A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value

The SPARQL declarative query language includes innovative capabilities to match subgraph patterns within a semantic graph database, providing a powerful base upon which to implement complex graph algorithms for very large data. Iterative algorithms are useful in a wide variety of domains

of Malkhi, Reiter, Wool, and Wright, and we illustrate how to program with such registers in the framework of Bertsekas, using the notation of Üresin and Dubois. Consequently, existing iterative algorithms for a significant class of problems (including solving systems of linear equations, finding shortest

A fast iterative algorithm, with computation based on the fast Fourier transform (FFT), is presented. It can be used to control a soundfield at several control points with a loudspeaker array from multiple reference signals. It designs an equalizer able to invert long FIR filters and which achieves

We present a general algorithm for synthesizing state invariants that speed up automated planners and have other applications in reasoning about change. Invariants are facts that hold in all states that are reachable from an initial state by the application of a number of operators. In contrast

approach: Can we show the existence of limit cycles for these other block-iterative algorithms? Does the sequence of successive limit cycles converge to a single vector? If so, which vector is it? 1

the advantages of bounded inference provided by anytime schemes such as Mini-Clustering (MC), and combine them with the virtues of iterative algorithms such as Iterative Belief Propagation (IBP). Our resulting hybrid algorithm Iterative Join-Graph Propagation (IJGP) is shown empirically to surpass

We consider the class of Iterative Shrinkage-Thresholding Algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. In this paper we present a Fast

FacetCube: a general framework for non-negative tensor factorization

Gaussian elimination for solving an n × n linear system of equations Ax = b is the archetypal direct method of numerical linear algebra. In this note we point out that GE has an iterative side too. We can’t resist beginning with a curious piece of history. The second most famous algorithm