.codecogs.com/svg.image?%5cLambda_F%28x%29%20%3d%20%5cprod_%7bj%20%5cin%20F%7d%20%5cleft%28%31%20%2d%20X_j%20x%5cright%29"
-   >
-   </p>
-   
-   Note:
-
-   <p align="center">
-   <img
-       alt="\Lambda(x) = \Lambda_E(x) \Lambda_F(x)"
-       src="https://latex.codecogs.com/svg.image?%5cLambda%28x%29%20%3d%20%5cLambda_E%28x%29%20%5cLambda_F%28x%29"
-   >
-   </p>
-   
-   Find the Forney syndromes $S_{Ei}$:
-
-   <p align="center">
-   <img
-       alt="S_E(x) = S(x) \Lambda_F(x) \bmod x^n"
-       src="https://latex.codecogs.com/svg.image?S_E%28x%29%20%3d%20S%28x%29%20%5cLambda_F%28x%29%20%5cbmod%20x%5en"
-   >
-   </p>
-   
-   Note:
-
-   <p align="center">
-   <img
-       alt="\begin{aligned} \Omega(x) &= S(x)\Lambda(x) \bmod x^n \\ &= S(x)\Lambda_E(x)\Lambda_F(x) \bmod x^n \\ &= S_F(x)\Lambda_E(x) \bmod x^n \end{aligned}"
-       src="https://latex.codecogs.com/svg.image?%5cbegin%7baligned%7d%20%5cOmega%28x%29%20%26%3d%20S%28x%29%5cLambda%28x%29%20%5cbmod%20x%5en%20%5c%5c%20%26%3d%20S%28x%29%5cLambda_E%28x%29%5cLambda_F%28x%29%20%5cbmod%20x%5en%20%5c%5c%20%26%3d%20S_F%28x%29%5cLambda_E%28x%29%20%5cbmod%20x%5en%20%5cend%7baligned%7d"
-   >
-   </p>
-   
-   Use Berlekamp-Massey to find the error-locator polynomial
-   $\Lambda_E(x)$ from the Forney syndromes $S_{Ei}$.
-
-
-   Combine:
-
-   <p align="center">
-   <img
-       alt="\Lambda(x) = \Lambda_E(x) \Lambda_F(x)"
-       src="https://latex.codecogs.com/svg.image?%5cLambda%28x%29%20%3d%20%5cLambda_E%28x%29%20%5cLambda_F%28x%29"
-   >
-   </p>
-   
-   And continue as normal, finding $X_j$ where $\Lambda(X_j^{-1})=0$
-   and solving for $Y_j$ where
-   $Y_j = X_j \frac{\Omega(X_j^{-1})}{\Lambda'(X_j^{-1})}$.