PCx user guide

Czyzyk, Joseph; Mehrotra, Sanjay; Wright, Stephen J.

doi:10.2172/475586

Cited by 65 publications

(49 citation statements)

References 7 publications

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“…Remark 4.1 Our extensive experience (by testing Netlib problems with various combinations of the pre-solves and comparing results composed of the first five columns of Table 1 in the next section and the corresponding columns of Table 1 in [17]) shows that the set of our pre-process methods uses less time and reduces the problem size more efficiently than the set of pre-process methods discussed and implemented in [17].…”

Section: A Singleton Variable Defined By Two Rowsmentioning

confidence: 98%

“…However, many commercial software packages do not provide sufficient details, for example, [17,18]. We will use the methods proposed in [5,7].…”

Section: Initial Point Selectionmentioning

confidence: 99%

“…The first part f obj at the beginning is zero and is updated all the time as we reduce the problem (remove some c k from c); the terms in the summation in the second part are continuously reduced and c k are updated as necessary when we reduce the problem. The first 6 pre-process methods presented below were reported in various literatures, such as [1,17,25,26,27]; the rest of them, to the best of our knowledge, are not reported anywhere. …”

Section: Pre-processmentioning

confidence: 99%

“…Although many more algorithms have been proposed since then (see, for example, [10,11,12,13,14]), there is no significant improvement in the best polynomial bound for interior-point algorithms, and there is no report of a better algorithm than MPC for general linear programming problems 1 . In fact, the most popular interior-point method software packages implemented MPC, for example, LOQO [16], PCx [17] and LIPSOL [18].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming

Yang¹

2016

Numer Algor

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Mehrotra's algorithm has been the most successful infeasible interior-point algorithm for linear programming since 1990. Most popular interior-point software packages for linear programming are based on Mehrotra's algorithm. This paper proposes an alternative algorithm, arc-search infeasible interior-point algorithm. We will demonstrate, by testing Netlib problems and comparing the test results obtained by arc-search infeasible interior-point algorithm and Mehrotra's algorithm, that the proposed arc-search infeasible interior-point algorithm is a more efficient algorithm than Mehrotra's algorithm.

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Section: A Singleton Variable Defined By Two Rowsmentioning

confidence: 98%

“…However, many commercial software packages do not provide sufficient details, for example, [17,18]. We will use the methods proposed in [5,7].…”

Section: Initial Point Selectionmentioning

confidence: 99%

Section: Pre-processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming

Yang¹

2016

Numer Algor

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show abstract

“…These are standard techniques that can be found in optimization and machine learning literature. For example, there are excellent linear programming solvers based on simplex method, as implemented in Clp, Glpk, and lp solve (Berkelaar, 2004), and based on interior point method as implemented in the Bpmd (Mészáros, 1996), the Hopdm and the PCx packages (Czyzyk et al, 2004). We neglect the details of these techniques and point readers to the excellent treatises of (Papadimitriou and Steiglitz, 1998;Vanderbei, 1996).…”

Section: Optimization Techniquesmentioning

confidence: 99%

Knowledge-Based Energy Functions for Computational Studies of Proteins

Li¹,

Liang²

Biological and Medical Physics Biomedical Engineering

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A Java/CORBA virtual machine architecture for remote execution of optimization solvers in heterogeneous networks

González-Castaño

Anido‐Rifón

Pousada-Carballo

et al. 2001

Softw: Pract. Exper.

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Virtual machines for remote execution are a useful tool for utilizing light user interfaces and intensive application cores in different physical machines connected through the Internet. In a virtual machine, application cores are distributed in a network. Specific locations, operating systems and hardware characteristics are hidden by virtual machines. They make it possible to use a PC to execute user interfaces and (a few) high‐performance computers for application cores. We present a Java/CORBA‐based brokerage platform that allows remote execution of optimization solvers from a client running on any platform. The system offers a dynamic library of available problem solvers, and a graphic interface to browse several defined properties and metadata on available solvers. In addition, an embedded file compression module to reduce data transfer time is included as a plug‐in feature of the proposed virtual machine. Analogous systems could be constructed for applications in which interaction traffic time is much lower than execution time. Copyright © 2001 John Wiley & Sons, Ltd.

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PCx user guide

Cited by 65 publications

References 7 publications

CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming

CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming

Knowledge-Based Energy Functions for Computational Studies of Proteins

A Java/CORBA virtual machine architecture for remote execution of optimization solvers in heterogeneous networks

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