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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20180417T140000
DTEND;TZID=Europe/Paris:20180417T153000
DTSTAMP:20260405T053616
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=Europe/Paris:20180425T140000
DTEND;TZID=Europe/Paris:20180425T150000
DTSTAMP:20260405T053616
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:20180525T140000
DTEND;TZID=Europe/Paris:20180525T150000
DTSTAMP:20260405T053616
CREATED:20180427T070629Z
LAST-MODIFIED:20180427T070629Z
UID:1135-1527256800-1527260400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: More Virtuous Smoothing and Embracing the Ghost of Rolle
DESCRIPTION:Title: More Virtuous Smoothing and Embracing the Ghost of Rolle\nSpeaker: Prof Jon Lee\nAffiliation: Industrial and Operations Engineering\, College of Engineering\, University of Michigan\nLocation: LT 308 Huxley Building\nTime: 14:00 – 15:00 \nAbstract. 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”. \nThis is joint work with Luze Xu and Daphne Skipper. \nBiography. 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). \nCurrently\, 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.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-more-virtuous-smoothing-and-embracing-the-ghost-of-rolle/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181003T150000
DTEND;TZID=Europe/Paris:20181003T160000
DTSTAMP:20260405T053616
CREATED:20181001T080116Z
LAST-MODIFIED:20181001T084704Z
UID:1154-1538578800-1538582400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Random projections in mathematical programming
DESCRIPTION:Title: Random projections in mathematical programming\nSpeaker: Dr Leo Liberti\nAffiliation: CNRS LIX\, École Polytechnique\nLocation: 218 Huxley Building\nTime: 15:00 – 16:00 \nAbstract. 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. \nBiography. 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.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-random-projections-in-mathematical-programming/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20181116T150000
DTEND;TZID=UTC:20181116T160000
DTSTAMP:20260405T053616
CREATED:20181001T084447Z
LAST-MODIFIED:20181001T084617Z
UID:1158-1542380400-1542384000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Arrangements of Circles and Spheres leading to Convex Hulls with Minimal Boundaries
DESCRIPTION:Title: Arrangements of Circles and Spheres leading to Convex Hulls with Minimal Boundaries\nSpeaker: Dr Josef Kallrath\nAffiliation: Advanced Business Analytics\, BASF SE\, Germany\nLocation: 218 Huxley Building\nTime: 15:00 – 16:00 \nAbstract. 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. \nA 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. \nIn 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 %. \nIn 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. \nFor 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.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-arrangements-of-circles-and-spheres-leading-to-convex-hulls-with-minimal-boundaries/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20181128T160000
DTEND;TZID=UTC:20181128T170000
DTSTAMP:20260405T053616
CREATED:20180907T201315Z
LAST-MODIFIED:20181126T205541Z
UID:1145-1543420800-1543424400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Capacity upper bounds for deletion-type channels
DESCRIPTION:Title: Capacity upper bounds for deletion-type channels\nSpeaker: Dr Mahdi Cheraghchi\nAffiliation: Dept of Computing\, Imperial College London\nLocation: 218 Huxley Building\nTime: 16:00 – 17:00 \nAbstract. 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: \n1. 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. \n2. We derive the first set of capacity upper bounds for the Poisson-repeat channel. \n3. 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 $d=1/2$). \nAlong the way\, we develop several new techniques of potentially independent interest in information theory\, probability\, and mathematical analysis. \n[Based on an article published in proceedings of ACM STOC 2018] \nBiography. 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.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-capacity-upper-bounds-for-deletion-type-channels/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20190206T160000
DTEND;TZID=UTC:20190206T170000
DTSTAMP:20260405T053616
CREATED:20190123T095139Z
LAST-MODIFIED:20190123T095139Z
UID:1208-1549468800-1549472400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Model-based design of optimal experiments using exact confidence regions
DESCRIPTION:Title: Model-based design of optimal experiments using exact confidence regions\nSpeaker: Dr Radoslav Paulen\nAffiliation: Institute of Information Engineering\, Automation and Mathematics\, Slovak University of Technology\nLocation: 218 Huxley Building\nTime: 16:00 – 17:00 \nAbstract. 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. \nBiography. 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.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-model-based-design-of-optimal-experiments-using-exact-confidence-regions/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20190226T140000
