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X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20100101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20111208T170000
DTEND;TZID=UTC:20111208T170000
DTSTAMP:20260407T110132
CREATED:20170124T102152Z
LAST-MODIFIED:20170124T102152Z
UID:619-1323363600-1323363600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Decision rules for information discovery in multi-stage stochastic programming
DESCRIPTION:Title: Decision rules for information discovery in multi-stage stochastic programmingSpeaker: Phebe VayanosAffiliation: Department of Computing – Imperial College LondonLocation: CPSE Seminar room (C615 Roderic Hill)Time: 5:00pm \nAbstract. Stochastic programming and robust optimization are disciplines concerned with optimal decision-making under uncertainty over time. Traditional models and solution algorithms have been tailored to problems where the order in which the uncertainties unfold is independent of the controller actions. Nevertheless\, in numerous real-world decision problems\, the time of information discovery can be influenced by the decision maker\, and uncertainties only become observable following an (often costly) investment. Such problems can be formulated as mixed-binary multi-stage stochastic programs with decision-dependent non-anticipativity constraints. Unfortunately\, these problems are severely computationally intractable. We propose an approximation scheme for multi-stage problems with decision-dependent information discovery which is based on techniques commonly used in modern robust optimization. In particular\, we obtain a conservative approximation in the form of a mixed-binary linear program by restricting the spaces of measurable binary and real-valued decision rules to those that are representable as piecewise constant and linear functions of the uncertain parameters\, respectively. We assess our approach on a problem of infrastructure and production planning in offshore oil fields from the literature.  \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-decision-rules-for-information-discovery-in-multi-stage-stochastic-programming/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20111208T170000
DTEND;TZID=UTC:20111208T170000
DTSTAMP:20260407T110132
CREATED:20170124T102152Z
LAST-MODIFIED:20170124T102152Z
UID:620-1323363600-1323363600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: A Scenario Approach for Estimating the Suboptimality of Linear Decision Rules in Two-Stage Robust Optimization
DESCRIPTION:Title: A Scenario Approach for Estimating the Suboptimality of Linear Decision Rules in Two-Stage Robust Optimization Speaker: Michael HadjiyiannisAffiliation: Department of Computing – Imperial College LondonLocation: CPSE Seminar room (C615 Roderic Hill)Time: 5:00pm \nAbstract. Robust dynamic optimization problems involving adaptive decisions are computationally intractable in general. Tractable upper bounding approximations can be obtained by requiring the adaptive decisions to be representable as linear decision rules (LDRs). In this presentation we investigate families of tractable lower bounding approximations\, which serve to estimate the degree of suboptimality of the best LDR. These approximations are obtained either by solving a dual version of the robust optimization problem in LDRs or by utilizing an inclusion-wise discrete approximation of the problem’s uncertainty set. The quality of the resulting lower bounds depends on the distribution assigned to the uncertain parameters or the choice of the discretization points within the uncertainty set\, respectively. We prove that identifying the best possible lower bounds is generally intractable in both cases and propose an efficient procedure to construct suboptimal lower bounds. The resulting instance-wise bounds outperform known worst-case bounds in the majority of our test cases.  \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-a-scenario-approach-for-estimating-the-suboptimality-of-linear-decision-rules-in-two-stage-robust-optimization/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20111208T170000
DTEND;TZID=UTC:20111208T170000
DTSTAMP:20260407T110132
CREATED:20170124T102152Z
LAST-MODIFIED:20170124T102152Z
UID:621-1323363600-1323363600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Scenario-Free Stochastic Programming with Polynomial Decision Rules
DESCRIPTION:Title: Scenario-Free Stochastic Programming with Polynomial Decision RulesSpeaker: Dimitra BampouAffiliation: Department of Computing – Imperial College LondonLocation: CPSE Seminar room (C615 Roderic Hill)Time: 5:00pm \nAbstract. Multi-stage stochastic programming provides a versatile framework for optimal decision making under uncertainty\, but it gives rise to hard functional optimization problems since the adaptive recourse decisions must be modeled as functions of some or all uncertain parameters. We propose to approximate these recourse decisions by polynomial decision rules and show that the best polynomial decision rule of a fixed degree can be computed efficiently. We also show that the suboptimality of the best polynomial decision rule can be estimated efficiently by solving a dual version of the stochastic program in polynomial decision rules. \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-scenario-free-stochastic-programming-with-polynomial-decision-rules/
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