Iterative algorithms for large-scale eigenpair computation are mostly based subspace projections consisting of two main steps: a subspace update (SU) step that generates bases for approximate eigenspaces, followed by a Rayleigh-Ritz (RR) projection step that extracts approximate eigenpairs. A predominant methodology for the SU step makes use of Krylov subspaces that builds orthonormal bases pie...

Community detection is an important information mining task in many fields including computer science, social sciences, biology and physics. For increasingly common large network data sets, global community detection is prohibitively expensive, and attention has shifted to methods that mine local communities, i.e. methods that identify all latent members of a particular community from a few lab...

For efficient simulation of state-of-the-art dynamical systems as arise in all aspects of engineering, the development of reduced-order models is of paramount importance. While linear reduction techniques have received considerable study, increasingly nonlinear model reduction is becoming a significant field of interest. From a circuits and systems viewpoint, systems involving micromachined dev...

Many problems in engineering and physics require the solution of a large sequence of linear systems. We can reduce the cost of solving subsequent systems in the sequence by recycling information from previous systems. We consider two di erent approaches. For several model problems, we demonstrate that we can reduce the iteration count required to solve a linear system by a factor of two. We con...

This paper proposes a new approach for choosing the regularization parameters in multiparameter regularization methods when applied to approximate the solution of linear discrete ill-posed problems. We consider both direct methods, such as Tikhonov regularization with two or more regularization terms, and iterative methods based on the projection of a Tikhonov-regularized problem onto Krylov su...

The aim of this paper is to examine a numerical method for the computation of approximate solution of the continuous-time algebraic Riccati equation using Krylov subspace matrix. First of all, Global Arnoldi process is initiated to construct an orthonormal basis. In addition, Krylov subspace matrix is employed as projection method because it is one of the frequently referred method in the liter...

Solving the trust-region subproblem (TRS) plays a key role in numerical optimization and many other applications. The generalized Lanczos (GLTR) method is well-known type approach for solving large-scale TRS. projects original TRS onto sequence of lower dimensional Krylov subspaces, whose orthonormal bases are generated by symmetric process, computes approximate solutions from underlying subspa...

Dynamical systems are mathematical models characterized by a set of differential or difference equations. Due to the increasing demand for more accuracy, the number of equations involved may reach the order of thousands and even millions. With so many equations, it often becomes computationally cumbersome to work with these large-scale dynamical systems. Model reduction aims to replace the orig...

INCORPORATING KRYLOV SUBSPACE METHODS IN THE ETDRK4 SCHEME by Jeffrey H. Allen The University of Wisconsin–Milwaukee, 2014 Under the Supervision of Professor Bruce Wade A modification of the (2, 2)-Padé algorithm developed by Wade et al. for implementing the exponential time differencing fourth order Runge-Kutta (ETDRK4) method is introduced. The main computational difficulty in implementing th...

Today, we will discuss a class of methods based on repeated applications of the matrix A to some vector (either the residual or the right hand side). These methods are called Krylov methods. We will present two important Krylov methods: the famous Conjugate Gradients method, which can be viewed as an improvement of steepest descent, applicable only to symmetric positive-definite matrices; and t...