DTEND;TZID=UTC:20190226T150000
DTSTAMP:20260405T053616
CREATED:20190214T121614Z
LAST-MODIFIED:20190214T143922Z
UID:1212-1551189600-1551193200@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Generalized maximum entropy estimation
DESCRIPTION:Title: Generalized maximum entropy estimation\nSpeaker: Dr David Sutter\nAffiliation: Institute for Theoretical Physics\, ETH Zurich\nLocation: 217 Huxley Building\nTime: 14:00 – 15:00 \nAbstract. 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. \nThis is joint work with T. Sutter\, P. Esfahani\, and J. Lygeros (arXiv:1708.07311). \nBiography. 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.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-generalized-maximum-entropy-estimation/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20190425T110000
DTEND;TZID=Europe/London:20190425T120000
DTSTAMP:20260405T053616
CREATED:20190416T132348Z
LAST-MODIFIED:20190416T132348Z
UID:1226-1556190000-1556193600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Data-Driven Methods for Integrated Production Scheduling and Process Control
DESCRIPTION:Title: Data-Driven Methods for Integrated Production Scheduling and Process Control\nSpeaker: Calvin Tsay\nAffiliation: McKetta Dept of Chemical Engineering\, University of Texas at Austin\nLocation: 218 Huxley Building\nTime: 11:00 – 12:00 \nAbstract. 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. \nIn 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.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-data-driven-methods-for-integrated-production-scheduling-and-process-control/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20190502T133000
DTEND;TZID=Europe/London:20190502T143000
DTSTAMP:20260405T053616
CREATED:20190410T081934Z
LAST-MODIFIED:20190410T081934Z
UID:1222-1556803800-1556807400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Exact and heuristic MIP methods for the solution of MINLP - Examples from  gas transport optimization problems
DESCRIPTION:Title: Exact and heuristic MIP methods for the solution of MINLP – Examples from gas transport optimization problems\nSpeaker: Dr Lars Schewe\nAffiliation: Dept of Mathematics\, FAU Erlangen-Nürnberg\nLocation: 217 Huxley Building\nTime: 13:30 – 14:30 \nAbstract. 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.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-exact-and-heuristic-mip-methods-for-the-solution-of-minlp-examples-from-gas-transport-optimization-problems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20190502T150000
DTEND;TZID=Europe/London:20190502T160000
DTSTAMP:20260405T053616
CREATED:20190410T081937Z
LAST-MODIFIED:20190410T081937Z
UID:1223-1556809200-1556812800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Robust Discrete Optimization: Globalized Gamma Robustness and Radius of Robust Feasibility
DESCRIPTION:Title: Robust Discrete Optimization: Globalized Gamma Robustness and Radius of Robust Feasibility\nSpeaker: Prof. Dr Frauke Liers\nAffiliation: Dept of Mathematics\, FAU Erlangen-Nürnberg\nLocation: 217 Huxley Building\nTime: 15:00 – 16:00 \nAbstract. In this talk\, we extend the notion of two robust optimization methodologies that were originally introduced for continuous problems towards robust discrete tasks. On the one hand\, we look at globalized robust optimization that has been proposed as a generalization of the standard robust optimization framework in order to allow for a controlled decrease in protection. It depends on the distance of the realized from the predefined uncertainty set. In this talk\, we specialize the notion of globalized robustness to Gamma-uncertainty in order to extend its usability for discrete optimization. We show that the generalized robust counterpart possesses algorithmically tractable reformulations for mixed-integer linear nominal problems that use only slightly more variables and constraints than the standard robust counterpart under Gamma-uncertainty. For combinatorial problems\, our globalized robust counterpart remains fixed-parameter tractable\, although with a runtime exponential in Gamma. In computational studies\, it turns out that our algorithmically tractable reformulations are not more difficult to solve than the respective standard robust counterparts\, while globalized robustness is guaranteed. Secondly\, we extend the notion of determining the radius of robust feasibility for a mixed integer linear problem (MIP) with uncertain constraints. The radius of robust feasibility (RRF) determines a value for the maximal size of the uncertainty set such that robust feasibility of the MIP can be guaranteed. We will analyze relations between the RRF of a MIP and its continuous relaxation. In contrast to the general setting of the literature\, we extend the concept to computing the RRF to MIPs that might include safe constraints. Finally\, we apply our methods to the standard benchmark set of the MIPLIB in order to test their performance and analyze the price of robustness with respect to the RRF.The work about Globalized Gamma Robustness is joint with Andreas Bärmann (FAU Erlangen-Nürnberg\, Germany) and Christina Büsing (RWTH Aachen\, Germany). The work about the radius of robust feasibility is joint with Lars Schewe and Johannes Thürauf (both FAU Erlangen-Nürnberg\, Germany)
