Numerical methods for computing Casimir interactions

We review several different approaches for computing Casimir forces and related fluctuation-induced interactions between bodies of arbitrary shapes and materials. The relationships between this problem and well known computational techniques from classical electromagnetism are emphasized. We also review the basic principles of standard computational methods, categorizing them according to three criteria---choice of problem, basis, and solution technique---that can be used to classify proposals for the Casimir problem as well. In this way, mature classical methods can be exploited to model Casimir physics, with a few important modifications.Comment: 46 pages, 142 references, 5 figures. To appear in upcoming Lecture Notes in Physics book on Casimir Physic

Multi-Processor Architectures for Solving Sparse Linear Systems. Application to the Load Flow Problem

IFAC SYMPOSIUM ON COMPONENTS,INSTRUMENTS AND TECHNIQUES FOR LOW COST AUTOMATION AND APPLICATIONS.1986 (.1986.VALENCIA)Three heuristic algorithms for solving a cluster problem associated with the tearing of a symmetric matrix are presented. Based on these partitioning procedures, a method for the parallel solution of the fast decoupled load flow has been developed, although it is also suitable for the parallel solution of any linear equations system. Experimental results of applying such algorithms on several test systems have been obtained using a multiprocessor architecture

New Sequential and Scalable Parallel Algorithms for Incomplete Factor Preconditioning

The solution of large, sparse, linear systems of equations Ax = b is an important kernel, and the dominant term with regard to execution time, in many applications in scientific computing. The large size of the systems of equations being solved currently (millions of unknowns and equations) requires iterative solvers on parallel computers. Preconditioning, which is the process of translating a linear system into a related system that is easier to solve, is widely used to reduce solution time and is sometimes required to ensure convergence. Level-based preconditioning (ILU(ℓ)) has long been used in serial contexts and is widely recognized as robust and effective for a wide range of problems. However, the method has long been regarded as an inherently sequential technique. Parallelism, it has been thought, can be achieved primarily at the expense of increased iterations. We dispute these claims. The first half of this dissertation takes an in-depth look at structurally based ILU(ℓ) symbolic factorization. There are two definitions of fill level, “sum” and “max,” that have been proposed. Hitherto, these definitions have been cast in terms of matrix terminology. We develop a sequence of lemmas and theorems that provide graph theoretic characterizations of both definitions; these characterizations are based on the static graph of a matrix, G(A). Our Incomplete Fill Path Theorem characterizes fill levels per the sum definition; this is the definition that is used in most library implementations of the “classic” ILU(ℓ) factorization algorithm. Our theorem leads to several new graph-search algorithms that compute factors identical, or nearly identical, to those computed by the “classic” algorithm. Our analyses shows that the new algorithms have lower run time complexity than that of the previously existing algorithms for certain classes of matrices that are commonly encountered in scientific applications. The second half of this dissertation presents a Parallel ILU algorithmic framework (PILU). This framework enables scalable parallel ILU preconditioning by combining concepts from domain decomposition and graph ordering. The framework can accommodate ILU(ℓ) factorization as well as threshold-based ILUT methods. A model implementation of the framework, the Euclid library, was developed as part of this dissertation. This library was used to obtain experimental results for Poisson\u27s equation, the Convection-Diffusion equation, and a nonlinear Radiative Transfer problem. The experiments, which were conducted on a variety of platforms with up to 400 CPUs, demonstrate that our approach is highly scalable for arbitrary ILU(ℓ) fill levels

Building Information Filtering Networks with Topological Constraints: Algorithms and Applications

