Computational Optimisation Group

Seminar: Integrating Mixed Integer Optimisation and Logic with Satisfiability Modulo Theories
2016-05-03 17:00 UTC
Title: Integrating Mixed Integer Optimisation and Logic with Satisfiability Modulo TheoriesSpeaker: Miten MistryAffiliation: Department of Computing – Imperial College LondonLocation: Room 418 Huxley BuildingTime: 5:00pm
Abstract. Mixed integer optimisation problems, especially those involving design or organisation, often have an inherent logical structure. Existing frameworks to model and utilise such structure reformulate the problem into a mixed integer model or make use of specialised constraints. Using the application of two-dimensional bin packing, we explore Satisfiability Modulo Theories (SMT) as a means to exploit logical structure. The logical connectives and reasoning provided by SMT allows us to derive cuts to strengthen a Mixed Integer Linear Programming (MILP) solver and, by using unsatisfiability proofs, identify new ways of traversing the search tree.
About the speaker. Miten Mistry is a PhD student in the Department of Computing (QUADS group) at Imperial College London, under the supervision of Dr Ruth Misener. He received EPSRC HiPEDs CDT funding. He previously obtained a MEng Mathematics and Computer Science degree from Imperial College London.

Seminar: On the Convergence of Galerkin Type Multilevel Optimization Methods
2016-05-17 16:00 UTC
Title: On the Convergence of Galerkin Type Multilevel Optimization MethodsSpeaker: Chin Pang Ho (Clint)Affiliation: Department of Computing – Imperial College LondonLocation: Huxley BuildingTime: 4:00pm
Abstract.
About the speaker. Chin Pang Ho (Clint) is a PhD student in the Department of Computing (QUADS group) at Imperial College, under the supervision of Dr Panos Parpas. He received a BS in Applied Mathematics from the University of California, Los Angeles and an MSc in Mathematical Modeling and Scientific Computing from the University of Oxford.

Seminar: A Parametric Approach to Solving the Pooling Problem
2016-05-17 17:00 UTC
Title: A Parametric Approach to Solving the Pooling ProblemSpeaker: Radu Baltean LugojanAffiliation: Department of Computing – Imperial College LondonLocation: Huxley BuildingTime: 5:00pm
Abstract. We develop an algorithm solving specialised pooling problem instances and generating cutting planes for more generic instances. The approach parameterises the optimisation problem with respect to the pool concentration variables and uncovers embedded sparsity and polyhedral/topological properties for a variety of instances. The presentation generalises and extends recent work analysing computational complexity of the pooling problem [Boland et al. 2015, Haugland 2016]. Our analysis also integrates source-to-output streams and both upper and lower bounds on the network parameters.
About the speaker. Radu Baltean-Lugojan is a PhD student in the Department of Computing (QUADS group) at Imperial College London, under the supervision of Dr. Ruth Misener and Dr. Panos Parpas. He received EPSRC funding, and previously obtained the MEng Computing degree from Imperial College London.

Seminar: Asymptotic Error Bounds for Control Constrained Singularly Perturbed Linear Quadratic Optimal Control Problems
2016-05-31 16:00 UTC
Title: Asymptotic Error Bounds for Control Constrained Singularly Perturbed Linear Quadratic Optimal Control ProblemsSpeaker: Sei HoweAffiliation: Department of Computing – Imperial College LondonLocation: Huxley BuildingTime: 4:00pm
Abstract.
About the speaker. Sei Howe is a PhD student in the QUADS group at Imperial College. She received her B.A in pure mathematics from Reed College, USA in 2011 and her M.Sc. in pure mathematics from Imperial College in 2012. Her supervisor is Dr. Panos Parpas and her research focuses on stochastic optimization of multi-scale processes.

Seminar: Symmetry Groups and Topological Structure of Optimisation Problems
2016-05-31 17:00 UTC
Title: Symmetry Groups and Topological Structure of Optimisation ProblemsSpeaker: Georgia KouyialisAffiliation: Department of Computing – Imperial College LondonLocation: Huxley BuildingTime: 5:00pm
Abstract.
About the speaker. Georgia Kouyialis is a PhD student in the Department of Computing (QUADS group), at Imperial College, under the supervision of Dr. Ruth Misener. She obtained the MSci (Hons) degree in Mathematics from University College London (UCL). She received the EPSRC DTA funding and her research evolves around Mixed Integer Nonlinear Programming.

Seminar: Scheduling Algorithms for Energy Efficiency in Computing Systems
2016-07-01 15:00 UTC
Title: Scheduling Algorithms for Energy Efficiency in Computing SystemsSpeaker: Dimitrios LetsiosAffiliation: Department of Computing – Imperial College LondonLocation: Huxley BuildingTime: 3:00pm
Abstract. Energy consumption of computing devices has become an important issue nowadays. A major tool for efficient energy management in the system level is dynamic speed (frequency) scaling combined with job scheduling. In this context, the processing time of a job is not fixed, but it depends on the speed at which it is processed while the energy is a convex function of the speed. The main goal is the design efficient algorithms which compute good trade-off solutions with respect to performance and energy. Our focus will be algorithmic techniques with provably good performances for fundamental problems of the area.
About the speaker. Dimitrios Letsios has very recently joined Imperial College as a postdoctoral researcher. Previously, he has served as a postdoctoral researcher with teaching duties at the University of Nice – Sophia Antipolis (2015-2016), at the Technical University of Munich (2014-2015) and at University Pierre and Marie Curie (2013-2014). Before, he obtained his PhD degree at the University of Evry in Paris (2010-2013) and his MSc and BSc degrees at Athens University of Economics and Business (2004-2010). His research interests lie in theoretical computer science and, more specifically, the design of algorithms with proven performance guarantees (approximation algorithms). He has mainly worked on scheduling problems taking into account the energy consumption and communication costs of computing systems.

Seminar: Mixed-integer convex optimization
2016-09-16 15:00 UTC
Title: Mixed-integer convex optimizationSpeaker: Miles LubinAffiliation: Massachusetts Institute of TechnologyLocation: Room 217 Huxley BuildingTime: 3:00pm
Abstract. Mixed-integer convex optimization problems are convex problems with the additional (non-convex) constraints that some variables may take only integer values. Despite the past decades’ advances in algorithms and technology for both mixed-integer *linear* and *continuous, convex* optimization, mixed-integer convex optimization problems have remained relatively more challenging and less widely used in practice. In this talk, we describe our recent algorithmic work on mixed-integer convex optimization which has yielded advances over the state of the art, including the globally optimal solution of open benchmark problems. Based on our developments, we have released Pajarito, an open-source solver written in Julia and accessible from popular optimization modeling frameworks. Pajarito is immediately useful for solving challenging mixed combinatorial continuous problems arising from engineering and statistical applications.
About the speaker. Miles Lubin is a Ph.D. candidate in Operations Research at the Massachusetts Institute of Technology advised by Juan Pablo Vielma. His research interests span diverse areas of mathematical optimization, with a unifying theme of developing new methodologies for large-scale optimization drawing from motivating applications in renewable energy. He has published work in chance constrained optimization, mixed-integer conic optimization, robust optimization, stochastic programming, algebraic modeling, automatic differentiation, numerical linear algebra, and parallel computing techniques for large-scale problems. He is an author of the JuMP modeling package and co-founder of the JuliaOpt organization for optimization software written in Julia.