URL:https://optimisation.doc.ic.ac.uk/event/seminar-robust-discrete-optimization-globalized-gamma-robustness-and-radius-of-robust-feasibility/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20190628T140000
DTEND;TZID=Europe/London:20190628T150000
DTSTAMP:20260405T053616
CREATED:20190610T073147Z
LAST-MODIFIED:20190610T073147Z
UID:1254-1561730400-1561734000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Extremal Cuts and Isoperimetry in Random Cubic Graphs
DESCRIPTION:Title: Extremal Cuts and Isoperimetry in Random Cubic Graphs\nSpeaker: Prof. Gregory B. Sorkin\nAffiliation: Dept of Mathematics\, The London School of Economics and Political Science (LSE)\nLocation: LT139 Huxley Building\nTime: 14:00 – 15:00 \nAbstract. The minimum bisection width of random cubic graphs is of interest because it is one of the simplest questions imaginable in extremal combinatorics\, and also because the minimum bisection of (general) cubic graphs plays a role in the construction of efficient exponential-time algorithms\, and it seems likely that random cubic graphs are extremal. \nIt is known that a random cubic graph has a minimum bisection of size at most 1/6 times its order (indeed this is known for all cubic graphs)\, and we reduce this upper bound to below 1/7 (to 0.13993) by analyzing an algorithm with a couple of surprising features. We increase the corresponding lower bound on minimum bisection using the Hamilton cycle model of a random cubic graph. We use the same Hamilton cycle approach to decrease the upper bound on maximum cut. We will discuss some related conjectures.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-extremal-cuts-and-isoperimetry-in-random-cubic-graphs/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20190702T140000
DTEND;TZID=Europe/London:20190702T150000
DTSTAMP:20260405T053616
CREATED:20190425T090204Z
LAST-MODIFIED:20190425T090204Z
UID:1229-1562076000-1562079600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Chordal Completions - Semidefinite Programming and Minimum Completions
DESCRIPTION:Title: Chordal Completions – Semidefinite Programming and Minimum Completions\nSpeaker: Dr Arvind Raghunathan\nAffiliation: Mitsubishi Electric Research Laboratories (MERL)\nLocation: 217 Huxley Building\nTime: 14:00 – 15:00 \nAbstract. A graph is chordal if every cycle of length at least four contains a chord\, that is\, an edge connecting two nonconsecutive vertices of the cycle. Chordal completion of a given undirected graph G is a chordal graph\, on the same vertex set\, that has G as a subgraph. Several classical applications in sparse linear systems\, database management\, computer vision\, and SemiDefinite Programming (SDP) utilize chordal completions. The computation workload that results can be related to the number of edges that are added. Hence\, finding the minimum number of edges that makes a graph chordal is important. We refer to this as the Minimum Chordal Completion Problem (MCCP). In this talk\, we will present results on an application of completions in SDPs and the solution of MCCP. \nConversion approach\, a decomposition based on chordal completions\, is routinely used for solving large-scale SDPs. We show that the SDP resulting from the conversion approach is numerically degenerate under very mild assumptions. Numerical experiments on SDPLIB are provided to demonstrate the impact on solvers such as SDPT3 and SeDuMi. \nWe propose a new formulation for the MCCP which does not rely on finding perfect elimination orderings of the graph\, as has been considered in previous work. We introduce several families of facet-defining inequalities for cycle subgraphs. Numerical studies combining heuristic separation methods based on a threshold rounding and lazy-constraint generation indicate that our approach substantially outperforms existing methods for the MCCP\, solving many benchmark graphs to optimality for the first time. \nBiography. Arvind Raghunathan is a Senior Principal Scientist at Mitsubishi Electric Research Laboratories (MERL). His research interests are in the development of algorithms for the solution of nonlinear and mixed integer programming problems with applications in electric grid operations\, model predictive control\, and transportation. Arvind’s research has found business impact in Mitsubishi’s products and has won top technical honors within MERL and Mitsubishi Electric Corporation. Arvind currently serves as an associate editor for Optimization & Engineering journal and as an expert of ANSI on the ISO Working Group on Smart Transportation. He obtained a Ph.D. from Carnegie Mellon University and a B.Tech from Indian Institute of Technology (Madras) both in Chemical Engineering. He worked for United Technologies Research Center for 7 years prior to joining MERL.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-chordal-completions-semidefinite-programming-and-minimum-completions/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20190722T140000
