Fast Heuristics for the Maximum Feasible Subsystem Problem

Chinneck, John W.

doi:10.1287/ijoc.13.3.210.12632

Cited by 70 publications

(84 citation statements)

References 19 publications

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“…Altogether, y ij = 1 − x ij . By using the above results, we can rewrite the objective function (9) by splitting the case of e ij = 0 from the case of e ij = 1. Denote with E ∈ {0, 1} n×n the following diagonal matrix.…”

Section: Correction Of the Data Recordsmentioning

confidence: 99%

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Error correction for massive datasets

Bruni¹

2005

Optimization Methods and Software

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The paper is concerned with the problem of automatic detection and correction of errors into massive data sets. As customary, erroneous data records are detected by formulating a set of rules. Such rules are here encoded into linear inequalities. This allows to check the set of rules for inconsistencies and redundancies by using a polyhedral mathematics approach. Moreover, it allows to correct erroneous data records by introducing the minimum changes through an integer linear programming approach. Results of a particularization of the proposed procedure to a real-world case of census data correction are reported.

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Section: Correction Of the Data Recordsmentioning

confidence: 99%

“…3. Inconsistencies are selected by selecting irreducible infeasible subsystems (IIS, see also [1,9,10,32]) by using a variant of Farkas' lemma (see e.g. [31]), while redundancies are detected by finding implied inequalities.…”

Section: Introductionmentioning

confidence: 99%

Error correction for massive datasets

Bruni¹

2005

Optimization Methods and Software

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“…Additionally it has been shown that it does not admit a polynomial time approximation scheme (unless P = NP) [3]). Some exact and heuristc algorithms have been proposed (see, e.g., [24], [10], [2], [25]), but none of these has been tested on problems of the dimensions with which we are concerned. For more details and additional references the reader is referred to [4], [25] or [16].…”

Section: Dealing With Infeasibility and Insufficient Memorymentioning

confidence: 99%

“…Our experience with problems of different sizes is that typically between 5 and 20 iterations are necessary to find an optimal solution, and up to 60 if the problem is infeasible. The number of iterations obviously depends on the particular choice of right-hand side in (10). In particular, the number of iterations will depend on the choice of the constant ρ in (8).…”

Section: Parallel Performancementioning

confidence: 99%

Large-scale linear programming techniques for the design of protein folding potentials

Wagner

Meller

Elber

2004

Math. Program., Ser. A

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Abstract. We present large-scale optimization techniques to model the energy function that underlies the folding process of proteins. Linear Programming is used to identify parameters in the energy function model, the objective being that the model predict the structure of known proteins correctly. Such trained functions can then be used either for ab-initio prediction or for recognition of unknown structures. In order to obtain good energy models we need to be able to solve dense Linear Programming Problems with tens (possibly hundreds) of millions of constraints in a few hundred parameters, which we achieve by tailoring and parallelizing the interior-point code PCx.

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“…Many published researches h infeasibility, [1,[10][11][12][13], however, there are few papers deal with infeasibility resolution [14][15][16]. One of the useful references is [17].…”

Section: Introductionmentioning

confidence: 99%