BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Computational Optimisation Group - ECPv6.15.11//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Computational Optimisation Group X-ORIGINAL-URL:https://optimisation.doc.ic.ac.uk X-WR-CALDESC:Events for Computational Optimisation Group REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20170326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20171029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20180325T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20181028T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20190331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20191027T010000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD TZOFFSETFROM:+0000 TZOFFSETTO:+0000 TZNAME:UTC DTSTART:20150101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Europe/Paris:20180425T140000 DTEND;TZID=Europe/Paris:20180425T150000 DTSTAMP:20260405T053702 CREATED:20180319T142205Z LAST-MODIFIED:20180319T142644Z UID:1056-1524664800-1524668400@optimisation.doc.ic.ac.uk 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/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Paris:20180417T140000 DTEND;TZID=Europe/Paris:20180417T153000 DTSTAMP:20260405T053702 CREATED:20180410T110415Z LAST-MODIFIED:20180410T110415Z UID:1055-1523973600-1523979000@optimisation.doc.ic.ac.uk 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/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20180119T111500 DTEND;TZID=UTC:20180119T123000 DTSTAMP:20260405T053702 CREATED:20180105T143648Z LAST-MODIFIED:20180105T143707Z UID:1024-1516360500-1516365000@optimisation.doc.ic.ac.uk SUMMARY:Seminar: ADMM and Random Walks on Graphs DESCRIPTION:Title: ADMM and Random Walks on Graphs\nSpeaker: Prof. José Bento\nAffiliation: Dept. of Computer Science\, Boston College\nLocation: Room 217 Huxley Building\nTime: 11:15am – 12:30pm \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-admm-and-random-walks-on-graphs/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20171121T140000 DTEND;TZID=UTC:20171121T150000 DTSTAMP:20260405T053702 CREATED:20171029T101434Z LAST-MODIFIED:20171029T101434Z UID:1012-1511272800-1511276400@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Stochastic Vehicle Routing: an Overview and some Recent Advances DESCRIPTION:Title: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems\nSpeaker: Prof. Michel Gendreau\nAffiliation: Dept. of Mathematics and Industrial Engineering\, Polytechnique Montréal\nLocation: LG19a\, Business School\nTime: 2:00pm \nAbstract. 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. \nIn 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. \nAbout 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. \nDr. 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-stochastic-vehicle-routing-an-overview-and-some-recent-advances/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20171114T160000 DTEND;TZID=UTC:20171114T170000 DTSTAMP:20260405T053702 CREATED:20170921T091809Z LAST-MODIFIED:20170921T091809Z UID:989-1510675200-1510678800@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems DESCRIPTION:Title: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems\nSpeaker: Prof. Stratos Pistikopoulos\, FREng\nAffiliation: Artie McFerrin Department of Chemical Engineering\, Texas A&M University\nLocation: Room 217 Huxley Building\nTime: 4:00pm \nAbstract. 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. \nIn 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. \nThis 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. \nAbout 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. \nHe 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-multi-level-optimization-by-multi-parametric-programming-its-use-for-the-solution-of-mixed-integer-adjustable-robust-optimization-problems/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20171027T160000 DTEND;TZID=UTC:20171027T170000 DTSTAMP:20260405T053702 CREATED:20171011T095354Z LAST-MODIFIED:20171011T095354Z UID:1001-1509120000-1509123600@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Robust Model Predictive Control DESCRIPTION:Title: Robust Model Predictive Control\nSpeaker: Dr. Saša V. Raković\nLocation: Room 218 Huxley Building\nTime: 4:00pm \nAbstract. \nModel 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. \nThe 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). \nAbout the speaker. \nDr. 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.) \nDr. 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. \nDr. 