在 $$M ^{\natural }$$ -凹处理速度条件下的广义可延展调度的常系数近似值

IF 2.2 2区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Mathematical Programming Pub Date : 2024-01-29 DOI:10.1007/s10107-023-02054-z
Dimitris Fotakis, Jannik Matuschke, Orestis Papadigenopoulos
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引用次数: 0

摘要

在广义延展性调度中,作业可在多台机器上同时分配和处理,以减少调度的总体时间跨度。每个作业所需的处理时间由所分配机器的联合处理速度决定。我们研究了处理速度是与作业相关的 \(M ^{\natural }\)-concave 函数的情况,并为这种情况提供了一个常系数近似值,从而大大扩展了这种近似值可能适用的函数领域。此外,我们还探讨了可变调度与向一组具有不同效用函数的代理公平分配物品问题之间的联系,并设计了一种黑箱还原方法,从而为后者获得了资源增量近似算法。
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A constant-factor approximation for generalized malleable scheduling under $$M ^{\natural }$$ -concave processing speeds

In generalized malleable scheduling, jobs can be allocated and processed simultaneously on multiple machines so as to reduce the overall makespan of the schedule. The required processing time for each job is determined by the joint processing speed of the allocated machines. We study the case that processing speeds are job-dependent \(M ^{\natural }\)-concave functions and provide a constant-factor approximation for this setting, significantly expanding the realm of functions for which such an approximation is possible. Further, we explore the connection between malleable scheduling and the problem of fairly allocating items to a set of agents with distinct utility functions, devising a black-box reduction that allows to obtain resource-augmented approximation algorithms for the latter.

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来源期刊
Mathematical Programming
Mathematical Programming 数学-计算机:软件工程
CiteScore
5.70
自引率
11.10%
发文量
160
审稿时长
4-8 weeks
期刊介绍: Mathematical Programming publishes original articles dealing with every aspect of mathematical optimization; that is, everything of direct or indirect use concerning the problem of optimizing a function of many variables, often subject to a set of constraints. This involves theoretical and computational issues as well as application studies. Included, along with the standard topics of linear, nonlinear, integer, conic, stochastic and combinatorial optimization, are techniques for formulating and applying mathematical programming models, convex, nonsmooth and variational analysis, the theory of polyhedra, variational inequalities, and control and game theory viewed from the perspective of mathematical programming.
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