Computational Optimisation Group

Seminar: Robust Optimization of Dynamic Systems and Its Applications.
2012-01-06 15:00 UTC
Title: Robust Optimization of Dynamic Systems and Its Applications.Speaker: Dr. Boris Houska Affiliation: Location: CPSE Seminar room (C616 Roderic Hill)Time: 3:00pm
Abstract. This talk gives an introduction to numerical methods for the robust optimization of uncertain dynamic systems. In contrast to standard differential equations, which describe the propagation of a single vector-valued trajectory in time, uncertain dynamic systems propagate a set valued function: the uncertainty tube. For the case that control inputs are available this uncertainty tube can be influenced and optimized such that certain robustness criteria are met. The aim of the first part of the talk is to explain how to formulate and solve such tube-based robust optimal control problems.The second part of the talk is about the software environment and algorithm collection ACADO Toolkit, which implements tools for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including robust optimal control and model predictive control. The ACADO Toolkit is implemented as a selfcontained C++ code, while the object-oriented design allows for convenient coupling of existing optimization packages and for extending it with user-written optimization routines. We present numerical examples from the field of mechanics and biochemical engineering.
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Seminar: A Scenario Approach for Estimating the Suboptimality of Linear Decision Rules in Two-Stage Robust Optimization
2011-12-08 17:00
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
Abstract. 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.
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Seminar: Scenario-Free Stochastic Programming with Polynomial Decision Rules
2011-12-08 17:00
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
Abstract. 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.
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Seminar: Decision rules for information discovery in multi-stage stochastic programming
2011-12-08 17:00
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
Abstract. 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.
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Seminar: How IBM enables industries to better leverage optimisation technology?
2011-12-01 14:00
Title: How IBM enables industries to better leverage optimisation technology?Speaker: Dr. Mozafar Hajian Affiliation: Location: 219A William Penney Lab (entrance from walkway) Time: 2:00pm
Abstract. IBM ILOG CPLEX Optimisation Studio is used by leading academic & business institutions to rapidly create both mathematical programming and constraint programming models. It is used to build efficient optimisation models and state-of-the-art applications for the full range of planning and scheduling problems. The tool provides a powerful Integrated Development Environment (IDE) using the Optimisation Programming Language (OPL) language, programmatic APIs, or indeed other 3rd party modelling interfaces. This makes it easy to evaluate different modelling approaches and to integrate external data. With its built-in development tools, it supports the entire model development process.In addition, IBM ILOG ODM Enterprise provides an enterprise scale platform for developing and deploying highly effective optimisation based analytical planning and scheduling solutions for business decision makers across a variety of industries. In this presentation, you will find out, how IBM is providing a powerful platform to enable, a) better scenario management, b) faster decision making process, c) distributed planning processes for large scale applications, and d) a role based planning and collaboration environment.
About the speaker. Mozafar Hajian completed his Ph.D. at Brunel University of West London in Combinatorial Optimisation, before joining IC-Parc at Imperial College as a Senior Research Fellow. During this period, he collaborated with both Computing and Manufacturing departments to pioneer new methods in solving combinatorial optimisation problems and distributed Supply Chain Optimisation. Outside of academia, he has held senior roles at ILOG, SAP and IBM, including senior business development manager for Supply Chain Optimisation at SAP. He is presently a certified Client Technical Advisor for IBM ILOG Optimisation & Supply Chain solutions.

Seminar: Coordinating Flexible Responsive Electricity Demand via Smart Grids
2011-11-25 14:00
Title: Coordinating Flexible Responsive Electricity Demand via Smart Grids Speaker: Dr. Daniel Livengood Affiliation: Location: CPSE Seminar room (C615 Roderic Hill)Time: 2:00pm
Abstract. As the electric grid evolves into a ‘smart’ grid, there is renewed interest in developing and implementing strategies for integrating flexible, responsive electric demand into the perpetual task of balancing electricity supply and demand on the grid. Pilot programs and research have demonstrated that automated energy management systems, particularly for residential customers, are instrumental in increasing the response of flexible demand from customers participating in these strategies. This talk will begin with a discussion of the potential benefits to a single residence when coordinating electricity consumption, distributed generation and storage with an automated energy management system under time-varying pricing and uncertain weather conditions. A discussion of the open questions involving what may happen when large numbers of automated energy management systems are installed on the smart grid will follow.
About the speaker. Daniel is a postdoctoral research fellow at the Massachusetts Institute of Technology, where he recently completed his Ph.D. in Engineering Systems. Daniel’s research focuses on automated home energy management systems as part of future smart grids, with a particular interest in developing algorithms that coordinate energy consumption, distributed generation and storage in response to time-varying pricing and uncertain weather forecasts. Along with his ongoing academic research, Daniel is working on energy and thermal algorithms with the startup Coincident, Inc., as part of their energy management appliance. Daniel is also an advisor to the startup Grid Solutions, Inc., and has worked as an intern at the demand response company EnerNOC, analyzing baseline calculation methods for demand response programs.