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. \nDr. 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. \nDr. 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-robust-model-predictive-control/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20170925T140000 DTEND;TZID=UTC:20170925T150000 DTSTAMP:20260405T053702 CREATED:20170921T091147Z LAST-MODIFIED:20170921T091419Z UID:987-1506348000-1506351600@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Prediction of Stock market crashes\,entry exits from bubbles\, hedge fund disasters and their prevention DESCRIPTION:Title: Prediction of Stock market crashes\,entry exits from bubbles\, hedge fund disasters and their prevention\nSpeaker: Prof. William T. Ziemba\nAffiliation: Sauder School of Business\, University of British Columbia\nLocation: LG19b Seminar Room\, Business School\nTime: 2:00pm \nAbstract. 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. \nAbout 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. \nHe 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. \nHe 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. \nRecent 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) \nHe 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-prediction-of-stock-market-crashesentry-exits-from-bubbles-hedge-fund-disasters-and-their-prevention/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20170120T150000 DTEND;TZID=UTC:20170120T150000 DTSTAMP:20260405T053702 CREATED:20170116T143747Z LAST-MODIFIED:20170116T144626Z UID:419-1484924400-1484924400@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Stochastic reformulations of linear systems and efficient randomized algorithms DESCRIPTION:Title: Stochastic reformulations of linear systems and efficient randomized algorithms\nSpeaker: Dr. Peter Richtarik\nAffiliation: School of Mathematics\, University of Edinburgh\nLocation: Room 217 Huxley Building\nTime: 3:00pm \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-stochastic-reformulations-of-linear-systems-and-efficient-randomized-algorithms/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20161213T133000 DTEND;TZID=UTC:20161213T133000 DTSTAMP:20260405T053702 CREATED:20170116T144707Z LAST-MODIFIED:20170116T145403Z UID:422-1481635800-1481635800@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Smart Grids and Optimization - A Winning Combination DESCRIPTION:Title: Smart Grids and Optimization – A Winning Combination\nSpeaker: Prof. Miguel Anjos\nAffiliation: Polytechnique Montreal\nLocation: Room 217 Huxley Building\nTime: 1:30pm (1 hour) \nAbstract. 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). \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-smart-grids-and-optimization-a-winning-combination/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20161206T150000 DTEND;TZID=UTC:20161206T150000 DTSTAMP:20260405T053702 CREATED:20170116T145225Z LAST-MODIFIED:20170116T145225Z UID:425-1481036400-1481036400@optimisation.doc.ic.ac.uk SUMMARY:Seminar: On Bit Representations of Mixed-Integer Quadratic Programs. DESCRIPTION:Title: On Bit Representations of Mixed-Integer Quadratic Programs.\nSpeaker: Prof. Adam Letchford\nAffiliation: Management School – Lancaster University\nLocation: Room 217 Huxley Building\nTime: 3:00pm (1 hour) \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-on-bit-representations-of-mixed-integer-quadratic-programs/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20161205T150000 DTEND;TZID=UTC:20161205T150000 DTSTAMP:20260405T053702 CREATED:20170116T145614Z LAST-MODIFIED:20170116T145614Z UID:428-1480950000-1480950000@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Solution of an old problem in vector optimisation - Support function of the generalised Jacobian DESCRIPTION:Title: Solution of an old problem in vector optimisation – Support function of the generalised Jacobian\nSpeaker: Prof. Abbas Edalat\nAffiliation: Department of Computing – Imperial College\nLocation: Room 217 Huxley Building\nTime: 3:00pm (1 hour) \nAbstract. 