Preconditioned iterative solution methods are compared with the direct Gaussian elimination method to solve dense linear systems Ax = b which originate from crack propagation problems, modeled and discretized by boundary element (BEM) techniques. Numerical experiments are presented and compared with the direct solution method available in a commercial BEM package. The experiments show that the ...

We propose an iterative gradient descent procedure for computing approximate solutions for the scenario-based mean-CVaR portfolio selection problem. This procedure is based on an algorithm proposed by Nesterov [13] for solving non-smooth convex optimization problems. Our procedure does not require any linear programming solver and in many cases the iterative steps can be solved in closed form. ...

The limiting factor for efficiency of sparse linear solvers is the memory bandwidth. In this work, we describe a fast Conjugate Gradient solver for unstructured problems, which runs on multiple GPUs installed on a single mainboard. The solver achieves double precision accuracy with single precision GPUs, using a mixed precision iterative refinement algorithm. To achieve high computation speed, ...

This paper presents the adaptive integral method (AIM) utilized to solve scattering problem of mixed dielectric/conducting objects. The scattering problem is formulated using the Poggio-MillerChang-Harrington-Wu-Tsai (PMCHWT) formulation and the electric field integral equation approach for the dielectric and conducting bodies, respectively. The integral equations solved using these approaches ...

The increasing gap between processor performance and memory access time warrants the re-examination of data movement in iterative linear solver algorithms. For this reason, we explore and establish the feasibility of modifying a standard iterative linear solver algorithm in a manner that reduces the movement of data through memory. In particular, we present an alternative to the restarted GMRES...

Data mining and classification is a growing and important field in bioinformatics. Machine learning algorithms such as support vector machines can be used with genetic information to predict disease susceptibility. In particular, single nucleotide polymorphisms have been analyzed to classify an individual into "sick" or "healthy" categories for a specific genetic disorder. The most computationa...