7,612 research outputs found
Optimal parameter for the SOR-like iteration method for solving the system of absolute value equations
The SOR-like iteration method for solving the absolute value equations~(AVE)
of finding a vector such that with is investigated. The convergence conditions of the SOR-like iteration method
proposed by Ke and Ma ([{\em Appl. Math. Comput.}, 311:195--202, 2017]) are
revisited and a new proof is given, which exhibits some insights in determining
the convergent region and the optimal iteration parameter. Along this line, the
optimal parameter which minimizes with and the approximate optimal parameter which
minimizes are explored.
The optimal and approximate optimal parameters are iteration-independent and
the bigger value of is, the smaller convergent region of the iteration
parameter is. Numerical results are presented to demonstrate that the
SOR-like iteration method with the optimal parameter is superior to that with
the approximate optimal parameter proposed by Guo, Wu and Li ([{\em Appl. Math.
Lett.}, 97:107--113, 2019]). In some situation, the SOR-like itration method
with the optimal parameter performs better, in terms of CPU time, than the
generalized Newton method (Mangasarian, [{\em Optim. Lett.}, 3:101--108, 2009])
for solving the AVE.Comment: 23 pages, 7 figures, 7 table
Tensor-Structured Coupled Cluster Theory
We derive and implement a new way of solving coupled cluster equations with
lower computational scaling. Our method is based on decomposition of both
amplitudes and two electron integrals, using a combination of tensor
hypercontraction and canonical polyadic decomposition. While the original
theory scales as with respect to the number of basis functions, we
demonstrate numerically that we achieve sub-millihartree difference from the
original theory with scaling. This is accomplished by solving directly
for the factors that decompose the cluster operator. The proposed scheme is
quite general and can be easily extended to other many-body methods
Row-Action Methods for Compressed Sensing
Compressed Sensing uses a small number of random, linear measurements to acquire a sparse signal. Nonlinear algorithms, such as l1minimization, are used to reconstruct the signal from the measured data. This paper proposes row-action methods as a computational approach to solving the l1optimization problem. This paper presents a specific row-action method and provides extensive empirical evidence that it is an effective technique for signal reconstruction. This approach offers several advantages over interior-point methods, including minimal storage and computational requirements, scalability, and robustness
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