DTEND;TZID=UTC:20190722T150000
DTSTAMP:20260405T053616
CREATED:20190703T163349Z
LAST-MODIFIED:20190703T163349Z
UID:1273-1563804000-1563807600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Largest Small n-Polygons: Numerical Results and Conjectured Optima
DESCRIPTION:Title: Largest Small n-Polygons: Numerical Results and Conjectured Optima \nSpeaker: János D. Pintér \nAffiliation: Department of Industrial and Systems Engineering\, Lehigh University \nLocation: 218 Huxley Building \nTime: 14:00 – 15:00 \n  \nAbstract. LSP(n)\, the largest small polygon with n vertices\, is defined as the polygon of unit diameter that\nhas maximal area A(n). Finding the configuration LSP(n) and the corresponding A(n) for even\nvalues n >= 6 is a long-standing challenge that can be also perceived as class of hard global\noptimization problems. We present numerical solution estimates for all even values 6 <= n <= 80\,\nusing the AMPL model development environment with the LGO global-local solver engine option.\nOur results are in close agreement with the results obtained by other researchers who tackled the\nproblem using exact approaches (for 6 <= n <= 20)\, and with the best results obtained using general\npurpose numerical optimization software (for selected values from the range 6 <= n <= 100). Based\non our numerical results\, we also present a regression model based estimate of {A(n)} for all even\nvalues n >= 6. \n  \nBio. János D. Pintér is a researcher and practitioner with over four decades of experience. His\ngeneral professional interests are related to Computational Optimization\, Data Analytics\, and\nOperations Research (O.R.). His more specific primary area of expertise is nonlinear optimization\,\nincluding model\, algorithm and software development\, with a range of applications.\nHe received his M.Sc. in the area of Applied Mathematics / Operations Research from\nEötvös Loránd University\, Hungary; Ph.D. in Probability Theory / Stochastic Optimization from\nMoscow State University; and D.Sc. in Mathematics / Global Optimization from the Hungarian\nAcademy of Sciences. \nAs of 2019\, Dr. Pintér wrote and edited ten books. He is also the author/co-author of more\nthan 200 journal articles\, book chapters\, proceedings contributions\, book reviews\, and research\nreports. His monograph titled Global Optimization in Action received the 2000 INFORMS\nComputing Society Prize for Research Excellence. \nAmong other professional affiliations\, he serves on the editorial board of the Journal of\nGlobal Optimization\, and he is an editor of the book series SpringerBriefs in Optimization. He also\nserved as Global Optimization vice-chair of the INFORMS Optimization Society\, and as a member\n(later chair) of the Managing Board of EUROPT. Currently\, he is a member of the Canadian and\nthe Hungarian Operations Research Societies\, INFORMS\, and EUROPT.\nHe has worked and presented lectures in about 40 countries of the Americas\, Europe\, the\nMiddle East\, and the Pacific Region. His LGO software – with links to modeling languages and\nscientific-technical computing systems – has been in use at hundreds of academic\, business\,\ngovernment\, and research organizations. \nIn 2016\, Dr. Pintér joined the Department of Industrial and Systems Engineering at Lehigh\nUniversity as a Professor of Practice. Since that time\, he has been teaching a range of O.R. related\ncourses for undergraduate and graduate students\, as well as ISE in-class and online (distance)\ncourses for healthcare engineering professionals. \n 
URL:https://optimisation.doc.ic.ac.uk/event/seminar-largest-small-n-polygons-numerical-results-and-conjectured-optima/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20191128T140000
DTEND;TZID=UTC:20191128T153000
DTSTAMP:20260405T053616
CREATED:20191025T170003Z
LAST-MODIFIED:20191025T170134Z
UID:1418-1574949600-1574955000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar with Prof. Greg Sorkin: Extremal Cuts and Isoperimetry in Random Cubic Graphs
DESCRIPTION:The minimum bisection width of random cubic graphs is of interest because it is one of the simplest questions imaginable in extremal combinatorics\, and also because the minimum bisection of (general) cubic graphs plays a role in the construction of efficient exponential-time algorithms\, and it seems likely that random cubic graphs are extremal. \nIt is known that a random cubic graph has a minimum bisection of size at most 1/6 times its order (indeed this is known for all cubic graphs)\, and we reduce this upper bound to below 1/7 (to 0.13993) by analyzing an algorithm with a couple of surprising features. We increase the corresponding lower bound on minimum bisection using the Hamilton cycle model of a random cubic graph. We use the same Hamilton cycle approach to decrease the upper bound on maximum cut. We will discuss some related conjectures.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-with-prof-greg-sorkin-extremal-cuts-and-isoperimetry-in-random-cubic-graphs/
