Dual-Bounded Generating Problems: All Minimal Integer Solutions for a Monotone System of Linear Inequalities

Automata, Languages and Programming

Gurvich

et al. 2003

Abstract. Given two finite sets of points X , Y in R n which can be separated by a nonnegative linear function, and such that the componentwise minimum of any two distinct points in X is dominated by some point in Y, we show that |X | ≤ n|Y|. As a consequence of this result, we obtain quasi-polynomial time algorithms for generating all maximal integer feasible solutions for a given monotone system of separable inequalities, for generating all p-inefficient points of a given discrete probability distribution, and for generating all maximal empty hyper-rectangles for a given set of points in R n . This provides a substantial improvement over previously known exponential algorithms for these generation problems related to Integer and Stochastic Programming, and Data Mining. Furthermore, we give an incremental polynomial time generation algorithm for monotone systems with fixed number of separable inequalities, which, for the very special case of one inequality, implies that for discrete probability distributions with independent coordinates, both p-efficient and p-inefficient points can be separately generated in incremental polynomial time.

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“…Since by (4) the family F t is uniformly dual-bounded, the results of [4], as we cited earlier, directly imply the following.…”

Section: Theorem 1 If F T Is the Family Of Maximal Feasible Solutionsupporting

confidence: 52%

Section: Dual(c a B): Given An Integer Box C A Family Of Vectors Amentioning

confidence: 99%

“…Thus, by Lemma 1 we can derive (2), which in its turn is sufficient for the efficient generation of the family F π (see [4]). …”

Section: Dual(c a B): Given An Integer Box C A Family Of Vectors Amentioning

confidence: 99%

“…Generalizing results on monotone systems of linear inequalities from [4], we will now use Lemma 1 to prove the following:…”

Section: Represents the Set Of Minimal Infeasible Vectors For (3)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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