Multiobjective (Combinatorial) Optimisation—Some Thoughts on Applications

Multiobjective Programming and Goal Programming - Lecture Notes in Economics and Mathematical Systems ◽

10.1007/978-3-540-85646-7_25 ◽

2009 ◽

pp. 267-282 ◽

Cited By ~ 1

Author(s):

Matthias Ehrgott

Keyword(s):

Combinatorial Optimisation

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On modelling hard combinatorial optimisation problems as linear programs: refutations of the 'unconditional impossibility' claims

International Journal of Operational Research ◽

10.1504/ijor.2021.113505 ◽

2021 ◽

Vol 40 (2) ◽

pp. 261

Author(s):

Moustapha Diaby ◽

Mark H. Karwan ◽

Lei Sun

Keyword(s):

Combinatorial Optimisation ◽

Linear Programs

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Material Approximation of Combinatorial Optimisation

From Pattern Formation to Material Computation - Emergence, Complexity and Computation ◽

10.1007/978-3-319-16823-4_9 ◽

2015 ◽

pp. 161-180

Author(s):

Jeff Jones

Keyword(s):

Combinatorial Optimisation

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Hyper-heuristic Based Local Search for Combinatorial Optimisation Problems

AI 2018: Advances in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-030-03991-2_30 ◽

2018 ◽

pp. 312-317 ◽

Cited By ~ 1

Author(s):

Ayad Turky ◽

Nasser R. Sabar ◽

Simon Dunstall ◽

Andy Song

Keyword(s):

Local Search ◽

Combinatorial Optimisation

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Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/151 ◽

2019 ◽

Author(s):

Emir Demirovic ◽

Peter J. Stuckey ◽

James Bailey ◽

Jeffrey Chan ◽

Christopher Leckie ◽

...

Keyword(s):

Machine Learning ◽

Computational Complexity ◽

Combinatorial Optimisation ◽

Linear Functions ◽

Optimal Parameters ◽

Common Goal ◽

Learning Function ◽

Input Parameters ◽

Theoretical Insight ◽

Insight Into

We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. Our contributions are two-fold: 1) we provide theoretical insight into the properties and computational complexity of predict+optimise problems in general, and 2) develop a novel framework that, in contrast to related work, guarantees to compute the optimal parameters for a linear learning function given any ranking optimisation problem. We illustrate the applicability of our framework for the particular case of the unit-weighted knapsack predict+optimise problem and evaluate on benchmarks from the literature.

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