An iterative method was developed for the adaptive inverse planning automation. The expected treatment dose in organs of interest constructed using the previous and on going treatment plans, and daily CBCT images was utilized to setup an initial criteria for each organ in the objective function. Following the initial inverse planning optimization, the achieved objective value was used to modify...

The main advantage of inverse iteration over the QR algorithm and Divide & Conquer for the symmetric tridiagonal eigenproblem is that subsets of eigenpairs can be computed at reduced cost. The MRRR algorithm (MRRR = Multiple Relatively Robust Representations) is a clever variant of inverse iteration without the need for reorthogonalization. stegr, the current version of MRRR in LAPACK 3.0, does...

Abstract The capacity for solving eigenstates with a quantum computer is key ultimately simulating physical systems. Here we propose inverse iteration eigensolvers, which exploit the power of computing classical method. A ingredient constructing an Hamiltonian as linear combination coherent evolution. We first consider continuous-variable mode (qumode) realizing such integral, weights being enc...

We describe a matrix multiply based block unsymmetric inverse iteration solver for upper Hessenberg matrices. Our kernel is robust in that it prevents overrow by scaling. It uses new techniques to ensure performance is not sacriiced when scaling is not necessary. Finally, we give results on a parallel implementation on an Intel Paragon TM supercomputer.

In this paper we analyse inexact inverse iteration for the real symmetric eigenvalue problem Av = λv. Our analysis is designed to apply to the case when A is large and sparse and where iterative methods are used to solve the shifted linear systems (A − σI)y = x which arise. We rst present a general convergence theory that is independent of the nature of the inexact solver used. Next we consider...

where U = V k−mV k−m+1 · · ·V k−1. For the L-BFGS, we need not explicitly store the approximated inverse Hessian matrix. Instead, we only require matrix-vector multiplications at each iteration, which can be implemented by a twoloop recursion with a time complexity of O(mn) (Jorge & Stephen, 1999). Thus, we only store 2m vectors of length n: sk−1, sk−2, · · · , sk−m and yk−1,yk−2, · · · ,yk−m w...

Inexact Newton regularization methods have been proposed by Hanke and Rieder for solving nonlinear ill-posed inverse problems. Every such a method consists of two components: an outer Newton iteration and an inner scheme providing increments by regularizing local linearized equations. The method is terminated by a discrepancy principle. In this paper we consider the inexact Newton regularizatio...

The performance of pipelined datapath implementations is measured basically by three parameters: the clock cycle length, the initiation interval between successive iterations (inverse of the throughput) and the iteration time (turn-around time). In this paper we present a new method for computing performance bounds of pipelined implementations: • Given an iterative behavior, a set of resource c...

In the present paper, we show that $S^*$ iteration method can be used to approximate fixed point of almost contraction mappings. Furthermore, we prove that this iteration method is equivalent to CR iteration method  and it produces a slow convergence rate compared to the CR iteration method for the class of almost contraction mappings. We also present table and graphic to support this result. F...