 Difference: AVFLogA012LeastSquares (12 vs. 13)

Revision 132015-03-27 - AlexanderFedotov

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 META TOPICPARENT name="AVFedotovLogA"
-- AlexanderFedotov - 2015-03-03
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that are complementary to indices in .

Consider the following iterative procedure.

Changed:
<
<
1. Start with an vector as an initial approximation to >
>
1. Start with an vector as an initial approximation to .

1. Make N steps or, equivalently, iterations for , thus finding vectors by minimizing over at the step :
Line: 181 to 181
\beta^{(k)}_i = \left\{ \begin{array}{rl}
Changed:
<
<
(\hat{ \beta }_{(g_k)})_i & \text{if } i \in g_k ,\
>
>
(\hat{ \beta }_{(g_k)}^{(k)})_i & \text{if } i \in g_k ,\
\beta^{(k-1)}_i & \text{if } i \notin g_k . \end{array} \right. \qquad , \qquad\qquad (4.1.3)

Changed:
<
<
where is the "point"
>
>
where is the "point"
where as a function of parameters , takes minimum (while the rest parameters are fixed at the values obtained in the previous iteration: Changed:
<
<
)
1. Repeat (4.3) infinitly, defining for >
>
) .
1. Repeat (4.1.3) infinitly, defining for (i.e. ) . One can expect that

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