88 research outputs found
A Subspace Shift Technique for Nonsymmetric Algebraic Riccati Equations
The worst situation in computing the minimal nonnegative solution of a
nonsymmetric algebraic Riccati equation associated with an M-matrix occurs when
the corresponding linearizing matrix has two very small eigenvalues, one with
positive and one with negative real part. When both these eigenvalues are
exactly zero, the problem is called critical or null recurrent. While in this
case the problem is ill-conditioned and the convergence of the algorithms based
on matrix iterations is slow, there exist some techniques to remove the
singularity and transform the problem to a well-behaved one. Ill-conditioning
and slow convergence appear also in close-to-critical problems, but when none
of the eigenvalues is exactly zero the techniques used for the critical case
cannot be applied.
In this paper, we introduce a new method to accelerate the convergence
properties of the iterations also in close-to-critical cases, by working on the
invariant subspace associated with the problematic eigenvalues as a whole. We
present a theoretical analysis and several numerical experiments which confirm
the efficiency of the new method
Two-Step Relaxation Newton Method for Nonsymmetric Algebraic Riccati Equations Arising from Transport Theory
We propose a new idea to construct an effective algorithm to compute the minimal positive solution of the nonsymmetric algebraic Riccati equations arising from transport theory. For a class of these equations, an important feature is that the minimal positive solution can be obtained by computing the minimal positive solution of a couple of fixed-point equations with vector form. Based on the fixed-point vector equations, we introduce a new algorithm, namely,
two-step relaxation Newton, derived by combining two different relaxation Newton methods to compute the minimal positive solution. The monotone convergence of the solution sequence generated by this new algorithm is established. Numerical results are given to show the advantages of the new algorithm for the nonsymmetric algebraic Riccati equations in vector form
Approximate solutions to large nonsymmetric differential Riccati problems with applications to transport theory
In the present paper, we consider large scale nonsymmetric differential
matrix Riccati equations with low rank right hand sides. These matrix equations
appear in many applications such as control theory, transport theory, applied
probability and others. We show how to apply Krylov-type methods such as the
extended block Arnoldi algorithm to get low rank approximate solutions. The
initial problem is projected onto small subspaces to get low dimensional
nonsymmetric differential equations that are solved using the exponential
approximation or via other integration schemes such as Backward Differentiation
Formula (BDF) or Rosenbrok method. We also show how these technique could be
easily used to solve some problems from the well known transport equation. Some
numerical experiments are given to illustrate the application of the proposed
methods to large-scale problem
Explicit Solutions for a Riccati Equation from Transport Theory
This is the published version, also available here: http://dx.doi.org/10.1137/070708743.We derive formulas for the minimal positive solution of a particular nonsymmetric Riccati equation arising in transport theory. The formulas are based on the eigenvalues of an associated matrix. We use the formulas to explore some new properties of the minimal positive solution and to derive fast and highly accurate numerical methods. Some numerical tests demonstrate the properties of the new methods
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