11 research outputs found
ESTIMATING THE CONVERGENCE RATE OF FUNCTIONAL ITERATIONS FOR SOLVING QUADRATIC MATRIX EQUATIONS ARISING IN HYPERBOLIC QUADRATIC EIGENVALUE PROBLEMS (Study on Nonlinear Analysis and Convex Analysis)
We consider Bernoulli's method for solving quadratic matrix equations (QMEs) having form Q(X) = AX^2 +BX+ C = 0 arising in hyperbolic quadratic eigenvalue problems (QEPs) and quasi-birth-death problems (QBDs) where A, B, C ∈ R^[m×m] satisfy Esenfeld's condition [8]. First, we analyze the exsistence of a solution and the convergence of the methods. Second, we sharpen bounds of the rates of convergence. Finally, in numerical experimentations, we show that the modified bounds give appropriate estimations of the numbers of iterations
Shift techniques for Quasi-Birth and Death processes: canonical factorizations and matrix equations
We revisit the shift technique applied to Quasi-Birth and Death (QBD)
processes (He, Meini, Rhee, SIAM J. Matrix Anal. Appl., 2001) by bringing the
attention to the existence and properties of canonical factorizations. To this
regard, we prove new results concerning the solutions of the quadratic matrix
equations associated with the QBD. These results find applications to the
solution of the Poisson equation for QBDs
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
General solution of the Poisson equation for Quasi-Birth-and-Death processes
We consider the Poisson equation , where
is the transition matrix of a Quasi-Birth-and-Death (QBD) process with
infinitely many levels, is a given infinite dimensional vector and is the unknown. Our main result is to provide the general solution of this
equation. To this purpose we use the block tridiagonal and block Toeplitz
structure of the matrix to obtain a set of matrix difference equations,
which are solved by constructing suitable resolvent triples
A Novel Zeroing Neural Network for Solving Time-Varying Quadratic Matrix Equations against Linear Noises
The solving of quadratic matrix equations is a fundamental issue which essentially exists in the optimal control domain. However, noises exerted on the coefficients of quadratic matrix equations may affect the accuracy of the solutions. In order to solve the time-varying quadratic matrix equation problem under linear noise, a new error-processing design formula is proposed, and a resultant novel zeroing neural network model is developed. The new design formula incorporates a second-order error-processing manner, and the double-integration-enhanced zeroing neural network (DIEZNN) model is further proposed for solving time-varying quadratic matrix equations subject to linear noises. Compared with the original zeroing neural network (OZNN) model, finite-time zeroing neural network (FTZNN) model and integration-enhanced zeroing neural network (IEZNN) model, the DIEZNN model shows the superiority of its solution under linear noise; that is, when solving the problem of a time-varying quadratic matrix equation in the environment of linear noise, the residual error of the existing model will maintain a large level due to the influence of linear noise, which will eventually lead to the solution’s failure. The newly proposed DIEZNN model can guarantee a normal solution to the time-varying quadratic matrix equation task no matter how much linear noise there is. In addition, the theoretical analysis proves that the neural state of the DIEZNN model can converge to the theoretical solution even under linear noise. The computer simulation results further substantiate the superiority of the DIEZNN model in solving time-varying quadratic matrix equations under linear noise