Fast log-decon with a quasi-Newton solver |

We define a local minimizer for and we assume that and satisfy the ``standard requirements'':

- is twice differentiable,
- ,
- is positive definite , i.e., for all ( denotes the adjoint).

Newton's method is an iterative process where the solution to the problem is updated as follows:

(1) |

where is the updated solution at iteration , the step length computed by a line search that ensures a sufficient decrease of and the Hessian (or second derivative). In many circumstances the inverse of the Hessian can't be computed directly. It happens for example when the matrix is too big or when operators are used rather than matrices. Fortunately we might be able to compute an approximation of the Hessian of . This strategy gives birth to quasi-Newton methods where the way in which the Hessian is computed determines the method (Kelley, 1999).

A possible update of the Hessian is given by the BFGS technique Fletcher (1970); Goldfarb (1970); Shanno (1970); Broyden (1969). The BFGS update is given by

where and . In practice, however, we rather write the previous equation in terms of the inverse matrices. We have then

(3) |

In addition, we use the history of the iterations to compute the new Hessian rather than a full storage of the matrix . This requires that a gradient step vector and a solution step vector are kept in memory after each iteration. Consequently this method might not been affordable with large data and model space. In the next section a modified version of the BFGS method that limits the storage needed to compute the update of the Hessian is proposed.

Fast log-decon with a quasi-Newton solver |

2012-10-29