Lower Spectral Radius Research Articles

Genetic programming for iterative numerical methods

We introduce GPLS (Genetic Programming for Linear Systems) as a GP system that finds mathematical expressions defining an iteration matrix. Stationary iterative methods use this iteration matrix to solve a system of linear equations numerically. GPLS aims at finding iteration matrices with a low spectral radius and a high sparsity, since these properties ensure a fast error reduction of the numerical solution method and enable the efficient implementation of the methods on parallel computer architectures. We study GPLS for various types of system matrices and find that it easily outperforms classical approaches like the Gauss–Seidel and Jacobi methods. GPLS not only finds iteration matrices for linear systems with a much lower spectral radius, but also iteration matrices for problems where classical approaches fail. Additionally, solutions found by GPLS for small problem instances show also good performance for larger instances of the same problem.

On the spectral theory of positive operators and PDE applications

The strong version of the Krein-Rutman theorem requires that the positive cone of the Banach space has nonempty interior and the compact map is strongly positive, mapping nonzero points in the cone into its interior. In this paper, we first generalize this version of the Krein-Rutman theorem to the case of 'semi-strongly positive' operators; and we prove it in a totally elementary fashion. We then prove the equivalence of semi-strong positivity and irreducibility in a Banach lattice, linking the afore-mentioned result with the Krein-Rutman theorem for irreducible operators. One of the things we emphasize is to use 'upper and lower spectral radii' to characterize, in the fashion of Collatz-Wielandt formula for nonnegative irreducible matrices, the principal eigenvalue of these operators. For reducible operators, we prove that the lower spectral radius always serves as the least upper bound of the set of eigenvalues pertaining to positive eigenvectors, and the upper spectral radius the greatest lower bound of the set. Finally, we demonstrate the full power of these Krein-Rutman theorems on some PDE examples such as elliptic eigenvalue problems on non-smooth domains, and cooperative systems which may or may not be fully coupled, by using as few PDE tools as possible.

A new version of KSOR method with lower number of iterations and lower spectral radius

In this paper, we propose a new method for solving linear algebraic systems based on the KSOR method. The new approach introduces a new coefficient in the assumed updated section from the KSOR method. It obtains the same solution as KSOR for a given linear algebraic system, but in order to get this, we have to modify the diagonal of the coefficient matrix of the initial system. The new method reaches the solution in fewer steps than KSOR on certain examples, but we have an additional step for the modification mentioned above with O(m) complexity, while a normal step of iteration has $$O(m^2)$$ complexity, so overall our method has better performances than KSOR method. Furthermore, on certain examples, the spectral radius of the iteration matrix of the new method is strictly less than the one of KSOR and MKSOR methods.

Minimax joint spectral radius and stabilizability of discrete-time linear switching control systems

To estimate the growth rate of matrix products $A_{n}\cdots A_{1}$ with factors from some set of matrices $\mathcal{A}$, such numeric quantities as the joint spectral radius $\rho(\mathcal{A})$ and the lower spectral radius $\check{\rho}(\mathcal{A})$ are traditionally used. The first of these quantities characterizes the maximum growth rate of the norms of the corresponding products, while the second one characterizes the minimal growth rate. In the theory of discrete-time linear switching systems, the inequality $\rho(\mathcal{A})<1$ serves as a criterion for the stability of a system, and the inequality $\check{\rho}(\mathcal{A})<1 $ as a criterion for stabilizability. For matrix products $A_{n}B_{n}\cdots A_{1}B_{1}$ with factors $A_{i}\in\mathcal{A}$ and $B_{i}\in\mathcal{B}$, where $\mathcal{A}$ and $\mathcal{B}$ are some sets of matrices, we introduce the quantities $\mu(\mathcal{A},\mathcal{B})$ and $\eta(\mathcal{A},\mathcal{B})$, called the lower and upper minimax joint spectral radius of the pair $\{\mathcal{A},\mathcal{B}\}$, respectively, which characterize the maximum growth rate of the matrix products $A_{n}B_{n}\cdots A_{1}B_{1}$ over all sets of matrices $A_{i}\in\mathcal{A}$ and the minimal growth rate over all sets of matrices $B_{i}\in\mathcal{B}$. In this sense, the minimax joint spectral radii can be considered as generalizations of both the joint and lower spectral radii. As an application of the minimax joint spectral radii, it is shown how these quantities can be used to analyze the stabilizability of discrete-time linear switching control systems in the presence of uncontrolled external disturbances of the plant.

Exact Computation of Joint Spectral Characteristics of Linear Operators

We address the problem of the exact computation of two joint spectral characteristics of a family of linear operators, the joint spectral radius (JSR) and the lower spectral radius (LSR), which are well-known different generalizations to a set of operators of the usual spectral radius of a linear operator. In this paper we develop a method which—under suitable assumptions—allows us to compute the JSR and the LSR of a finite family of matrices exactly. We remark that so far no algorithm has been available in the literature to compute the LSR exactly. The paper presents necessary theoretical results on extremal norms (and on extremal antinorms) of linear operators, which constitute the basic tools of our procedures, and a detailed description of the corresponding algorithms for the computation of the JSR and LSR (the last one restricted to families sharing an invariant cone). The algorithms are easily implemented, and their descriptions are short. If the algorithms terminate in finite time, then they construct an extremal norm (in the JSR case) or antinorm (in the LSR case) and find their exact values; otherwise, they provide upper and lower bounds that both converge to the exact values. A theoretical criterion for termination in finite time is also derived. According to numerical experiments, the algorithm for the JSR finds the exact value for the vast majority of matrix families in dimensions ≤20. For nonnegative matrices it works faster and finds the JSR in dimensions of order 100 within a few iterations; the same is observed for the algorithm computing the LSR. To illustrate the efficiency of the new method, we apply it to give answers to several conjectures which have been recently stated in combinatorics, number theory, and formal language theory.

The Lyapunov exponent and joint spectral radius of pairs of matrices are hard?when not impossible?to compute and to approximate

We analyze the computability and the complexity of various definitions of spectral radii for sets of matrices. We show that the joint and generalized spectral radii of two integer matrices are not approximable in polynomial time, and that two related quantities—the lower spectral radius and the largest Lyapunov exponent—are not algorithmically approximable.