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Recall the fitting goals (18)
 |
(18) |
Without preconditioning we have the search direction
![$\displaystyle \Delta \bold m_{\rm bad} \quad =\quad \left[ \begin{array}{cc} \b...
...ray} \right] \left[ \begin{array}{c} \bold r_d \\ \bold r_m \end{array} \right]$](img86.png) |
(19) |
and with preconditioning we have the search direction
![$\displaystyle \Delta \bold p_{\rm good} \quad =\quad \left[ \begin{array}{cc} (...
...ray} \right] \left[ \begin{array}{c} \bold r_d \\ \bold r_m \end{array} \right]$](img87.png) |
(20) |
The essential feature of preconditioning is not that we perform
the iterative optimization in terms of the variable
.
The essential feature is that we use a search direction
that is a gradient with respect to
not
.
Using
we have
.
This enables us to define a good search direction in model space.
 |
(21) |
Define the gradient by
and
notice that
.
 |
(22) |
The search direction (22)
shows a positive-definite operator scaling the gradient.
Each component of any gradient vector is independent of each other.
All independently point a direction for descent.
Obviously, each can be scaled by any positive number.
Now we have found that we can also scale a gradient vector by
a positive definite matrix and we can still expect
the conjugate-direction algorithm to descend, as always,
to the ``exact'' answer in a finite number of steps.
This is because modifying the search direction with
is equivalent to solving
a conjugate-gradient problem in
.
Subsections
Next: The meaning of the
Up: Preconditioning
Previous: Need for an invertible
2011-08-20