Seminar: Pooling Problems: Advances in Theory and Applications
2016-10-03 15:00 UTC
Title: Pooling Problems: Advances in Theory and ApplicationsSpeaker: Fabian RigterinkAffiliation: School of Mathematical and Physical Sciences – The University of Newcastle AustraliaLocation: Room 554 Huxley BuildingTime: 3:00pm
Abstract. The pooling problem is a nonconvex nonlinear programming problem with important applications. The nonlinearities of the problem arise from bilinear constraints that capture the blending of raw materials. In this talk, we summarise our recent contributions to the problem, which fall into the following categories: Formulations: we propose new multi-commodity flow formulations based on output, input and output and (input, output) commodities, and evaluate their performance computationally. Complexity: we show that the pooling problem with one pool and a bounded number of inputs can be solved in polynomial time. Bounding the gap between the McCormick relaxation and the convex hull: we show that the so-called McCormick relaxation can be arbitrarily worse than the convex hull. Convex hulls of bilinear functions: Padberg introduced new classes of inequalities that can significantly strengthen the McCormick relaxation. We study classes of bilinear functions where some of the Padberg inequalities characterise the convex hull, and evaluate computationally which of the inequalities are strongest. We conclude the talk by studying an application of particular interest to Novocastrians: optimising coal blending operations at the port of Newcastle; the world’s largest coal export port. This is joint work with my PhD supervisors, Dr Thomas Kalinowski, Prof Natashia Boland, and Prof Martin Savelsbergh.
About the speaker. Fabian Rigterink is a PhD candidate at the University of Newcastle, Australia. He is supervised by Dr Thomas Kalinowski, Prof Natashia Boland, and Prof Martin Savelsbergh. Prior to commencing his PhD, Fabian received his BSc and MSc in Industrial Engineering and Management from Karlsruhe Institute of Technology, Germany.

Seminar: Optimisation with occasionally accurate data
2016-10-25 14:00 - 17:00 UTC
Title: Optimisation with occasionally accurate data
Speaker: Coralia Cartis
Affiliation: Mathematical Institute – Oxford and Balliol College
Location: Huxley building
Time: 2:00pm (1 hour)
Abstract. We present global rates of convergence for a general class of methods for nonconvex smooth optimization that include linesearch, trust-region and regularisation strategies, but that allow inaccurate problem information. Namely, we assume the local (first- or second-order) models of our function are only sufficiently accurate with a certain probability, and they can be arbitrarily poor otherwise. This framework subsumes certain stochastic gradient analyses and derivative-free techniques based on random sampling of function values. It can also be viewed as a robustness assessment of deterministic methods and their resilience to inaccurate derivative computation such as due to processor failure in a distribute framework. We show that in terms of the order of the accuracy, the evaluation complexity of such methods is the same as their counterparts that use deterministic accurate models; the use of probabilistic models only increases the complexity by a constant, which depends on the probability of the models being good. Time permitting, we also discuss the case of inaccurate, probabilistic function value information, that arises in stochastic optimization. This work is joint with Katya Scheinberg (Lehigh University, USA).
About the speaker. Coralia Cartis (BSc Mathematics, Babesh-Bolyai University, Romania; PhD Mathematics, University of Cambridge (2005)) has joined the Mathematical Institute at Oxford and Balliol College in 2013 as Associate Professor in Numerical Optimization. Previously, she worked as a research scientist at Rutherford Appleton Laboratory and as a postdoctoral researcher at Oxford University. Between 2007-2013, she was a (permanent) lecturer and senior lecturer in the School of Mathematics, University of Edinburgh. Her research interests address the development, analysis and implementation of algorithms for linear and nonlinear non-convex optimization problems, suitable for large-scale problems. A particular focus of her recent research has been the complexity analysis/global rates of convergence of nonconvex optimization algorithms. Some of her methods have been included in GALAHAD optimization software library. She has also worked on applications of optimization in compressed sensing, signal processing and for parameter estimation in climate modelling.

Seminar: On the Passage From Local to Global in Optimization - New Challenges in Theory and Practice
2016-11-03 16:30 UTC
Title: On the Passage From Local to Global in Optimization – New Challenges in Theory and Practice
Speaker: Prof. Panos M. Pardalos
Affiliation: Departments of Industrial and Systems and Biomedical Engineering – University of Florida
Location: Room 145 Huxley Building
Time: 4:30pm (1 hour)
Abstract. Large scale problems in the design of networks, energy systems, medicine and drug design, in finance, and engineering are modeled as optimization problems. Humans and nature are constantly optimizing to minimize costs or maximize profits, to maximize the flow in a network, or to minimize the probability of a blackout in the smart grid. Due to new algorithmic developments and the computational power of machines, optimization algorithms have been used to solve problems in a wide spectrum of applications in science and engineering. In this talk we are going to address new challenges in the theory and practice of optimization. First, we have to reflect back a few decades and see what has been achieved and then address the new research challenges and new directions.
About the speaker. Panos Pardalos is a Distinguished Professor in the Departments of Industrial and Systems and Biomedical Engineering at the University of Florida, and a world renowned leader in Global Optimization, Mathematical Modeling, and Data Sciences. He is currently a Fellow of AAAS, AIMBE, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Dr. Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies. This medal is the preeminent European award given to Operations Research (OR) professionals for “scientific contributions that stand the test of time.” Prof. Pardalos is also an elected Member of the New York Academy of Sciences, the Lithuanian Academy of Sciences, the Royal Academy of Spain, and the National Academy of Sciences of Ukraine. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization. He has published over 500 papers, edited/authored over 200 books and organized over 80 conferences. He has about 34,000 citations on his work, an H-index of 81, an I10-index of 472 (Google Scholar) and has graduated 60 PhD students so far.

