Kapil Ahuja

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Quantum Monte Carlo (QMC) methods are often used to calculate properties of many body quantum systems. The main cost of many QMC methods, for example, the variational Monte Carlo (VMC) method, is in constructing a sequence of Slater matrices and computing the ratios of determinants for successive Slater matrices. Recent work has improved the scaling of(More)
Science and engineering problems frequently require solving a sequence of dual linear systems. Besides having to store only few Lanczos vectors, using the BiConjugate Gradient method (BiCG) to solve dual linear systems has advantages for specific applications. For example, using BiCG to solve the dual linear systems arising in interpolatory model reduction(More)
Probability-one homotopy algorithms have strong convergence characteristics under mild assumptions. Such algorithms for mixed complementarity problems (MCPs) have potentially wide impact because MCPs are pervasive in science and engineering. A probability-one homotopy algorithm for MCPs was developed earlier by Billups and Watson based on the default(More)
Most user focused data mining techniques involve purchase pattern analysis, targeted at strictly-formatted database-like transaction records. Most personalization systems employ explicitly provided user preferences rather than implicit rating data obtained automatically by collecting users' interactions. In this paper, we show that in complex information(More)
In a variety of applications such as learning, we need to integrate multimedia information into convenient packages (like presentations). The challenges involved in this process are: Selecting or working with information elements at sub-document level while retaining the original context; describing the integration or packaging of such elements; and making(More)
Krylov subspace recycling is a process for accelerating the convergence of sequences of linear systems. Based on this technique we have recently developed the recycling BiCG algorithm. We now generalize and extend this recycling theory to BiCGSTAB. Recycling BiCG focuses on efficiently solving sequences of dual linear systems, while the focus here is on(More)
We focus on robust and efficient iterative solvers for the pressure Poisson equation in in-compressible Navier-Stokes problems. Preconditioned Krylov subspace methods are popular for these problems, with BiCGStab and GMRES(m) most frequently used for nonsymmet-ric systems. BiCGStab is popular because it has cheap iterations, but it may fail for stiff(More)
  • Navneet Pratap Singh, Kapil Ahuja, Heike Fassbender
  • 2016
Recently a new algorithm for model reduction of second order linear dynamical systems with proportional damping, the Adaptive Iterative Rational Global Arnoldi (AIRGA) algorithm [1], has been proposed. The main computational cost of the AIRGA algorithm is in solving a sequence of linear systems. These linear systems do change only slightly from one(More)
We model social storage systems as a strategic network formation game. We define the utility of each player in the network under two different frameworks, one where the cost to add and maintain links is considered in the utility function and the other where budget constraints are considered. In the context of social storage and social cloud computing, these(More)
The most popular iterative linear solvers in Computational Fluid Dynamics (CFD) calculations are restarted GMRES and BiCGStab. At the beginning of most incompressible flow calculations, the computation time and the number of iterations to converge for the pressure Poisson equation are quite high, since the initial guess is far from the solution. In this(More)