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:
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  1. Start with an vector as an initial approximation to
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  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 :
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  \beta^{(k)}_i = \left\{ \begin{array}{rl}
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(\hat{ \beta }_{(g_k)})_i & \text{if } i \in g_k ,\
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(\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:
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<
where is the "point"
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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:
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<
)
  1. Repeat (4.3) infinitly, defining for
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   ) .
  1. Repeat (4.1.3) infinitly, defining for
     (i.e. ) . One can expect that
 
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