Alexander Rand
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Featured researches published by Alexander Rand.
Advances in Computational Mathematics | 2012
Andrew Gillette; Alexander Rand; Chandrajit L. Bajaj
We prove the optimal convergence estimate for first-order interpolants used in finite element methods based on three major approaches for generalizing barycentric interpolation functions to convex planar polygonal domains. The Wachspress approach explicitly constructs rational functions, the Sibson approach uses Voronoi diagrams on the vertices of the polygon to define the functions, and the Harmonic approach defines the functions as the solution of a PDE. We show that given certain conditions on the geometry of the polygon, each of these constructions can obtain the optimal convergence estimate. In particular, we show that the well-known maximum interior angle condition required for interpolants over triangles is still required for Wachspress functions but not for Sibson functions.
SIAM Journal on Scientific Computing | 2011
Chandrajit L. Bajaj; Shun-Chuan Albert Chen; Alexander Rand
In order to compute polarization energy of biomolecules, we describe a boundary element approach to solving the linearized Poisson-Boltzmann equation. Our approach combines several important features including the derivative boundary formulation of the problem and a smooth approximation of the molecular surface based on the algebraic spline molecular surface. State of the art software for numerical linear algebra and the kernel independent fast multipole method is used for both simplicity and efficiency of our implementation. We perform a variety of computational experiments, testing our method on a number of actual proteins involved in molecular docking and demonstrating the effectiveness of our solver for computing molecular polarization energy.
Computational methods in applied mathematics | 2016
Andrew Gillette; Alexander Rand; Chandrajit L. Bajaj
Abstract We combine theoretical results from polytope domain meshing, generalized barycentric coordinates, and finite element exterior calculus to construct scalar- and vector-valued basis functions for conforming finite element methods on generic convex polytope meshes in dimensions 2 and 3. Our construction recovers well-known bases for the lowest order Nédélec, Raviart–Thomas, and Brezzi–Douglas–Marini elements on simplicial meshes and generalizes the notion of Whitney forms to non-simplicial convex polygons and polyhedra. We show that our basis functions lie in the correct function space with regards to global continuity and that they reproduce the requisite polynomial differential forms described by finite element exterior calculus. We present a method to count the number of basis functions required to ensure these two key properties.
solid and physical modeling | 2010
Chandrajit L. Bajaj; Radhakrishna Bettadapura; Na Lei; Alex Mollere; Chao Peng; Alexander Rand
Whereas traditional finite element methods use meshes to define domain geometry, weighted extended B-spline finite element methods rely on a weight function. A weight function is a smooth, strictly positive function which vanishes at the domain boundary at an appropriate rate. We describe a method for generating weight functions for a general class of domains based on A-splines. We demonstrate this approach and address the relationship between weight function quality and error in the resulting finite element solutions.
international conference on bioinformatics | 2015
Deukhyun Cha; Qin Zhang; Jesmin Jahan Tithi; Alexander Rand; Rezaul Alam Chowdhury; Chandrajit L. Bajaj
Motivation. Despite several reported acceleration successes of programmable GPUs (Graphics Processing Units) for molecular modeling and simulation tools, the general focus has been on fast computation with small molecules. This was primarily due to the limited memory size on the GPU. Moreover simultaneous use of CPU and GPU cores for a single kernel execution -- a necessity for achieving high parallelism -- has also not been fully considered. Results. We present fast computation methods for molecular mechanical (Lennard-Jones and Coulombic) and generalized Born solvation energetics which run on commodity multicore CPUs and manycore GPUs. The key idea is to trade off accuracy of pairwise, long-range atomistic energetics for higher speed of execution. A simple yet efficient CUDA kernel for GPU acceleration is presented which ensures high arithmetic intensity and memory efficiency. Our CUDA kernel uses a cache-friendly, recursive and linear-space octree data structure to handle very large molecular structures with up to several million atoms. Based on this CUDA kernel, we present a hybrid method which simultaneously exploits both CPU and GPU cores to provide the best performance based on selected parameters of the approximation scheme. Our CUDA kernels achieve more than two orders of magnitude speedup over serial computation for many of the molecular energetics terms. The hybrid method is shown to be able to achieve the best performance for all values of the approximation parameter. Availability. The source code and binaries are freely available as PMEOPA (Parallel Molecular Energetic using Octree Pairwise Approximation) and downloadable from http://cvcweb.ices.utexas.edu/software.
Mathematics of Computation | 2014
Alexander Rand; Andrew Gillette; Chandrajit L. Bajaj
Advances in Computational Mathematics | 2013
Alexander Rand; Andrew Gillette; Chandrajit L. Bajaj
Archive | 2011
Alexander Rand; Andrew Gillette; Chandrajit L. Bajaj
arXiv: Computational Geometry | 2011
Alexander Rand
Archive | 2016
Andrew Gillette; Alexander Rand