Seminar: On Robust Selection Problems
2016-11-07 14:30 UTC
Title: On Robust Selection Problems
Speaker: Marc Goerigk
Affiliation: Department of Management Science – Lancaster University
Location: Room LT1 Business School
Time: 2:30pm (1 hour)
Abstract. Robust optimisation considers problems that are affected by uncertain data: How can we find a solution that performs well, even if things don’t go quite as planned? Typically, adding robustness to a problem makes it harder to solve. The selection problem is maybe the simplest non-trivial combinatorial optimisation problem. Given a set of n items, the task is to choose p items that maximise some profit. Being that simple, it is an interesting object of study for complexity in robust optimisation, as its robust counterparts sometimes turn out to become NP-hard, sometimes not. In this talk I present some of the complexity results in this area, which can be surprising. Along the way, we develop an overview on different approaches to robust optimisation, and see what they mean for the selection problem.
About the speaker. Marc Goerigk is a Lecturer in the Department of Management Science at Lancaster University. He studied mathematics and computer science at the University of Gottingen, where he also completed his PhD in applied mathematics in 2012. From 2012 to 2015, he worked as a Post-Doc at the University of Kaiserslautern. Besides robust optimisation, his research interests include disaster management and public transportation problems.

Seminar: Solution of an old problem in vector optimisation - Support function of the generalised Jacobian
2016-12-05 15:00 UTC
Title: Solution of an old problem in vector optimisation – Support function of the generalised Jacobian
Speaker: Prof. Abbas Edalat
Affiliation: Department of Computing – Imperial College
Location: Room 217 Huxley Building
Time: 3:00pm (1 hour)
Abstract. Many optimisation problems are non-smooth in the sense that the objective function is not differentiable. This is invariably the case in many areas of application when the objective function contains simple constructs such as maximum or minimum of several differentiable functions or contains, for example, the absolute value function. The notion of the Clarke sub-gradient of locally Lipschitz maps has been used successfully in the past decades to solve non-smooth non-convex optimisation problems by methods such as the sub-gradient descent or the more powerful bundle method. The generalised Jacobian of a locally Lipschitz vector function, as introduced by Clarke in mid 1970’s, plays the same role in non-smooth vector (multi-objective) optimisation that the (Clarke) sub-gradient or sub-differential plays in the usual non-smooth scalar optimisation. The sub-gradient of a non-smooth objective function is a non-empty, compact and convex subset of the finite Euclidean space of the same dimension as the domain of the objective function. Similarly, the generalised Jacobian is a non-empty, compact and convex subset of the space of real mxn matrices where n is the dimension of input and m is the dimension of the output of the multi-objective function. However, until now, there has been a huge discrepancy in the way they have been defined. The sub-gradient of a scalar function at a point was originally defined constructively by its support function which provides a simple expression for its boundary. In contrast, the generalised Jacobian has been defined non-constructively by using a theorem of Rademacher in analysis which states that every Lipschitz map between finite dimensional Euclidean spaces is differentiable for almost all its arguments. The non-constructive nature of this definition has created a major obstacle in the application of the generalised Jacobian in vector optimisation and several other fields of computation such as non-smooth Newton method or solution of parametric ODE’s with non-smooth right hand side. There have been two attempts to tackle the problem of finding the support function of the generalised Jacobian. Hiriart-Urruty derived the support function of an approximation to the generalised Jacobian, namely its so-called plenary hull. Imbert used a Green-Stokes formula to obtain an intractable analytic expression for the support function of the generalized Jacobian that involves a limsup operation over surface integrals of the divergence of the derived function on a shrinking sequence of hypercubes. In this talk, I will derive a simple expression to compute the support function of the generalised Jacobian after 40 years. It determines the boundary of the non-empty compact convex set that the generalised Jacobian represents in the space of mxn matrices. The derivation is elementary, does not invoke Rademacher’s theorem and is in the same spirit as that of the sub-gradient of a scalar objective function. Moreover, I will show that the same technique can be used to compute the generalised Jacobian of locally Lipschitz multi-objective functions defined on an infinite dimensional Banach space, which allows optimisation over function spaces.
About the speaker. Abbas Edalat has been a professor of computer science and mathematics at the Department of Computing, Imperial College London, since 1997.

Seminar: On Bit Representations of Mixed-Integer Quadratic Programs.
2016-12-06 15:00 UTC
Title: On Bit Representations of Mixed-Integer Quadratic Programs.
Speaker: Prof. Adam Letchford
Affiliation: Management School – Lancaster University
Location: Room 217 Huxley Building
Time: 3:00pm (1 hour)
Abstract. A standard trick in integer programming is to replace each bounded general-integer variable with a small number of binary variables, using the bit representation of the given variable. (See, e.g., Owen & Mehrotra, 2002; Coppersmith & Lee, 2005; Muldoon et al., 2013; Bonami & Margot, 2015). Recently, bit representation was found to be useful for convexifying quadratic problems (Billionnet et al., 2012) and for linearising bilinear problems (Gupte et al., 2013). We show that,in the case of mixed-integer quadratic programs, bit representation has an additional benefit: it can enable one to obtain stronger linear programming relaxations.
About the speaker. Adam N. Letchford is known internationally for his research on exact solution methods for NP-hard optimisation problems. He has been the recipient of an IBM Faculty Award and an EPSRC Advanced Research Fellowship, and is a Fellow of the Operational Research Society. He has been on the editorial boards of six journals, including Mathematical Programming and Operations Research. From 2008-2014, he was the coordinator of the optimisation cluster of the LANCS Initiative. Since 2012, he has been the director of NATCOR, the UK National Taught Course Centre in Operational Research.

Seminar: Smart Grids and Optimization - A Winning Combination
2016-12-13 13:30 UTC
Title: Smart Grids and Optimization – A Winning Combination
Speaker: Prof. Miguel Anjos
Affiliation: Polytechnique Montreal
Location: Room 217 Huxley Building
Time: 1:30pm (1 hour)
Abstract. A smart grid is the combination of a traditional electrical power system with information and energy both flowing back and forth between suppliers and consumers. This new paradigm introduces major challenges such as the integration of intermittent generation and storage, and the need for electricity consumers to play an active role in the operations of the system. We will summarize the opportunities provided by smart grid to the optimization community, and illustrate one such opportunity through some recent research on optimal aggregation of energy resources (joint work with F. Gilbert, P. Marcotte, and G. Savard).
About the speaker. Miguel Anjos is a Professor at Polytechnique Montreal. He holds a Canada Research Chair and an Inria International Chair. He is also a licensed professional engineer in Ontario, Canada. His research is concerned with using mathematical optimization to provide guaranteed optimal or near-optimal solutions for important classes of large-scale discrete nonlinear optimization problems arising in engineering applications.