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. \nAbout the speaker. Abbas Edalat has been a professor of computer science and mathematics at the Department of Computing\, Imperial College London\, since 1997. URL:https://optimisation.doc.ic.ac.uk/event/seminar-solution-of-an-old-problem-in-vector-optimisation-support-function-of-the-generalised-jacobian/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20161107T143000 DTEND;TZID=UTC:20161107T143000 DTSTAMP:20260405T053702 CREATED:20170116T145746Z LAST-MODIFIED:20170116T145746Z UID:430-1478529000-1478529000@optimisation.doc.ic.ac.uk SUMMARY:Seminar: On Robust Selection Problems DESCRIPTION:Title: On Robust Selection Problems\nSpeaker: Marc Goerigk\nAffiliation: Department of Management Science – Lancaster University\nLocation: Room LT1 Business School\nTime: 2:30pm (1 hour) \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-on-robust-selection-problems/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20161103T163000 DTEND;TZID=UTC:20161103T163000 DTSTAMP:20260405T053702 CREATED:20170116T150029Z LAST-MODIFIED:20170116T150029Z UID:434-1478190600-1478190600@optimisation.doc.ic.ac.uk SUMMARY:Seminar: On the Passage From Local to Global in Optimization - New Challenges in Theory and Practice DESCRIPTION:Title: On the Passage From Local to Global in Optimization – New Challenges in Theory and Practice\nSpeaker: Prof. Panos M. Pardalos\nAffiliation: Departments of Industrial and Systems and Biomedical Engineering – University of Florida\nLocation: Room 145 Huxley Building\nTime: 4:30pm (1 hour) \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-on-the-passage-from-local-to-global-in-optimization-new-challenges-in-theory-and-practice/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20161025T140000 DTEND;TZID=UTC:20161025T170000 DTSTAMP:20260405T053702 CREATED:20170116T145907Z LAST-MODIFIED:20170116T145907Z UID:432-1477404000-1477414800@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Optimisation with occasionally accurate data DESCRIPTION:Title: Optimisation with occasionally accurate data\nSpeaker: Coralia Cartis\nAffiliation: Mathematical Institute – Oxford and Balliol College\nLocation: Huxley building\nTime: 2:00pm (1 hour) \nAbstract. 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). \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-optimisation-with-occasionally-accurate-data/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20161003T150000 DTEND;TZID=UTC:20161003T150000 DTSTAMP:20260405T053702 CREATED:20170124T101756Z LAST-MODIFIED:20170124T101756Z UID:529-1475506800-1475506800@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Pooling Problems: Advances in Theory and Applications DESCRIPTION: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 \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-pooling-problems-advances-in-theory-and-applications/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160916T150000 DTEND;TZID=UTC:20160916T150000 DTSTAMP:20260405T053702 CREATED:20170124T101756Z LAST-MODIFIED:20170124T101756Z UID:530-1474038000-1474038000@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Mixed-integer convex optimization DESCRIPTION:Title: Mixed-integer convex optimizationSpeaker: Miles LubinAffiliation: Massachusetts Institute of TechnologyLocation: Room 217 Huxley BuildingTime: 3:00pm \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-mixed-integer-convex-optimization/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160701T150000 DTEND;TZID=UTC:20160701T150000 DTSTAMP:20260405T053702 CREATED:20170124T101756Z LAST-MODIFIED:20170124T101756Z UID:531-1467385200-1467385200@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Scheduling Algorithms for Energy Efficiency in Computing Systems DESCRIPTION:Title: Scheduling Algorithms for Energy Efficiency in Computing SystemsSpeaker: Dimitrios LetsiosAffiliation: Department of Computing – Imperial College LondonLocation: Huxley BuildingTime: 3:00pm \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-scheduling-algorithms-for-energy-efficiency-in-computing-systems/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160531T170000 DTEND;TZID=UTC:20160531T170000 DTSTAMP:20260405T053702 CREATED:20170124T102131Z LAST-MODIFIED:20170124T102131Z UID:534-1464714000-1464714000@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Symmetry Groups and Topological Structure of Optimisation Problems DESCRIPTION:Title: Symmetry Groups and Topological Structure of Optimisation ProblemsSpeaker: Georgia KouyialisAffiliation: Department of Computing – Imperial College LondonLocation: Huxley BuildingTime: 5:00pm \nAbstract. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-symmetry-groups-and-topological-structure-of-optimisation-problems/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160531T160000 DTEND;TZID=UTC:20160531T160000 DTSTAMP:20260405T053702 CREATED:20170124T102131Z LAST-MODIFIED:20170124T102131Z UID:535-1464710400-1464710400@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Asymptotic Error Bounds for Control Constrained Singularly Perturbed Linear Quadratic Optimal Control Problems DESCRIPTION: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 \nAbstract. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-asymptotic-error-bounds-for-control-constrained-singularly-perturbed-linear-quadratic-optimal-control-problems/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160517T170000 DTEND;TZID=UTC:20160517T170000 DTSTAMP:20260405T053702 CREATED:20170124T102131Z LAST-MODIFIED:20170124T102131Z UID:536-1463504400-1463504400@optimisation.doc.ic.ac.uk SUMMARY:Seminar: A Parametric Approach to Solving the Pooling Problem DESCRIPTION:Title: A Parametric Approach to Solving the Pooling ProblemSpeaker: Radu Baltean LugojanAffiliation: Department of Computing – Imperial College LondonLocation: Huxley BuildingTime: 5:00pm \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-a-parametric-approach-to-solving-the-pooling-problem/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160517T160000 DTEND;TZID=UTC:20160517T160000 DTSTAMP:20260405T053702 CREATED:20170124T102132Z LAST-MODIFIED:20170124T102132Z UID:537-1463500800-1463500800@optimisation.doc.ic.ac.uk SUMMARY:Seminar: On the Convergence of Galerkin Type Multilevel Optimization Methods DESCRIPTION: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 \nAbstract. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-on-the-convergence-of-galerkin-type-multilevel-optimization-methods/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160503T170000 DTEND;TZID=UTC:20160503T170000 DTSTAMP:20260405T053702 CREATED:20170124T102132Z LAST-MODIFIED:20170124T102132Z UID:538-1462294800-1462294800@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Integrating Mixed Integer Optimisation and Logic with Satisfiability Modulo Theories DESCRIPTION: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 \nAbstract. 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. \nAbout 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. URL:https://optimisation.doc.ic.ac.uk/event/seminar-integrating-mixed-integer-optimisation-and-logic-with-satisfiability-modulo-theories/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160503T160000 DTEND;TZID=UTC:20160503T160000 DTSTAMP:20260405T053702 CREATED:20170124T102133Z LAST-MODIFIED:20170124T102133Z UID:539-1462291200-1462291200@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Multi-Level Accelerated Algorithm for Large-Scale Convex Composite Minimization DESCRIPTION:Title: Multi-Level Accelerated Algorithm for Large-Scale Convex Composite MinimizationSpeaker: Vahan HovhannisyanAffiliation: Department of Computing – Imperial College LondonLocation: Room 418 Huxley BuildingTime: 4:00pm \nAbstract. We propose a multi-level algorithm for solving convex composite optimization problems. Our method exploits the fact that many applications that give rise to large-scale problems can be modelled using varying degrees of fidelity. We show that it converges to a minimizer with optimal rate. Using numerical experiments we show that on large-scale computer vision problems our algorithm is several times faster than the state of the art. \nAbout the speaker. Vahan Hovhannisyan is a PhD student in the QUADS group at Imperial College\, under the supervision of Dr Panos Parpas. He received a BS in Applied Mathematics from the State Engineering University of Armenia and an MSc in Applied Mathematical (with application area in operations management) from ETH Zurich. His research interests are convex robust optimization with applications in machine learning. URL:https://optimisation.doc.ic.ac.uk/event/seminar-multi-level-accelerated-algorithm-for-large-scale-convex-composite-minimization/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160427T160000 DTEND;TZID=UTC:20160427T160000 DTSTAMP:20260405T053702 CREATED:20170124T102133Z LAST-MODIFIED:20170124T102133Z UID:540-1461772800-1461772800@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Parallel Algorithms and Applications in Structured Large-scale Optimization DESCRIPTION:Title: Parallel Algorithms and Applications in Structured Large-scale OptimizationSpeaker: Prof. Carl Laird Affiliation: School of Chemical Engineering – Purdue UniversityLocation: Lecture Theatre 2 ACEX BuildingTime: 4:00pm \nAbstract. Mathematical programming has proven to be an efficient tool for design and operation of chemical processes. However\, engineering and scientific needs continue to push the boundaries of