There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Motivated by applications to sensor, peer-to-peer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join and old nodes leave the network. Algorithms for such networks need to be robust against changes in topology. Additionally, nodes in sensor networks operate under limited computational, communication, and energy resources. These constraints have motivated the design of "gossip" algorithms: schemes which distribute the computational burden and in which a node communicates with a randomly chosen neighbor. We analyze the averaging problem under the gossip constraint for an arbitrary network graph, and find that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly stochastic matrix characterizing the algorithm. Designing the fastest gossip algorithm corresponds to minimizing this eigenvalue, which is a semidefinite program (SDP). In general, SDPs cannot be solved in a distributed fashion; however, exploiting problem structure, we propose a distributed subgradient method that solves the optimization problem over the network. The relation of averaging time to the second largest eigenvalue naturally relates it to the mixing time of a random walk with transition probabilities derived from the gossip algorithm. We use this connection to study the performance and scaling of gossip algorithms on two popular networks: Wireless Sensor Networks, which are modeled as Geometric Random Graphs, and the Internet graph under the so-called Preferential Connectivity (PC) model.

/pdf/randomized-gossip-algorithms-2g2zm9depe.pdf

Randomized gossip algorithms

Due to their extraordinary utility and broad applicability in many areas of classical mathematics and modern physical sciences (most notably, computerized tomography), algorithms for solving convex feasibility problems continue to receive great attention. To unify, generalize, and review some of these algorithms, a very broad and flexible framework is investigated. Several crucial new concepts which allow a systematic discussion of questions on behaviour in general Hilbert spaces and on the quality of convergence are brought out. Numerous examples are given.

/pdf/on-projection-algorithms-for-solving-convex-feasibility-2wyotvnuad.pdf

On Projection Algorithms for Solving Convex Feasibility Problems

We consider a network of distributed sensors, where where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihood estimate of the parameters. This scheme doesn't involve explicit point-to-point message passing or routing; instead, it diffuses information across the network by updating each node's data with a weighted average of its neighbors' data (they maintain the same data structure). At each step, every node can compute a local weighted least-squares estimate, which converges to the global maximum-likelihood solution. This scheme is robust to unreliable communication links. We show that it works in a network with dynamically changing topology, provided that the infinitely occurring communication graphs are jointly connected.

/pdf/a-scheme-for-robust-distributed-sensor-fusion-based-on-43zuqkuij5.pdf

A scheme for robust distributed sensor fusion based on average consensus

* Preface * Univariate Interpolation * Best Approximation * Numerical Integration * Peano's Theorem and Applications * Multivariate Interpolation * Splines * Bernstein Polynomials * Properties of the q-integers * References * Index *

Interpolation and Approximation by Polynomials

On the convergence of asynchronous paracontractions with application to tomographic reconstruction from incomplete data

We establish the convergence of sequential and asynchronous iteration schemes for nonlinear paracontracting operators acting in finite dimensional spaces. Applications to the solution of linear systems of equations with convex constraints are outlined. A first generalization of one of our convergence results to an infinite pool of asymptotically paracontracting operators is also presented.

/pdf/convergence-of-sequential-and-asynchronous-nonlinear-1pxnw78qe8.pdf

Convergence of sequential and asynchronous nonlinear paracontractions

Integral Equation Method for the Continuous Spectrum Radial Schrödinger Equation

A new highly accurate numerical approximation scheme based on a Gauss type Clenshaw-Curtis quadrature for Fredholm integral equations of the second kind x(t) + ∫ab k(t, s)x(s)ds = y(t), whose kernel k(t,s) is either discontinuous or not smooth along the main diagonal, is presented. This scheme is of spectral accuracy when k(t,s) is infinitely differentiable away from the diagonal t = s. Relation to the singular value decomposition is indicated. Application to integro-differential Schrodinger equations with nonlocal potentials is given.

/pdf/nystrom-clenshaw-curtis-quadrature-for-integral-equations-158h449uga.pdf

I. Koltracht

Papers

On the convergence of asynchronous paracontractions with application to tomographic reconstruction from incomplete data

Convergence of sequential and asynchronous nonlinear paracontractions

Integral Equation Method for the Continuous Spectrum Radial Schrödinger Equation

Nyström-Clenshaw-Curtis quadrature for integral equations with discontinuous kernels

Efficient Algorithm for Toeplitz Plus Hankel Matrices