Seminar: Stochastic reformulations of linear systems and efficient randomized algorithms
2017-01-20 15:00 UTC
Title: Stochastic reformulations of linear systems and efficient randomized algorithms
Speaker: Dr. Peter Richtarik
Affiliation: School of Mathematics, University of Edinburgh
Location: Room 217 Huxley Building
Time: 3:00pm
Abstract. We propose a new paradigm for solving linear systems. In our paradigm, the system is reformulated into a stochastic problem, and then solved with a randomized algorithm. Our reformulation can be equivalently seen as a stochastic optimization problem, stochastically preconditioned linear system, stochastic fixed point problem and as a probabilistic intersection problem. We propose and analyze basic, parallel and accelerated stochastic algorithms for solving the reformulated problem, with linear convergence rates.
About the speaker. Peter Richtarik is a Reader in the School of Mathematics at the University of Edinburgh, and is the Head of a Big Data Optimization Lab. He received his PhD from Cornell University in 2007, and currently holds an EPSRC Early Career Fellowship in Mathematical Sciences. His main research focus is the development of new optimization algorithms and theory. In particular, much of his recent work is in the emerging field of big data optimization, with applications in machine learning in general and empirical risk minimization in particular. For big data optimization problems, traditional methods are no longer suitable, and hence there is need to develop new algorithmic paradigms. An important role in this respect is played by randomized algorithms of various flavors, including randomized coordinate descent, stochastic gradient descent, randomized subspace descent and randomized quasi-Newton methods. Parallel and distributed variants are of particular importance.

Seminar: Prediction of Stock market crashes,entry exits from bubbles, hedge fund disasters and their prevention
2017-09-25 14:00 - 15:00 UTC
Title: Prediction of Stock market crashes,entry exits from bubbles, hedge fund disasters and their prevention
Speaker: Prof. William T. Ziemba
Affiliation: Sauder School of Business, University of British Columbia
Location: LG19b Seminar Room, Business School
Time: 2:00pm
Abstract. Bubbles occur in financial markets from time to time. By a bubble we mean that the prices are going up just because people expect them to continue rising. In these cases the prices exceed the fair value based on fundamentals. Jarrow and his colleagues have developed tests for the existence of such bubbles. While that is interesting our main focus is on can we predict when the bubble like market-not necessarily a strict bubble will crash. For this we use the BSEYD and other models. The BSEYD model suggests a crash of 10%+ is coming in the next year or so from the signal date when the long bond interest rate exceeds the earnings yield of stocks by a critical amount. The idea is that bonds and stocks compete for the investment dollar and when interest rates relative to earnings are too high a crash is likely. The talk will discuss the history of this signal in US, Japanese and other markets since I discovered it in Japan based on the US 1987 stock market crash in 1988. Besides a crash signal the BSEYD model is useful for long term asset allocation. I will discuss three other large crash models one based on behavorial finance relating to overconfidence measured by the put and call options market. Famed investor Warren Buffett has a measure based on the value of the economy to the value of the stock market. While the way buffett presents it the measure value of the economy to the stock market does not predict. The measure can be fixed to actually predict. The fourth measure is the value of Sotheby’s stock. This then provides signals to exit markets but does not tell you when to exit. The BSEYD signal did give correct calls in two instances where the world’s most famous bubble trader George Soros shorted too soon and lost a lot of money namely the nikkei stock average in 1988 (BSEYD signal in April 1989) and the Nasdaq 100 in 1998-9(BSEYD signal in April 1999). The issue of when to exit we analyze using an approach developed with two Russian colleagues. The idea is that there is a rising trend then a peak then a decline. That model is based on research of Shiraev modified by Zhitlukhin for our purpose here. We apply it to apple computer stock in 2012, to the Nasdaq in circa 1990, to the Nikkei stock average in 1989, to the bigger bubble golf course membership market in the same circa 1989-90 period, to the 1987 and 1929 stock market crashes in the united states. In general the exits are very good. This model is also useful for shorts as well as longs with somewhat different interpretation as a trading tool. The talk also discusses famed market guru Marty Zweigs prediction methods based on fed movements and momentum and small partially anticipated decides based on economics and political events such as the Brexit, Trump and French elections. These are useful in futures trading.
About the speaker. Dr William T. Ziemba is the Alumni Professor (Emeritus) of Financial Modeling and Stochastic Optimization in the Sauder School of Business, University of British Columbia where he taught from 1968-2006. His PhD is from the University of California, Berkeley. He currently teaches part time and makes short research visits at various universities. Recently he is the Distinguished Visiting Research Associate, Systemic Risk Centre, London School of Economics. He has been a visiting professor at Cambridge, Oxford, London School of Economics, University of Reading and Warwick in the UK, at Stanford, UCLA, Berkeley, MIT, University of Washington and Chicago in the US, Universities of Bergamo, Venice and Luiss in Italy, the Universities of Zurich, Cyprus, Tsukuba (Japan), KAIST (Korea) and the National University and the National Technological University of Singapore.
He has been a consultant to a number of leading financial institutions including the Frank Russell Company, Morgan Stanley, Buchanan Partners, RAB Hedge Funds, Gordon Capital, Matcap, Ketchum Trading and, in the gambling area, to the BC Lotto Corporation, SCA Insurance, Singapore Pools, Canadian Sports Pool, Keeneland Racetrack and some racetrack syndicates in Hong Kong, Manila and Australia. His research is in asset-liability management, portfolio theory and practice, security market imperfections, Japanese and Asian financial markets, hedge fund strategies, risk management, sports and lottery investments and applied stochastic programming. His co-written practitioner paper on the Russell-Yasuda model won second prize in the 1993 Edelman Practice of Management Science Competition. He has been a futures and equity trader and hedge fund and investment manager since 1983.
He has published widely in journals such as Operations Research, Management Science,, Mathematics of OR, Mathematical Programming, American Economic Review, Journal of Economic Perspectives, Journal of Finance, Journal of Economic Dynamics and Control, JFQA, Quantitative Finance, Journal of Portfolio Management and Journal of Banking and Finance and in many books and special journal issues.
Recent books include Applications of Stochastic Programming with S.W. Wallace, SIAM-MPS, 2005, Stochastic Optimization Models in Finance, 2nd edition with R.G. Vickson, World Scientific, 2006 and Handbook of Asset and Liability Modeling, Volume 1: Theory and Methodology (2006) and Volume 2: Applications and Case Studies (2007) with S. A. Zenios, North Holland, Scenarios for Risk Management and Global Investment Strategies with Rachel Ziemba, Wiley, 2007, Handbook of Investments: Sports and Lottery Betting Markets, with Donald Hausch, North Holland, 2008, Optimizing the Aging, Retirement and Pensions Dilemma with Marida Bertocchi and Sandra Schwartz and The Kelly Capital Growth Investment Criterion, 2010, with legendary hedge fund trader Edward Thorp and Leonard MacLean, Calendar Anomalies and Arbitrage, The Handbook of Financial Decision Making (with Leonard MacLean) and Stochastic Programming (with Horand Gassman), published by World Scientific in 2012 and 2013. In progress in 2014 are Handbooks on the Economics of Wine (with O. Ashenfelter, O. Gergaud and K. Storchmann) and Futures (with T. Mallaris)
He is the series editor for North Holland’s Handbooks in Finance, World Scientific Handbooks in Financial Economics and Books in Finance, and previously was the CORS editor of INFOR and the department of finance editor of Management Science, 1982-1992. He has continued his columns in Wilmott and his 2013 book with Rachel Ziemba have the 2007-2013 columns updated with new material published by World Scientific. Ziemba, along with Hausch, wrote the famous Beat the Racetrack book (1984 (which was revised into Dr Z’s Beat the Racetrack (1987), which presented their place and show betting system and the Efficiency of Racetrack Betting Markets (1994, 2008) – the so-called bible of racetrack syndicates. Their 1986 book Betting at the Racetrack extends this efficient/inefficient market approach to simple exotic bets. Ziemba is revising BATR into Exotic Betting at the Racetrack (World Scientific) which adds Pick3,4,5,6, etc and provides updates to be out in the spring 2014.