existing mathematical programming tools\, often outstripping the capabilities of a single CPU workstation. Furthermore\, computer chip manufacturers are no longer focusing on increasing clock speeds\, and the free performance improvements that we have historically enjoyed will no longer be available\, unless we develop algorithms that are capable of utilizing modern parallel architectures. This presentation discusses advances in parallel algorithms for structured nonlinear mathematical programming problems\, along with a few applications of large-scale optimization. Large-scale optimization formulations arise from a number of different problem classes\, including design and operations under uncertainty\, optimization of complex networks\, and optimization of discretized systems. In this presentation\, I will outline applications in each of these areas. In design of process safety systems\, we have developed advanced stochastic programming formulations for the optimal placement of gas detectors in chemical process facilities based on data from CFD simulations of leak dispersion. As well\, we have partnered with both industry and federal agencies to develop a suite of tools for protecting drinking water distribution systems in the event of accidental or intentional contamination. Our research has focused on improved simulation capabilities\, optimal placement of booster response units\, real-time determination of contamination sources\, and response optimization. Finally\, we have been working with epidemiologists at Johns Hopkins University to develop improved models of infectious disease spread. These dynamic optimization formulations find seasonal patterns in inputs by solving inverse problems based on observed case counts. In particular\, these results help quantify the importance of school-term holiday schedules on the spread of childhood infectious diseases. \nAbout the speaker. Carl Laird is an associate professor in the School of Chemical Engineering at Purdue University. Dr. Laird’s research interests include large-scale nonlinear optimization and parallel scientific computing. Focus areas include chemical process systems\, homeland security applications\, and large-scale infectious disease spread. Dr. Laird is the recipient of several research and teaching awards\, including the CAST Division Outstanding Young Researcher Award\, National Science Foundation Faculty Early Development (CAREER) Award and the Montague Center for Teaching Excellence Award. He is also a recipient of the prestigious Wilkinson Prize for Numerical Software and the IBM Bravo award for his work on IPOPT\, a software library for solving nonlinear\, nonconvex\, large-scale continuous optimization problems. Dr. Laird earned his Ph.D. in Chemical Engineering from Carnegie Mellon in 2006 and his Bachelor of Science in Chemical Engineering from the University of Alberta. URL:https://optimisation.doc.ic.ac.uk/event/seminar-parallel-algorithms-and-applications-in-structured-large-scale-optimization/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160321T120000 DTEND;TZID=UTC:20160321T120000 DTSTAMP:20260405T053702 CREATED:20170124T102133Z LAST-MODIFIED:20170124T102133Z UID:541-1458561600-1458561600@optimisation.doc.ic.ac.uk SUMMARY:Seminar: On some tractable optimization models dealing with uncertainty DESCRIPTION:Title: On some tractable optimization models dealing with uncertaintySpeaker: Prof. Patrick JailletAffiliation: Laboratory for Information and Decision Systems – MITLocation: LG19 seminar room Business SchoolTime: 12:00pm \nAbstract. In the first part of the talk we consider the minmax regret model for combinatorial optimization problems under uncertainty\, which can be viewed as a zero-sum game played between an optimizing player and an adversary\, where the optimizing player selects a solution and the adversary selects costs with the intention of maximizing the regret of the player. The conventional model considers only deterministic solutions/strategies\, and minmax regret versions of most polynomial solvable problems are NP-hard. In this talk\, we consider a randomized model where the optimizing player selects a probability distribution (corresponding to a mixed strategy) over solutions and the adversary selects costs with knowledge of the player’s distribution\, but not its realization. We show that under this randomized model\, the minmax regret version of any polynomial solvable combinatorial problem becomes polynomial solvable. This holds true for both