Approximating single- and multi-objective nonlinear sum and product knapsack problems

IF 0.9 4区 数学 Q3 MATHEMATICS, APPLIED Discrete Optimization Pub Date : 2023-05-01 DOI:10.1016/j.disopt.2023.100771
Jan Boeckmann , Clemens Thielen , Ulrich Pferschy
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Abstract

We present a fully polynomial-time approximation scheme (FPTAS) for a very general version of the well-known knapsack problem. This generalization covers, with few exceptions, all versions of knapsack problems that have been studied in the literature so far and allows for an objective function consisting of sums or products of possibly nonlinear, separable item profits, while the knapsack constraint states an upper bound on the sum of possibly nonlinear, separable item weights. Moreover, we extend our FPTAS to a multi-objective fully polynomial-time approximation scheme (MFPTAS) for the multi-objective version of the problem.

As applications of our general algorithms, we obtain the first FPTAS for the recently-introduced 0–1 time-bomb knapsack problem as well as FPTASs for a variety of robust knapsack problems. Moreover, we extend our FPTAS to the minimization version of our general problem, which, in particular, allows us to explicitly state an FPTAS for the classical minimization knapsack problem, which has been missing in the literature so far.

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单目标和多目标非线性和积背包问题的逼近
我们提出了一个完全多项式时间近似方案(FPTAS)的一个非常普遍的版本众所周知的背包问题。除了少数例外,这种推广涵盖了迄今为止文献中研究的背包问题的所有版本,并允许由可能非线性、可分离的项目利润的和或乘积组成的目标函数,而背包约束规定了可能非线性、不可分的项目权重的和的上界。此外,我们将FPTAS扩展到该问题的多目标版本的多目标全多项式时间近似方案(MFPTAS)。作为我们通用算法的应用,我们获得了最近引入的0–1定时炸弹背包问题的第一个FPTAS,以及各种鲁棒背包问题的FPTAS。此外,我们将我们的FPTAS扩展到我们的一般问题的最小化版本,这特别允许我们明确地为经典最小化背包问题声明FPTAS,这在迄今为止的文献中是缺失的。
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来源期刊
Discrete Optimization
Discrete Optimization 管理科学-应用数学
CiteScore
2.10
自引率
9.10%
发文量
30
审稿时长
>12 weeks
期刊介绍: Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization. In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments, and novel applications (in particular, large-scale and real-time applications). The journal also publishes clearly labelled surveys, reviews, short notes, and open problems. Manuscripts submitted for possible publication to Discrete Optimization should report on original research, should not have been previously published, and should not be under consideration for publication by any other journal.
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