Seminar: Robust Model Predictive Control
2017-10-27 16:00 - 17:00 UTC
Title: Robust Model Predictive Control
Speaker: Dr. Saša V. Raković
Location: Room 218 Huxley Building
Time: 4:00pm
Abstract.
Model predictive control (MPC) is an advanced control technique that employs an open–loop online optimization in order to take account of system dynamics, constraints and control objectives and to obtain the best current control action. Robust MPC (RMPC) is an improved MPC form that is robust against the bounded uncertainty. RMPC employs a generalized prediction framework that allows for a meaningful optimization of, and over, the set of possible system behaviours effected by the uncertainty. A real intricacy in RMPC arises due to the facts that the exact RMPC provides strong structural properties but it is computationally unwieldy, while the conventional MPC is not necessarily robust even though it is computationally convenient.
The seminar focuses on novel RMPC methods, developed through my research investigations and collaborations, that address effectively the fundamental challenge of reaching a meaningful compromise between the quality of guaranteed structural properties and the associated computational complexity. In particular, the talk discusses flexible and efficiently optimizable parameterizations as well as tube MPC, which lead to synthesis methods that are theoretically sound (i.e., they guarantee a–priori strong structural properties) and computationally efficient (i.e., they have a manageable computational complexity that is close enough to that of the conventional MPC synthesis).
About the speaker.
Dr. Saša V. Raković received the Ph.D. degree in Control Theory from Imperial College London. His Ph.D. thesis, entitled “Robust Control of Constrained Discrete Time Systems: Characterization and Implementation”, was awarded the Eryl Cadwaladr Davies Prize as the best Ph.D. thesis in the Electrical and Electronic Engineering Department of Imperial College London in 2005. (This award is presented annually to the Ph.D. student who produces the best thesis during the academic year.)
Dr. Saša V. Raković has been affiliated with a number of the leading international universities, including Imperial College London, ETH Zürich, Oxford University, UMD at College Park, UT at Austin and Texas A&M University at College Station.
Dr. Saša V. Raković’s research spans the broad areas of autonomy, controls, dynamics, systems, applied mathematics, optimization and set–valued analysis. His current research activity is focused on problems encountered in, or closely related to, the fields of smart autonomous and cyber-physical systems, and that belong to the intersection of controls, dynamics, systems and optimization. Dr. Saša V. Raković has authored 95 publications, most of which are highly cited (i.e., more than 3600 citations according to Google Scholar) and which are published in leading international journals and proceedings of key conferences in the related fields.
Dr. Saša V. Raković is best known for his research in model predictive control that has made significant contributions to theory, computation and implementation of conventional, robust and stochastic model predictive control. Dr. Saša V. Raković is one of the global leaders in robust model predictive control, and one of the key pioneers of the tube model predictive control framework. Tube model predictive control has been recognized as a milestone contribution to, and a major paradigm shift in, model predictive control under uncertainty.
Dr. Saša V. Raković’s most important work in analysis of dynamics and control synthesis via optimization and set–valued methods has dealt with previously long–standing problems. Inter alia, Zvi Artstein and Saša V. Raković have resolved important problems concerned with minimality of invariant sets and set invariance under output feedback. Dr. Saša V. Raković has also significantly expanded classical results on the linear quadratic Lyapunov equations by developing theory and computations for Minkowski–Lyapunov equations, namely Lyapunov equations within the class of generic vector semi–norms.

Seminar: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems
2017-11-14 16:00 - 17:00 UTC
Title: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems
Speaker: Prof. Stratos Pistikopoulos, FREng
Affiliation: Artie McFerrin Department of Chemical Engineering, Texas A&M University
Location: Room 217 Huxley Building
Time: 4:00pm
Abstract. Optimization problems involving multiple decision makers at different decision levels are referred to as multi-level programming problems. We are considering bi-level (two decision levels) and tri-level (three decision levels) programming problems. Multi-level programming problems are very challenging to solve even when considering just two linear decision levels. For classes of problems where the lower level problems also involve discrete variables, this complexity is further increased, typically requiring global optimization methods for its solution. Solution approaches for mixed integer bi-level problems with discrete variables in both levels mainly include reformulation approaches, branch and bound techniques or genetic algorithms, all of which result in approximate solutions.
In this work, we present novel algorithms for the exact, global and parametric solution of two classes of multi-level programming problems, namely (i) bi-level mixed-integer linear or quadratic programming problems (B-MILP or B-MIQP) and (ii) tri-level mixed-integer linear or quadratic programming problems (T-MILP or T-MIQP) containing both integer and continuous variables at all optimization levels. Based on multi-parametric theory and our earlier results for bi-level programing problems [5, 6], the main idea is to recast the lower levels of the multi-level programming problem as multi-parametric programming problems, in which the optimization variables of all the upper level problems, both continuous and integer, are considered as parameters for the lower level problems.
This novel algorithm can be then used for the exact and global solution of adjustable robust optimization problems. Classical robust optimization (RO) is an approach for incorporating uncertainty in optimization problems, and traditionally assumes that all decisions must be made before the realization of uncertainty (referred to as “here-and-now” decisions), a strategy which may be overly conservative. A more realistic approach is adjustable robust optimization (ARO) which involves recourse decisions (i.e. reactive actions after the realization of the uncertainty, “wait-and-see”) as functions of the uncertainty, typically posed in a two-stage stochastic setting. We propose a novel method for the derivation of generalized affine decision rules for linear/quadratic/nonlinear and mixed-integer ARO problems through multi-parametric programming. The problem is treated as a multi-level programming problem that can be then solved using the presented algorithm. A set of illustrative numerical examples are provided to demonstrate the potential of the proposed novel approach.
About the speaker. Professor Pistikopoulos is TEES Distinguished Research Professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University. He was a Professor of Chemical Engineering at Imperial College London, UK (1991-2015) and the Director of its Centre for Process Systems Engineering (2002-2009). At Texas A&M, he is the Interim Co-Director & Deputy Director of the Texas A&M Energy Institute, the Course Director of the Master of Science in Energy, the Director of the Gulf Coast Regional Manufacturing Centre, and the Texas A&M Principal Investigator of the RAPID Institute on process intensification, co-leading the Modeling & Simulation Focus Area.
He holds a Ph.D. degree from Carnegie Mellon University and he worked with Shell Chemicals in Amsterdam before joining Imperial. He has authored or co-authored over 400 major research publications in the areas of modelling, control and optimization of process, energy and systems engineering applications, 12 books and 2 patents. He is a Fellow of IChemE and AIChE, and the Editor-in-Chief of Computers & Chemical Engineering. He is the current Chair of the Computing and Systems Technology (CAST) Division of AIChE and he serves as a trustee of the Computer Aids for Chemical Engineering (CACHE) Organization. In 2007, Prof. Pistikopoulos was a co-recipient of the prestigious MacRobert Award from the Royal Academy of Engineering. In 2012, he was the recipient of the Computing in Chemical Engineering Award of CAST/AIChE. He received the title of Doctor Honoris Causa in 2014 from the University Politehnica of Bucharest, and from the University of Pannonia in 2015. In 2013, he was elected Fellow of the Royal Academy of Engineering in the UK.