interval and discrete scenario representations of uncertainty. In the second part of the talk we consider satisficing models\, which\, as an approach to decision-making under uncertainty\, aims at achieving solutions that satisfy the problem’s constraints as much as possible. Mathematical optimization problems that are related to this form of decision-making include the P-model of Charnes and Cooper (1963)\, where satisficing is the objective\, as well as chance-constrained and robust optimization problems\, where satisficing is articulated in the constraints. In this talk\, we introduce the most general framework of a satisficing model\, termed the S-model\, which seeks to maximize a satisficing decision criterion in its objective\, and the corresponding satisficing-constrained optimization problem that generalizes robust optimization and chance-constrained optimization problems. We then focus on a tractable probabilistic S-model\, termed the T-model whose objective is a lower bound of the P-model. \nAbout the speaker. Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science and a member of the Laboratory for Information and Decision Systems at MIT. He is also one of the two Directors of the MIT Operations Research Center. Before MIT\, he held faculty positions at the University of Texas at Austin and at the Ecole Nationale des Ponts et Chaussees\, Paris. He received a Diplôme d’Ingénieur from France\, and a PhD in Operations Research from MIT. His current research interests include on-line and data-driven optimization under uncertainty. He is a Fellow of INFORMS and a member of SIAM. URL:https://optimisation.doc.ic.ac.uk/event/seminar-on-some-tractable-optimization-models-dealing-with-uncertainty/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160309T160000 DTEND;TZID=UTC:20160309T160000 DTSTAMP:20260405T053702 CREATED:20170124T102134Z LAST-MODIFIED:20170124T102134Z UID:542-1457539200-1457539200@optimisation.doc.ic.ac.uk SUMMARY:Seminar: On Optimal selection of fixed-size populations: an application to tree breeding DESCRIPTION:Title: On Optimal selection of fixed-size populations: an application to tree breedingSpeaker: Dr. Pietro BelottiAffiliation: FICO companyLocation: CPSE seminar room (C615 Roderic Hill)Time: 4:00pm \nAbstract. One of the problems that tree breeders face is the selection of a pedigree of trees with two aims: 1) conserving genetic diversity; 2) maximize response to selection. We tackled the problem of selecting a fixed-size breeding population while imposing a constraint on relatedness of the population members. The problem is expressed as a Mixed Integer Quadratically Constrained Optimization (MIQCO)\, in which a function is maximized subject to nonlinear quadratic constraints an discreteness of some variables\, and solved using a variant of the branch-and-bound method that uses a linear relaxation of the original problem. I will discuss details of the problem and of the algorithm (including a fast heuristic to find feasible solutions). I will also illustrate case studies of the selection of breeding populations for Scots pine and loblolly pine (Joint work with Tim Mullin\, Skogforsk\, the Swedish Forestry Research Institute). \nAbout the speaker. Pietro Belotti received a PhD in Computer Engineering in 2003 from the Technical University of Milan with a dissertation on optimal network design under survivability constraints. He has subsequently held a postdoctoral position at the Tepper School of Business\, Carnegie Mellon University\, a Visiting Professor post at the Department of Industrial and Systems Engineering\, Lehigh University\, and then an Assistant professor position at the department of Mathematical Sciences of Clemson University. He is currently working at Fair Isaac\, in the development team of the Xpress Optimizer. His research interests lie primarily in mixed integer nonlinear optimization\, robust optimization\, and discrete bi-objective optimization. URL:https://optimisation.doc.ic.ac.uk/event/seminar-on-optimal-selection-of-fixed-size-populations-an-application-to-tree-breeding/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160226T160000 DTEND;TZID=UTC:20160226T160000 DTSTAMP:20260405T053702 CREATED:20170124T102134Z LAST-MODIFIED:20170124T102134Z UID:543-1456502400-1456502400@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Large-scale MILP and MINLP problems in power system planning DESCRIPTION:Title: Large-scale MILP and MINLP problems in power system planningSpeaker: Dr. Ioannis