this study develops and analyzes preconditioned krylov subspace methods to solve linear systemsarising from discretization of the time-independent space-fractional models. first, we apply shifted grunwald formulas to obtain a stable finite difference approximation to fractional advection-diffusion equations. then, we employee two preconditioned iterative methods, namely, the preconditioned gene...

preconditioning (pc), is an approach to improve therapeutic potential of stem cells against ischemic environment. pc has several advantages over other therapeutic techniques as this results in increase of transplanted stem cells recruitment, retention, survival and subsequently the induction of a more supportive environment within the damaged tissue via secretion of angiogenic factors. special ...

This report is devoted to preconditioning techniques for the matrix-free truncated Newton method. After a review of basic known approaches, we propose new results concerning tridiagonal and pentadiagonal preconditioners based on the standard BFGS updates and on numerical differentiation. Furthermore, we present results of extensive numerical experiments serving for the careful comparison of sui...

This study develops and analyzes preconditioned Krylov subspace methods to solve linear systems arising from discretization of the time-independent space-fractional models. First, we apply shifted Grunwald formulas to obtain a stable finite difference approximation to fractional advection-diffusion equations. Then, we employee two preconditioned iterative methods, namely, the preconditioned gen...

Multiplicative preconditioning is a popular SVD-based techniques for the solution of linear systems of equations. Our novel SVD-free additive preconditioners are more readily available and better preserve matrix structure. We study their generation and their affect on conditioning of the input matrix. In other papers we combine additive preconditioning with aggregation and other relevant techni...