Seminar: Stochastic Vehicle Routing: an Overview and some Recent Advances
2017-11-21 14:00 - 15:00 UTC
Title: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems
Speaker: Prof. Michel Gendreau
Affiliation: Dept. of Mathematics and Industrial Engineering, Polytechnique Montréal
Location: LG19a, Business School
Time: 2:00pm
Abstract. While Vehicle Routing Problems have now been studied extensively for more than 50 years, those in which some parameters are uncertain at the time where the routes are made have received significantly less attention, in spite of the fact that there are many real-life settings where key parameters are not known with certainty.
In the first part of this talk, we will examine the main classes of Stochastic Vehicle Routing Problems: problems with stochastic demands, stochastic customers, and stochastic service or travel times. We will emphasize the main approaches for modeling and tackling uncertainty: a priori models, a posteriori approaches, and chance-constrained models. The second part of the talk will devoted to a presentation of some of our recent work in the area.
About the speaker. Michel Gendreau is Department Chair and Professor of Operations Research in the Department of Mathematics and Industrial Engineering of Polytechnique Montréal (Canada). He received both his M.Sc. and his Ph.D. degrees from University of Montreal. His main research area is the application of operations research methods to a wide range of problem areas: transportation and logistics systems planning and operation, energy production and storage, healthcare, and telecommunications. Dr. Gendreau has published more than 300 papers in peer-reviewed journals and conference proceedings. He is also the co-editor of six books dealing with transportation planning and scheduling, as well as with metaheuristics.
Dr. Gendreau was the Director of the Centre for Research on Transportation (formerly CRT and now CIRRELT) from 1999 to 2007. He completed his 6-year term as Editor in chief of Transportation Science at the end of 2014. In 2001, he received the Merit Award of the Canadian Operational Research Society in recognition of his contributions to the development of O.R. in Canada. He was elected Fellow of INFORMS in 2010. In 2015, Dr. Gendreau received the prestigious Robert Herman Lifetime Achievement Award of the Transportation Science & Logistics Society of INFORMS.

Seminar: ADMM and Random Walks on Graphs
2018-01-19 11:15 - 12:30 UTC
Title: ADMM and Random Walks on Graphs
Speaker: Prof. José Bento
Affiliation: Dept. of Computer Science, Boston College
Location: Room 217 Huxley Building
Time: 11:15am – 12:30pm
Abstract. A connection between the distributed alternating direction method of multipliers (ADMM) and lifted Markov chains was recently proposed by Franca et al. 2016 for a non-strictly-convex consensus problem parametrized by a graph G. This was followed by a conjecture that ADMM is faster than gradient descent by a square root factor in its convergence time, in close analogy to the mixing speedup achieved by lifting several Markov chains. Nevertheless, a proof was lacking. In this talk, I will start by revisiting Franca’s conjecture. Afterwards, I will fully characterize the distributed over-relaxed ADMM for this consensus problem in terms of the topology of the graph G. To be specific, I will relate its convergence rate with the mixing time of random walks on the graph G. A consequence of this result is a proof of the aforementioned conjecture, which, interestingly, is valid for any graph, even those whose random walks cannot be accelerated via Markov chain lifting.
About the speaker. José Bento completed his Ph.D. in Electrical Engineering at Stanford University where he worked with Professor Andrea Montanari on statistical inference and structural learning of graphical models. After his Ph.D., he moved to Disney Research, Boston lab, where he worked with Dr. Jonathan Yedidia on algorithms for distributed optimization, robotics, and computer vision. He is now with the Computer Science department at Boston College. His current research lies at the intersection of distributed algorithms and machine learning. In 2014 he received a Disney Inventor Award for his work on distributed optimization, which recently lead to an approved patent. In 2016 he was awarded a $10M NIH joint grant to study the emergence of antibiotic resistance and in 2017 a $2M NSF joint grant to study measures of distance between large graphs.

Seminar: Large neighbourhood Benders' search
2018-04-17 14:00 - 15:30 (Europe/Paris)
Title: Large neighbourhood Benders’ search
Speaker: Dr. Stephen Maher
Affiliation: Lancaster University Management School
Location: Room 218 Huxley Building
Time: 14:00 – 15:30
Abstract. 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.

Seminar: Computing Pessimistic Leader-Follower Equilibria with Multiple Followers
2018-04-25 14:00 - 15:00 (Europe/Paris)
Title: Computing Pessimistic Leader-Follower Equilibria with Multiple Followers
Speaker: Dr Stefano Coniglio
Affiliation: Dept. of Mathematical Sciences, Southampton University
Location: Room 217 Huxley Building
Time: 14:00 – 15:00
Abstract. 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.
This is joint work with Nicola Gatti and Alberto Marchesi.
About 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.