Seminar: Large Scale Numerical Shape Optimisation
2011-11-24 16:30
Title: Large Scale Numerical Shape Optimisation Speaker: Dr. Stephan Schmidt Affiliation: Location: CPSE Seminar room (C615 Roderic Hill)Time: 4:30pm
Abstract. Shape Optimisation problems are a special sub-class of optimisation problems with PDE constraints, where the domain in which the PDE is defined becomes the unknown to be found. The Hadamard Theorem states that given sufficient regularity, one can always find a representation of the shape gradient that exists on the surface of the unknown domain alone, thereby offering the potential to create very efficient numerical schemes. Unless the structure of a shape optimisation problem is exploited in some fashion, the resulting need to include the deformation of the surrounding domain can potentially become prohibitively expensive. The desire to create higher order methods like SQP also necessitates studies about second order shape derivatives. The talk will also focus on applications in aerodynamic design and the shaping of wave emitters.
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Seminar: LAROS: a convex optimization approach for feature extraction
2011-11-17 15:00
Title: LAROS: a convex optimization approach for feature extractionSpeaker: Dr. Xuan Vinh Doan Affiliation: Warwick Business School – University of WarwickLocation: Room 572A Huxley BuildingTime: 3:00pm
Abstract. We propose a convex optimization formulation using nuclear norm and $ell_1$-norm to find a large approximately rank-one submatrix (LAROS) for a given matrix. We are able to characterize the low-rank and sparsity structure of the resulting solutions. We show that our model can recover low-rank submatrices for matrices with subgaussian random noises. We solve the proposed model using a proximal point algorithm and show that it can be applied to applications in feature extraction. This is joint work with Stephen Vavasis and Kim-Chuan Toh.
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Seminar: Optimization under Uncertainty - Recent Advances in Multi-Parametric Mixed Integer Linear Programming and its Applications
2011-11-10 16:00
Title: Optimization under Uncertainty – Recent Advances in Multi-Parametric Mixed Integer Linear Programming and its Applications Speaker: Martina Wittmann-Hohlbein Affiliation: CPSE – Department of Chemical Engineering – Imperial College LondonLocation: 219A William Penney Lab (entrance from walkway)Time: 4:00pm
Abstract. In optimization under uncertainty, multi-parametric programming is a powerful tool to account for the presence of uncertainty in mathematical models. Its objective is to derive the optimal solution as a function of the parameters without exhaustively enumerating the parameter space. We consider the general multi-parametric mixed integer linear programming (mp-MILP) problem. The presence of uncertainty in mixed integer linear programming models employed in widespread application fields, including planning/scheduling, process synthesis and hybrid control, significantly increases the complexity and computational effort in retrieving optimal solutions. A particular difficulty arises when uncertainty is simultaneously present in the coefficients of the objective function, the constraint matrix and the constraint vector, which transforms the original linear model into a nonlinear and non-convex one with respect to both the optimization variables and the parameters. Here, we discuss two novel methods for the solution of the general mp-MILP problem. The first approach deals with the global solution of the general mp-MILP model. Exploiting the special structure of the problem, we outline the steps of a parametric branch and bound procedure. The second approach is a two-stage method for the approximate solution of the general mp-MILP problem which combines state-of-the-art robust optimization and multi-parametric programming techniques. The two-stage method is applied to short-term batch process scheduling under uncertainty and we demonstrate its potential in the construction of a pro-active scheduling policy.
About the speaker. Martina Wittmann-Hohlbein obtained a Diploma in Mathematics and Economics at Martin-Luther-University Halle-Wittenberg, Germany, in 2007. She joined CPSE at Imperial College in 2009.
