We propose practical stopping criteria for the iterative solution of sparse linear least squares (LS) problems. Although we focus our discussion on the algorithm LSQR of Paige and ...
For the approximation of time-dependent data tensors and of solutions to tensor differential equations by tensors of low Tucker rank, we study a computational approach that can be ...
The polar decomposition of a square matrix has been generalized by several authors to scalar products on Rn or Cn given by a bilinear or sesquilinear form. Previous work has focuse...
Computational resolution enhancement (superresolution) is generally regarded as a memory intensive process due to the large matrix-vector calculations involved. In this paper, a de...
Optimality systems and their linearizations arising in optimal control of partial differential equations with pointwise control and (regularized) state constraints are considered. ...
It is well-known that two-level and multi-level preconditioned conjugate gradient (PCG) methods provide efficient techniques for solving large and sparse linear systems whose coeff...
J. M. Tang, S. P. MacLachlan, Reinhard Nabben, C. ...
Abstract. Linear inverse problems with uncertain measurement matrices appear in many different applications. One of the standard techniques for solving such problems is the total l...