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The average quality of greedy-algorithms for the Subset-Sum-Maximization Problem

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Abstract

This paper deals with the quality of approximative solutions for the Subset-Sum-Maximization-Problem maximize

$$\sum\limits_{i = l}^n {a_i x_i } $$

subject to

$$\sum\limits_{i = l}^n {a_i x_i } \leqslant b$$

wherea l,...,an,bεR+ andx l,...xnε{0,1}. produced by certain heuristics of a Greedy-type. Every heuristic under consideration realizes a feasible solution (x 1, ..., xn) whose objective value is less or equal the optimal value, which is of course not greater thanb. We use the gap between capacityb and realized value as an upper bound for the error made by the heuristic and as a criterion for quality.

Under the stochastic model:a 1, ..., an, b independent,a 1...,an uniformly distributed on [0, 1], b uniformly distributed on [0,n] we derive the gap-distributions and the expected size of the gaps.

The analyzed algorithms include four algorithms which can be done in linear time and four heuristics which require sorting, which means that they are done inO(nlnn) time.

Zusammenfassung

Diese Arbeit untersucht die Güte von Näherungslösungen für das Subset-Sum-Maximierungsproblem maximiere

$$\sum\limits_{i = l}^n {a_i x_i } $$

unter

$$\sum\limits_{i = l}^n {a_i x_i } \leqslant b$$

wobeia l,...,an,bεR+ andx l,...xnε{0,1}. welche von bestimmten Greedy-Heuristiken erzeugt werden. Jede dieser Heuristiken realisiert eine zuverlässige Lösung (x1, ..., xn), deren Zielfunktionswert den Optimalwert nicht übertrifft und letzterer ist sicher nicht größer alsb. Die Lücke zwischen der Kapazitätb und dem realisierten Wert stellt also eine obere Schranke für den Fehler und somit ein Qualitätskriterium dar.

Unter dem stochastischen Modell: a1, ..., an,b unabhängig, a1, ..., an gleichverteilt auf [0, 1],b gleichverteilt auf [0,n], ermitteln wir die Verteilungen der Lücke und ihre Erwartungswerte. Unter den analysierten Algorithmen befinden sich vier, die in linearer Zeit ausgeführt werden können. Weitere vier erfordern eine Vorsortierung, so daß sie O(nlnn) an Zeit benötigen.

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Borgwardt, K.H., Tremel, B. The average quality of greedy-algorithms for the Subset-Sum-Maximization Problem. ZOR - Methods and Models of Operations Research 35, 113–149 (1991). https://doi.org/10.1007/BF02331572

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