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DTSTART:20171029T010000
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DTSTART;TZID=Europe/Paris:20180417T140000
DTEND;TZID=Europe/Paris:20180417T153000
DTSTAMP:20260430T010338
CREATED:20180410T110415Z
LAST-MODIFIED:20180410T110415Z
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SUMMARY:Seminar: Large neighbourhood Benders' search
DESCRIPTION:Title: Large neighbourhood Benders’ search\nSpeaker: Dr. Stephen Maher\nAffiliation: Lancaster University Management School\nLocation: Room 218 Huxley Building\nTime: 14:00 – 15:30 \nAbstract. Enhancements for the Benders’ decomposition algorithm can be derived from large neighbourhood search (LNS) heuristics. While mixed-integer programming (MIP) solvers are endowed with an array of LNS heuristics\, their use is typically limited in bespoke Benders’ decomposition implementations. To date\, only ad hoc approaches have been developed to enhance the Benders’ decomposition algorithm using large neighbourhood search techniques—namely local branching and proximity search. A general implementation of Benders’ decomposition has been developed within the MIP solver SCIP to permit a greater use of LNS heuristics with the expectation that it will enhance the solution algorithm. Benders’ decomposition is employed for all LNS heuristics to improve the quality of the identified solutions and generate additional cuts that can be used to improve the convergence of the main solution algorithm. Focusing on the heuristics of proximity search\, RINS and DINS\, the results will demonstrate the value of using Benders’ decomposition within LNS.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-large-neighbourhood-benders-search/
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DTSTART;TZID=Europe/Paris:20180425T140000
DTEND;TZID=Europe/Paris:20180425T150000
DTSTAMP:20260430T010338
CREATED:20180319T142205Z
LAST-MODIFIED:20180319T142644Z
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SUMMARY:Seminar: Computing Pessimistic Leader-Follower Equilibria with Multiple Followers
DESCRIPTION:Title: Computing Pessimistic Leader-Follower Equilibria with Multiple Followers\nSpeaker: Dr Stefano Coniglio\nAffiliation: Dept. of Mathematical Sciences\, Southampton University\nLocation: Room 217 Huxley Building\nTime: 14:00 – 15:00 \nAbstract. We investigate the problem of computing a Leader-Follower equilibrium in Stackelberg games where two or more followers react to the strategy chosen by the (single) leader by playing a Nash Equilibrium. We consider two natural cases\, the optimistic one where the followers select a Nash Equilibrium maximizing the leader’s utility and the pessimistic one where they select an equilibrium minimizing the leader’s utility. We first illustrate that\, in both cases\, computing a Leader-Follower Nash equilibrium is NP-hard and not in Poly-APX unless P=NP\, and that even deciding whether one of the leader’s actions would be played with strictly positive probability is NP-hard. We then focus on the pessimistic case with followers restricted to pure strategies\, showing that this problem too is NP-hard and not in Poly-APX unless P=NP. After casting it as a pessimistic bilevel programming problem\, we propose an exact implicit enumeration algorithm for its solution. In particular\, our algorithm is capable of computing the maximum of the problem and\, for the general case where the former only admits a supremum\, an alpha-approximate strategy for any nonnegative alpha. Experimental results are presented and illustrated\, showing the viability of the proposed approach. \nThis is joint work with Nicola Gatti and Alberto Marchesi. \nAbout the speaker. Dr Stefano Coniglio is a Lecturer in Operational Research within Mathematical Sciences at the University of Southampton. Dr Stefano Coniglio joined the University of Southampton as Lecturer in Operational Research within Mathematical Sciences in February 2016. Prior to that\, he served as Research Associate at the RWTH Aachen University\, Germany\, and as Postdoctoral Researcher at Politecnico di Milano\, Italy\, where he received his Ph.D. in Information Technology in 2011. His research focuses on exact methods for the solution of mathematical programming and combinatorial optimization problems. Recently\, he has been most active in bilevel programming\, robust optimization\, and methodological aspects of cutting plane generation\, with applications to algorithmic game theory\, optimization in telecommunication networks\, energy production planning\, and data mining.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-computing-pessimistic-leader-follower-equilibria-with-multiple-followers/
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