KonstantelosAffiliation: Department of Electrical and Electronic Engineering – Imperial College LondonLocation: Room 218 Huxley BuildingTime: 4:00pm \nAbstract. Achieving the ambitious decarbonization goals set by governments worldwide will entail significant changes to the way electrical energy is generated\, transmitted and used. The cost-effective integration of inflexible low-carbon plant within conventional energy systems constitutes a significant challenge. Furthermore\, transmission planners are unable to make fully-informed decisions due to the increasing uncertainty that surrounds future system developments. Recently\, it has been shown that stochastic system planning based on scenario trees enables the identification of strategic opportunities for the management of long-term uncertainty. However\, the description of these problems is given by large MILP models which contain thousands of binary variables and tens of millions of continuous variables and constraints. After reviewing the general characteristics of the stochastic multi-stage transmission planning problem we will present two novel solution algorithms; one based on hierarchical decomposition and one on temporal problem splitting. We will demonstrate their computational benefits and highlight how efficient solutions can inform the real-world planning process. We will also present a typology of MILP and MINLP problems encountered in energy system planning and operation. \nAbout the speaker. Ioannis Konstantelos is a Research Associate in the Control and Power group\, Electrical Engineering\, Imperial College London. He obtained a PhD from Imperial College in 2013. His work has focused on the development of optimisation models for transmission and distribution system planning and operation aimed at valuing the benefit of new technologies\, demonstrating the strategic value of storage and demand-side response and other flexible technologies when facing long-term uncertainty. His research interests include the application of decomposition and machine learning techniques to large-scale optimization problems for energy systems. URL:https://optimisation.doc.ic.ac.uk/event/seminar-large-scale-milp-and-minlp-problems-in-power-system-planning/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160224T160000 DTEND;TZID=UTC:20160224T160000 DTSTAMP:20260405T053702 CREATED:20170124T102134Z LAST-MODIFIED:20170124T102134Z UID:544-1456329600-1456329600@optimisation.doc.ic.ac.uk SUMMARY:Seminar: On the standard pooling problem and strong valid inequalities DESCRIPTION:Title: On the standard pooling problem and strong valid inequalitiesSpeaker: Dr. Claudia D AmbrosioAffiliation: Laboratory for Information – Ecole PolytechniqueLocation: Room 311 Huxley BuildingTime: 4:00pm \nAbstract. The focus of this talk will be on the standard pooling problem\, i.e.\, a continuous\, non-convex optimization problem arising in the chemical engineering context. First\, we will introduce the problem that consists of finding the optimal composition of final products obtained by blending in pools different percentages of raw materials. Bilinear terms arise from the requirements on the quality of certain attributes of the final products. The quality is a linear combination of the attributes of the raw materials and intermediate products that compose the final product. Three different classical formulations have been proposed in the literature and their characteristics will be discussed and analysed. In the second part of the talk\, strong relaxations for the pooling problem will be presented. In particular\, we studied a structured non-convex subset of some special cases to derive valid nonlinear convex inequalities that we conjecture\, and proved for a particular case\, to define the convex hull of the non-convex subset. Preliminary computational results on instances from the literature are reported and demonstrate the utility of the inequalities when used in a global optimization solver. This is a joint work with Jeff Linderoth (University of Wisconsin-Madison)\, James Luedtke (University of Wisconsin-Madison)\, Jonas Schweiger (IBM). \nAbout the speaker. Claudia D’Ambrosio is a research scientist (chargé de recherche) at CNRS affiliated at LIX\, Ecole Polytechnique (France). She holds a Computer Science Engineering Master Degree and a PhD in Operations Research from University of Bologna (Italy). Her research speciality is mixed integer nonlinear programming. During her whole carrier\, she was involved both in theoretical and