Seminar: More Virtuous Smoothing and Embracing the Ghost of Rolle
2018-05-25 14:00 - 15:00 (Europe/Paris)
Title: More Virtuous Smoothing and Embracing the Ghost of Rolle
Speaker: Prof Jon Lee
Affiliation: Industrial and Operations Engineering, College of Engineering, University of Michigan
Location: LT 308 Huxley Building
Time: 14:00 – 15:00
Abstract. In the context of global optimization of mixed-integer nonlinear optimization formulations, we consider smoothing univariate concave increasing functions that have poorly behaved derivative at 0 (for example, root functions). Extending earlier work of Lee and Skipper, we give general conditions under which our smoothing is concave and increasing, is an underestimator, and dominates a simple “shift smoothing”.
This is joint work with Luze Xu and Daphne Skipper.
Biography. Jon is the G. Lawton and Louise G. Johnson Professor of Engineering and also Professor of Industrial and Operations Engineering, in the College of Engineering at the University of Michigan, Ann Arbor. He is an Affiliate of CHEPS (The Center for Healthcare Engineering and Patient Safety), MIDAS (Michigan Institute for Data Science), MCAIM (Michigan Center for Applied and Interdisciplinary Mathematics), and of MICDE (Michigan Institute for Computational Discovery and Engineering).
Currently, Jon is Editor-in-Chief of the journal Mathematical Programming (Series A), Editorial Board Member of the journal Optimization and Engineering, and Editorial Board Member of the journal Discrete Applied Mathematics. Jon is a Permanent Member of DIMACS, and a Full Member of the COIN-OR Foundation.

Seminar: Random projections in mathematical programming
2018-10-03 15:00 - 16:00 (Europe/Paris)
Title: Random projections in mathematical programming
Speaker: Dr Leo Liberti
Affiliation: CNRS LIX, École Polytechnique
Location: 218 Huxley Building
Time: 15:00 – 16:00
Abstract. In the algorithmic trade-off between generality and efficiency, sometimes the only way out is to accept approximate methods. If all else fails, we can always fall back on heuristic methods. But some form of approximation guarantee is usually preferable. In this talk we shall discuss a set of approximating reformulations to various classes of mathematical programming problems involving matrices. Random projections are a dimensionality reduction methodology which projects a set of vectors into a much lower dimensional space, so that the projected vector set is approximately congruent to the original one with high probability. The probability of failure falls exponentially fast as the dimension increases, making this a truly “big data” methodology. We shall show how to apply this methodology to Linear and Conic Programming, as well as (bounded) Quadratic Programming. We shall discuss applications to Quantile Regression and Error-Correcting Codes.
Biography. Dr Leo Liberti is CNRS Research Director (and part-time professor) at CNRS LIX (the Computer Science Laboratory) of École Polytechnique, France. Dr Liberti did his undergraduate studies in mathematics at Imperial College London and the University of Turin. He received his Ph.D. from Imperial College London in 2004 and HDR (French professorship diploma) from Paris-Dauphine University, Paris, France in 2007. His main research interests are in (i) reformulations in mathematical programming, (i) mixed-integer nonlinear programming (MINLP), global and combinatorial optimization, (iii) distance geometry and bioinformatics, and (iv) complex industrial systems and sustainable development.

Seminar: Arrangements of Circles and Spheres leading to Convex Hulls with Minimal Boundaries
2018-11-16 15:00 - 16:00 UTC
Title: Arrangements of Circles and Spheres leading to Convex Hulls with Minimal Boundaries
Speaker: Dr Josef Kallrath
Affiliation: Advanced Business Analytics, BASF SE, Germany
Location: 218 Huxley Building
Time: 15:00 – 16:00
Abstract. We present and solve a new computational geometry optimization problem in 2D and 3D and provide theoretical insights. Circles (2D) and Spheres (3D) with given radii should be arranged in such a way that a) they do not overlap and b) the length of the perimeter or surface area, resp., of the boundary ∂S of the convex hull S enclosing the spheres is minimized. An additional constraint could be to fit the spheres into a specified geometry, e.g., a rectangle or a rectangular solid, respectively.
A motivation for the 2D problem is a rope to be minimized, which encloses the circles, where the circles could be the base of cylinders loaded an a truck with rectangular loading area.
In 2D, ∂S is established by line segments and circular arcs. To tackle the problem, we derive a non-convex mixed-integer non-linear programming (MINLP) formulation. We exploited the isoperimetric inequality to get a tighter lower bound for the perimeter than the lower bound obtained by the relaxation of the MINLP. As demonstrated in numerical experiments, problem instances with only congruent circles are computational better tractable than non-congruent instances, as for congruent instances only the line segments have to be minimized. For small instances with up to five non-congruent circles, and minimizing only lL the MINLP problems we obtained gaps smaller than 10−4 with the current state-of-the-art global solvers BARON and LINDO available in GAMS. For larger problems of up to 90 circles, the relative gap between the upper and lower bound provided by the global solvers remains larger than 7 %.
In 3D, ∂S consists of spherical polygons with k vertices formed by k arcs of great circles, triangles and truncated cones. Here, we take a different approach than in the 2D situation. Similarly as in modeling the surface of eclipsing binary stars, we cover the surface of the spheres by a grid of approximately uniformly distributed points using spherical coordinates. We derive closed non-convex NLP models for this sphere arrangement problem.
For two spheres, we prove that the minimal area of ∂S is identical to the sum of the surface areas of the two spheres. For special configurations of spheres we provide theoretical insights and compute analytically minimal area configurations. Numerically, we have solved problems containing up to 200 spheres using a polylithic modeling and solution approach.

