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Approximability and Hardness in Multi-objective Optimization

Identifieur interne : 001017 ( Istex/Corpus ); précédent : 001016; suivant : 001018

Approximability and Hardness in Multi-objective Optimization

Auteurs : Christian Gla Er ; Christian Reitwie Ner ; Heinz Schmitz ; Maximilian Witek

Source :

RBID : ISTEX:1182BEB3D362E19DD2BF882C51817000039621FC
Url:
DOI: 10.1007/978-3-642-13962-8_20

Links to Exploration step

ISTEX:1182BEB3D362E19DD2BF882C51817000039621FC

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<note type="content">We define and compare several solution notions that capture reasonable algorithmic tasks for computing optimal solutions.</note>
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<note type="content">We define approximative solution notions and investigate in which cases polynomial-time solvability translates from one to another notion. Moreover, for problems where all objectives have to be minimized, approximability results translate from single-objective to multi-objective optimization such that the relative error degrades only by a constant factor. Such translations are not possible for problems where all objectives have to be maximized (unless P = NP).</note>
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<Para>We study the approximability and the hardness of combinatorial multi-objective NP optimization problems (multi-objective problems, for short). Our contributions are:</Para>
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<Para>We define approximative solution notions and investigate in which cases polynomial-time solvability translates from one to another notion. Moreover, for problems where all objectives have to be minimized, approximability results translate from single-objective to multi-objective optimization such that the relative error degrades only by a constant factor. Such translations are not possible for problems where all objectives have to be maximized (unless P = NP).</Para>
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<Para>As a consequence we see that in contrast to single-objective problems (where the solution notions coincide), the situation is more subtle for multiple objectives. So it is important to exactly specify the NP-hardness notion when discussing the complexity of multi-objective problems.</Para>
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<title>Approximability and Hardness in Multi-objective Optimization</title>
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<note type="content">We define and compare several solution notions that capture reasonable algorithmic tasks for computing optimal solutions.</note>
<note type="content">These solution notions induce corresponding NP-hardness notions for which we prove implication and separation results.</note>
<note type="content">We define approximative solution notions and investigate in which cases polynomial-time solvability translates from one to another notion. Moreover, for problems where all objectives have to be minimized, approximability results translate from single-objective to multi-objective optimization such that the relative error degrades only by a constant factor. Such translations are not possible for problems where all objectives have to be maximized (unless P = NP).</note>
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