applied research projects. She was awarder the EURO Doctoral Dissertation Award for her PhD thesis supervised by Professor Andrea Lodi and the 2nd award “Prix Robert Faure” (3 candidates are awarded every 3 years) granted by ROADEF society. or more detailed info: URL:https://optimisation.doc.ic.ac.uk/event/seminar-on-the-standard-pooling-problem-and-strong-valid-inequalities/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160208T160000 DTEND;TZID=UTC:20160208T160000 DTSTAMP:20260405T053702 CREATED:20170124T102135Z LAST-MODIFIED:20170124T102135Z UID:545-1454947200-1454947200@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Revenue-Optimising Scheduling in Parallel Stochastic Networks DESCRIPTION:Title: Revenue-Optimising Scheduling in Parallel Stochastic NetworksSpeaker: Dr. Giuliano CasaleAffiliation: Department of Computing – Imperial College LondonLocation: Room 145 Huxley BuildingTime: 4:00pm \nAbstract. Cloud applications are often deployed on multiple virtual machines (VMs) with heterogeneous compute capacities. In this setting\, we consider the optimal static scheduling of users to application servers hosted in a set of parallel VMs. Our investigation seeks for a revenue-maximizing solution subject to resource utilization constraints\, multiple classes of users\, and a stochastic queueing-based description of latency experienced by the users at the VMs.After overviewing the general characteristics of scheduling in queueing networks\, and the underpinning optimization programs\, I will show that under a limiting regime this problem reduces to a bilinear optimization program. I will then introduce an heuristic solution for this program and determine an optimality gap. I will also demonstrate the effectiveness of this heuristic in a real system implementation and in comparison to approximate solutions that rely on convex formulations. \nAbout the speaker. URL:https://optimisation.doc.ic.ac.uk/event/seminar-revenue-optimising-scheduling-in-parallel-stochastic-networks/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20160119T150000 DTEND;TZID=UTC:20160119T150000 DTSTAMP:20260405T053702 CREATED:20170124T102135Z LAST-MODIFIED:20170124T102135Z UID:546-1453215600-1453215600@optimisation.doc.ic.ac.uk SUMMARY:Seminar: The Decision Rule Approach to Optimization Under Uncertainty: Theory and Applications DESCRIPTION:Title: The Decision Rule Approach to Optimization Under Uncertainty: Theory and ApplicationsSpeaker: Dr. Angelos GeorghiouAffiliation: Automatic Control Laboratory – Swiss Federal Institute of Technology (ETH)Location: Room 217 Huxley BuildingTime: 3:00pm \nAbstract. Decision making under uncertainty has a long and distinguished history in operations research. However\, most of the existing solution techniques suffer from the curse of dimensionality\, which restricts their applicability to small and medium-sized problems\, or they rely on simplifying modeling assumptions (e.g. absence of recourse actions). Recently\, a new solution technique has been proposed\, which is referred to as the decision rule approach. By approximating the feasible region of the decision problem\, the decision rule approach aims to achieve tractability without changing the fundamental structure of the problem. Despite their success\, existing decision rules (a) are typically constrained by their a priori design and (b) do not incorporate in their modeling binary recourse decisions. In this talk\, we present a methodology for the near optimal design of continuous and binary decision rules using mixed-integer optimization\, and demonstrate its potential in operations management applications. \nAbout the speaker. Angelos Georghiou is a post-doctoral researcher with the Automatic Control Laboratory at ETH Zurich. He joined ETH in 2013\, having previously been a post-doctoral researcher at the Process Systems Engineering Laboratory at MIT. He received the MSci degree in Mathematics in 2008 from Imperial College London\, and the Ph.D. degree in Operations Research in 2012 from the Department of Computing at Imperial College London. Angelos’s research focuses on the development of efficient computational methods for the solution of stochastic and robust optimization problems. His work is primarily application driven\, the main application areas being energy systems\, operations management\, and control. URL:https://optimisation.doc.ic.ac.uk/event/seminar-the-decision-rule-approach-to-optimization-under-uncertainty-theory-and-applications/ END:VEVENT END:VCALENDAR