Seminar: Capacity upper bounds for deletion-type channels
2018-11-28 16:00 - 17:00 UTC
Title: Capacity upper bounds for deletion-type channels
Speaker: Dr Mahdi Cheraghchi
Affiliation: Dept of Computing, Imperial College London
Location: 218 Huxley Building
Time: 16:00 – 17:00
Abstract. We develop a systematic approach, based on convex programming and real analysis, for obtaining upper bounds on the capacity of the binary deletion channel and, more generally, channels with i.i.d. insertions and deletions. Other than the classical deletion channel, we give a special attention to the Poisson-repeat channel introduced by Mitzenmacher and Drinea (IEEE Transactions on Information Theory, 2006). Our framework can be applied to obtain capacity upper bounds for any repetition distribution (the deletion and Poisson-repeat channels corresponding to the special cases of Bernoulli and Poisson distributions). Our techniques essentially reduce the task of proving capacity upper bounds to maximizing a univariate, real-valued, and often concave function over a bounded interval. We show the following:
1. The capacity of the binary deletion channel with deletion probability $d$ is at most $(1-d)log(phi)$ for $d > 1/2$, and, assuming the capacity function is convex, is at most $1-d log(4/phi)$ for $d<1/2$, where $phi=(1+sqrt{5})/2$ is the golden ratio. This is the first nontrivial capacity upper bound for any value of $d$ outside the limiting case $d to 0$ that is fully explicit and proved without computer assistance.
2. We derive the first set of capacity upper bounds for the Poisson-repeat channel.
3. We derive several novel upper bounds on the capacity of the deletion channel. All upper bounds are maximums of efficiently computable, and concave, univariate real functions over a bounded domain. In turn, we upper bound these functions in terms of explicit elementary and standard special functions, whose maximums can be found even more efficiently (and sometimes, analytically, for example for $d=1/2$).
Along the way, we develop several new techniques of potentially independent interest in information theory, probability, and mathematical analysis.
[Based on an article published in proceedings of ACM STOC 2018]
Biography. Dr Mahdi Cheraghchi is a Lecturer in the Department of Computing at Imperial College London. Previously, he has been a Qualcomm Research Fellow at the Simons Institute for the Theory of Computing of U.C. Berkeley and have held post-doctoral researcher positions at the MIT Computer Science and Artificial Intelligence Lab, Computer Science Department of the Carnegie Mellon University and the University of Texas at Austin. He received his PhD in Computer Science from EPFL, Switzerland, where he worked as a research assistant in the Laboratory of Algorithms (ALGO) under the supervision of Amin Shokrollahi. Dr Cheraghchi main research interest is in the field of Theoretical Computer Science.

Seminar: Model-based design of optimal experiments using exact confidence regions
2019-02-06 16:00 - 17:00 UTC
Title: Model-based design of optimal experiments using exact confidence regions
Speaker: Dr Radoslav Paulen
Affiliation: Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology
Location: 218 Huxley Building
Time: 16:00 – 17:00
Abstract. This talk discusses a model-based optimal experiment design (OED) for nonlinear systems. OED represents a methodology for optimizing the geometry of the parametric joint-confidence regions, which are obtained in an a posteriori analysis of the parameter estimates. The optimal design is achieved by using the available (experimental) degrees of freedom such that more informative measurements are obtained. Unlike the commonly used approaches, which base the OED procedure upon the linearized CRs, we explore a path where we explicitly consider the exact CRs in the OED framework. We use a methodology for a finite parameterization of the exact CRs within the OED problem. The employed techniques give the OED problem as a finite-dimensional mathematical program of a bilevel nature. We discuss the use of several solution techniques tailored to the problem. Finally, we illustrate the methodology on few small-scale case studies and compare the resulting optimal designs with the commonly used linearization-based approach.
Biography. Dr Radoslav Paulen currently works at the Institute of Information Engineering, Automation and Mathematics at Slovak University of Technology in Bratislava. He does research in Modelling, Parameter Estimation, Optimization and Advanced Control of Dynamic Systems. The group of Dr Paulen concentrates on research in estimation and optimal control of nonlinear dynamic systems with applications in chemical and biochemical processes. The main research topics include (i) guaranteed and statistical parameter estimation, and (ii) dynamic optimization, global optimization, predictive control.

Seminar: Generalized maximum entropy estimation
2019-02-26 14:00 - 15:00 UTC
Title: Generalized maximum entropy estimation
Speaker: Dr David Sutter
Affiliation: Institute for Theoretical Physics, ETH Zurich
Location: 217 Huxley Building
Time: 14:00 – 15:00
Abstract. We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error.
This is joint work with T. Sutter, P. Esfahani, and J. Lygeros (arXiv:1708.07311).
Biography. Dr David Sutter is a postdoctoral researcher at ETH Zurich, working on mathematical aspects of quantum information theory. He obtained his PhD degree from the Institute for Theoretical Physics at ETH Zurich under the supervision of Prof. Renato Renner. Dr Sutter’s interests lie in the intersection of quantum mechanics, information theory, and mathematical physics. To understand the fundamental limits of information processing and communications, he utilizes tools from matrix analysis, optimization theory and probability theory.

Seminar: Data-Driven Methods for Integrated Production Scheduling and Process Control
2019-04-25 11:00 - 12:00 (Europe/London)
Title: Data-Driven Methods for Integrated Production Scheduling and Process Control
Speaker: Calvin Tsay
Affiliation: McKetta Dept of Chemical Engineering, University of Texas at Austin
Location: 218 Huxley Building
Time: 11:00 – 12:00
Abstract. Due to the fast-changing market conditions enabled by globalization and modern infrastructures, industrial production scheduling is often performed over relatively short time intervals to maximize profits. For chemical processes, plant dynamics and control become highly relevant at these shortened time intervals, and careful attention is required to ensure computed schedules are feasible when implemented in the physical process. With this motivation, many recent works focused on integrating dynamic information from the process control layer into production scheduling. Unfortunately, the resulting optimization problems are high-dimensional and often intractable because of the broad range of time scales involved.
In this presentation, we describe data-driven techniques for learning low-order dynamic models of the behavior of a process and its controller (i.e., “closed-loop” behavior). Then, we formulate an optimal scheduling problem involving the learned, reduced-order representation of the relevant dynamics. Furthermore, we present a data-mining approach that exploits historical process data to reduce the dimensionality of the aforementioned dynamic models. Throughout the presentation, we will focus on applications to the scheduling of industrial air separation units, which consume immense amounts of electricity, in response to time-varying electricity prices (an operational strategy termed “demand response”). Several case studies will be presented, ranging from model-based analyses to a real-world application on an industrial air separation unit.

Seminar: Exact and heuristic MIP methods for the solution of MINLP - Examples from gas transport optimization problems
2019-05-02 13:30 - 14:30 (Europe/London)
Title: Exact and heuristic MIP methods for the solution of MINLP – Examples from gas transport optimization problems
Speaker: Dr Lars Schewe
Affiliation: Dept of Mathematics, FAU Erlangen-Nürnberg
Location: 217 Huxley Building
Time: 13:30 – 14:30
Abstract. In this talk, we present exact and heuristic methods for MINLP, the development of which was motivated by applications in gas transport optimization. In this talk, we present a sample of our approaches and focus on provable results for both the exact and the heuristic methods. The methods have been applied on both academic and real-world instances. We first discuss how to solve MINLPs using a hierarchy of piece-wise linear relaxations and discuss a convergence result for such an algorithm. We show how this algorithm performs on problems in instationary gas transport. We then show how we can use a combination of penalty and alternating-direction methods to solve difficult instances of gas transport optimization problems and on instances from the MINLPLib. For these methods, we can also give convergence results and discuss their relation to feasibility pump methods.
