On the efficacy of distributed simplex algorithms for linear programming

Ho, James K.; Sundarraj, R. P.

doi:10.1007/bf01299209

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…Their parallel algorithm divides the columns of the constraint matrix among many processors. Ho and Sundaraj [25] compared the problem of distributed computation of the simplex method using two different methods: (i) distributed reinversion (DINV) and (ii) distributing pricing (DPRI).…”

Section: Related Workmentioning

confidence: 99%

Peer‐to‐peer distributed text classifier learning in PADMINI

Zhu

Mahule

Dutta

et al. 2012

Statistical Analysis

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Popular Internet document repositories, such as online newspapers, digital libraries, and blogs store large amount of text and image data that are frequently accessed by large number of users. Users' input through collaborative commenting or tagging can be very useful in organizing and classifying documents. Some web sites (e.g. Google Image Labeler) support a collection of tags and labels, but a large fraction of these sites do not currently support such activities. Moreover, relying upon centrally controlled web‐service providers for such support is probably not a good idea if the objective is to make the collaborative inputs publicly available. Often, business entities offering such web‐based tagging environments end up owning and monetizing the result of the collective effort. This paper takes a step toward addressing this problem—it proposes a peer‐to‐peer (P2P) system (PADMINI), powered by distributed data mining algorithms. In particular, it focuses on learning a P2P classifier from tagged text data. This paper describes the PADMINI system and the distributed text classifier learning components; text classification is posed as a linear program and an asynchronous distributed algorithm is used to solve it. It also presents extensive empirical results on text data obtained from the Hubble Space Telescope (HST) proposal abstract database. Copyright © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2012

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Section: Related Workmentioning

confidence: 99%

Peer‐to‐peer distributed text classifier learning in PADMINI

Zhu

Mahule

Dutta

et al. 2012

Statistical Analysis

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“…The first attempt to exploit task parallelism in the revised simplex method was reported by Ho and Sundarraj (1994). In addition to the natural data parallelism of (column-wise) PRICE, Ho and Sundarraj identified that INVERT can be overlapped with simplex iterations.…”

Section: Task and Data Parallel Revised Simplexmentioning

confidence: 99%

Towards a practical parallelisation of the simplex method

Hall

2008

Comput Manag Sci

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“…Then, if SP 1 generates no proposals for SP 2 during the next cycle (implying W 1 ϭ 0), the algorithm, which has now reverted to DND, would have stopped incorrectly. Ho and Sundarraj [1994] have shown that solution times for LP problems can be reduced when reinversions are overlapped with the simplex steps (by using an extra node). Here, we propose a similar idea-namely, that of performing reinversions when a node is idle.…”

Section: Syncmentioning

confidence: 99%

Distributed nested decomposition of staircase linear programs

Ho¹,

Sundarraj

1997

ACM Trans. Math. Softw.

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This article considers the application of a primal nested-decomposition method to solve staircase linear programs (SLPs) on distributed-memory, multiple-instruction-multiple-data computers. Due to the coupling that exists among the stages of an SLP, a standard parallel-decomposition algorithm for these problems would allow only a subset of the subproblem processes to overlap with one another at any give time. We propose algorithms that seek to increase the amount of overlap among the processes as well as utilize idle time beneficially. Computational results testing the effectiveness of our algorithms are reported, using a standard set of test problems.

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On the efficacy of distributed simplex algorithms for linear programming

Cited by 11 publications

References 14 publications

Peer‐to‐peer distributed text classifier learning in PADMINI

Peer‐to‐peer distributed text classifier learning in PADMINI

Towards a practical parallelisation of the simplex method

Distributed nested decomposition of staircase linear programs

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