Frequent pattern mining has been a focused theme in data mining research and an important first step in the analysis of data arising in a broad range of applications. The traditional exact model for frequent pattern requires that every item occurs in each supporting transaction. However, real application data is usually subject to random noise or measurement error, which poses new challenges fo...

The Parallel Diagonal Dominant (PDD) algorithm is an eecient tridiagonal solver. In this paper, a detailed study of the PDD algorithm is given. First the PDD algorithm is extended to solve periodic tridiagonal systems and its scalability is studied. Then the reduced PDD algorithm, which has a smaller operation count than that of the conventional sequential algorithm for many applications, is pr...

a parallel algorithm for generating t-ary tree sequences in reverse b-order is presented. the algorithm generates t-ary trees by 0-1 sequences, and each 0-1 sequences is generated in constant average time o(1). the algorithm is executed on a crew sm simd model, and is adaptive and cost-optimal. prior to the discussion of the parallel algorithm a new sequential generation with o(1) average time ...

Global convergence to rst-order critical points is proved for two trust-region SQP-lter algorithms of the type introduced by Fletcher and Leyyer (1997). The algorithms allow for an approximate solution of the quadratic subproblem and incorporate the safeguarding tests described in Fletcher, Leyyer and Toint (1998). The rst algorithm decomposes the step into its normal and tangential components ...

This paper develops a decomposition algorithm by which a market economy with many households may be solved through the computation of equilibria for a sequence of representative agent economies. The paper examines local and global convergence properties of the sequential recalibration (SR) algorithm. SR is then demonstrated to efficiently solve AuerbachKotlikoff OLG models with a large number o...

A popular framework for the interpretation of image sequences is the layers or sprite model, see e.g. [1], [2]. Jojic and Frey [3] provide a generative probabilistic model framework for this task, but their algorithm is slow as it needs to search over discretized transformations (e.g. translations, or affines) for each layer simultaneously. Exact computation with this model scales exponentially...

This paper places the optimal tree ranking problem in N C. A ranking is a labeling of the nodes with natural numbers such that if nodes u and v have the same label then there exists another node with a greater label on the path between them. An optimal ranking is a ranking in which the largest label assigned to any node is as small as possible among all rankings. An O(n) sequential algorithm is...

We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online mirror descent, and gradients are computed by the power iteration. To analyze it, we give a new second order approximation of the squared spectral norm, which helps us to get a reg...

Spatio-temporal databases have achieved a high attention. Nevertheless, there is a little work on this kind of databases in distributed environment. This paper presents an on-line parallel algorithm to workload using MVR-tree index on the CREW RAM parallel model. The algorithm makes a distribution of the objects that more frequently change either their position or their form. Empirical results ...