We propose a new methodology for learning the structure of sparse networks from data; in doing so we adopt a dual perspective where we consider networks both as weighted graphs and as simplicial complexes. The proposed learning methodology belongs to the family of preferential attachment algorithms, where a network is extended by iteratively adding new vertices. In the conventional preferential attachment algorithm a new vertex is added to the network by adding a single edge to another existing vertex; in our approach a new vertex is added to a set of vertices by adding one or more new simplices to the simplicial complex. We propose the use of a score function to quantify the strength of the association between the new vertex and the attachment points. The methodology performs a greedy optimisation of the total score by selecting, at each step, the new vertex and the attachment points that maximise the gain in the score. Sparsity is enforced by restricting the space of the feasible configurations through the imposition of topological constraints on the candidate networks; the constraint is fulfilled by allowing only topological operations that are invariant with respect to the required property. For instance, if the topological constraint requires the constructed network to be be planar, then only planarity-invariant operations are allowed; if the constraint is that the network must be a clique forest, then only simplicial vertices can be added. At each step of the algorithm, the vertex to be added and the attachment points are those that provide the maximum increase in score while maintaining the topological constraints. As a concrete but general realisation we propose the clique forest as a possible topological structure for the representation of sparse networks, and we allow to specify further constraints such as the allowed range of clique sizes and the saturation of the attachment points. In this thesis we originally introduce the Maximally Filtered Clique Forest (MFCF) algorithm: the MFCF builds a clique forest by repeated application of a suitably invariant operation that we call Clique Expansion operator and adds vertices according to a strategy that greedily maximises the gain in a local score function. The gains produced by the Clique Expansion operator can be validated in a number of ways, including statistical testing, cross-validation or value thresholding. The algorithm does not prescribe a specific form for the gain function, but allows the use of any number of gain functions as long as they are consistent with the Clique Expansion operator. We describe several examples of gain functions suited to different problems. As a specific practical realisation we study the extraction of planar networks with the Triangulated Maximally Filtered Graph (TMFG). The TMFG, in its simplest form, is a specialised version of the MFCF, but it can be made more powerful by allowing the use of specialised planarity invariant operators that are not based on the Clique Expansion operator. We provide applications to two well known applied problems: the Maximum Weight Planar Subgraph Problem (MWPSP) and the Covariance Selection problem. With regards to the Covariance Selection problem we compare our results to the state of the art solution (the Graphical Lasso) and we highlight the benefits of our methodology. Finally, we study the geometry of clique trees as simplicial complexes and note how the statistics based on cliques and separators provides information equivalent to the one that can be achieved by means of homological methods, such as the analysis of Betti numbers, however with our approach being computationally more efficient and intuitively simpler. Finally, we use the geometric tools developed to provide a possible methodology for inferring the size of a dataset generated by a factor model. As an example we show that our tools provide a solution for inferring the size of a dataset generated by a factor model

Numerical Methods for Computing Casimir Interactions

We review several different approaches for computing Casimir forces and related fluctuation-induced interactions between bodies of arbitrary shapes and materials. The relationships between this problem and well known computational techniques from classical electromagnetism are emphasized. We also review the basic principles of standard computational methods, categorizing them according to three criteria\-choice of problem, basis, and solution technique\-that can be used to classify proposals for the Casimir problem as well. In this way, mature classical methods can be exploited to model Casimir physics, with a few important modifications.Keywords: Imaginary Frequency, Perfectly Match Layer, Casimir Force, Casimir Energy, Perfect Electric ConductorUnited States. Army Research Office (Contract W911NF-07-D-0004)Massachusetts Institute of Technology. Ferry FundUnited States. Defense Advanced Research Projects Agency (Contract N66001-09-1-2070-DOD

Numerical Computing with Functions on the Sphere and Disk

A new low rank approximation method for computing with functions in polar and spherical geometries is developed. By synthesizing a classic procedure known as the double Fourier sphere (DFS) method with a structure-preserving variant of Gaussian elimination, approximants to functions on the sphere and disk can be constructed that (1) preserve the bi-periodicity of the sphere, (2) are smooth over the poles of the sphere (and origin of the disk), (3) allow for the use of FFT-based algorithms, and (4) are near-optimal in their underlying discretizations. This method is used to develop a suite of fast, scalable algorithms that exploit the low rank form of approximants to reduce many operations to essentially 1D procedures. This includes algorithms for differentiation, integration, and vector calculus. Combining these ideas with Fourier and ultraspherical spectral methods results in an optimal complexity solver for Poisson\u27s equation, which can be used to solve problems with 108 degrees of freedom in just under a minute on a laptop computer. All of these algorithms have been implemented and are publicly available in the open-source computing system called Chebfun [21]

High performance selected inversion methods for sparse matrices: direct and stochastic approaches to selected inversion

The explicit evaluation of selected entries of the inverse of a given sparse matrix is an important process in various application fields and is gaining visibility in recent years. While a standard inversion process would require the computation of the whole inverse who is, in general, a dense matrix, state-of-the-art solvers perform a selected inversion process instead. Such approach allows to extract specific entries of the inverse, e.g., the diagonal, avoiding the standard inversion steps, reducing therefore time and memory requirements. Despite the complexity reduction already achieved, the natural direction for the development of the selected inversion software is the parallelization and distribution of the computation, exploiting multinode implementations of the algorithms. In this work we introduce parallel, high performance selected inversion algorithms suitable for both the computation and estimation of the diagonal of the inverse of large, sparse matrices. The first approach is built on top of a sparse factorization method and a distributed computation of the Schur-complement, and is specifically designed for the parallel treatment of large, dense matrices including a sparse block. The second is based on the stochastic estimation of the matrix diagonal using a stencil-based, matrix-free Krylov subspace iteration. We implement the two solvers and prove their excellent performance on Cray supercomputers, focusing on both the multinode scalability and the numerical accuracy. Finally, we include the solvers into two distinct frameworks designed for the solution of selected inversion problems in real-life applications. First, we present a parallel, scalable framework for the log-likelihood maximization in genomic prediction problems including marker by environment effects. Then, we apply the matrix-free estimator to the treatment of large-scale three-dimensional nanoelectronic device simulations with open boundary conditions