LOCATION:Huxley 217
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BEGIN:VEVENT
DTSTART;TZID=UTC:20191202T103000
DTEND;TZID=UTC:20191202T233000
DTSTAMP:20260405T053616
CREATED:20190723T135610Z
LAST-MODIFIED:20190916T101732Z
UID:1290-1575282600-1575329400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar by prof. Miguel Anjos
DESCRIPTION:Professor Miguel Anjos from the University of Edinburgh is giving a seminar on: Tight-and-Cheap Conic Relaxations for AC Optimal Power Flow and Optimal Reactive Power Dispatch \n  \nAbstract: The classical alternating current optimal power flow problem is nonconvex and generally hard to solve. We propose a new conic relaxation obtained by combining semidefinite optimization with RLT. The proposed relaxation is stronger than the second-order cone relaxation\, competitive with the recently proposed QC relaxation\, and up to one order of magnitude faster than for the semidefinite chordal approach on benchmarks with up to 6515 nodes\, with comparable global bounds. We extend the approach to optimal reactive power dispatch\, which requires the introduction of binary and integer variables\, and obtain with similar results and performance.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-by-prof-miguel-anjos/
LOCATION:Huxley 217
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BEGIN:VEVENT
DTSTART;TZID=UTC:20191206T140000
DTEND;TZID=UTC:20191206T153000
DTSTAMP:20260405T053616
CREATED:20191205T141856Z
LAST-MODIFIED:20191205T141940Z
UID:1432-1575640800-1575646200@optimisation.doc.ic.ac.uk
SUMMARY:Seminar by  Associate Professor Jakob Nordström
DESCRIPTION:TITLE:\nLearn to Relax: Integrating Integer Linear Programming with Conflict-Driven Search\n\n  \nABSTRACT:\nPseudo-Boolean (PB) solvers optimize 0-1 integer linear programs by\nextending the conflict-driven learning paradigm from SAT solving.\nThough PB solvers should be exponentially more efficient than SAT\nsolvers in theory\, in practice they can sometimes get hopelessly stuck\neven when the relaxed linear program (LP) is infeasible over the\nreals.  Inspired by mixed integer programming (MIP)\, we address this\nproblem by interleaving incremental LP solving with cut generation\nwithin the conflict-driven PB search.  This hybrid approach\, which for\nthe first time combines MIP techniques with full-blown conflict\nanalysis over linear inequalities using the cutting planes method\,\nsignificantly improves performance on a wide range of benchmarks\,\napproaching a “best of two worlds” scenario between SAT-style\nconflict-driven search and MIP-style branch-and-cut.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-by-associate-professor-jakob-nordstrom/
LOCATION:Huxley 217
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20200323T140000
DTEND;TZID=UTC:20200323T150000
DTSTAMP:20260405T053616
CREATED:20200127T124815Z
LAST-MODIFIED:20200317T114146Z
UID:1446-1584972000-1584975600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar by Prof. Oliver Stein (Cancelled due to the Covid-19 situation)
DESCRIPTION:Title: Pessimistic Bilevel Optimization \nPessimistic bilevel optimization problems\, as optimistic ones\, possess a structure involving\nthree interrelated optimization problems. Moreover\, their finite infima are only\nattained under strong conditions. We address these difficulties within a framework of moderate\nassumptions and a perturbation approach which allow us to approximate such finite\ninfima arbitrarily well by minimal values of a sequence of solvable single-level problems. \nTo this end\, as already done for optimistic problems\, we introduce the standard version of\nthe pessimistic bilevel problem. For its algorithmic treatment\, we reformulate it as a standard\noptimistic bilevel program with a two follower Nash game in the lower level. The latter lower level\ngame\, in turn\, is replaced by its Karush-Kuhn-Tucker conditions\, resulting in a single-level\nmathematical program with complementarity constraints. \nWe show that the perturbed pessimistic bilevel problem\, its standard version\, the\ntwo follower game as well as the mathematical program with complementarity constraints\nare equivalent with respect to their global minimal points. We also highlight the more intricate\nconnections between their local minimal points. As an illustration\, we consider a regulator problem\nfrom economics. \n  \nBio: \nOliver Stein is full professor at the Institute of Operations Research (IOR) at the Karlsruhe Institute of Technology (KIT).\nHe received his doctoral degree from the University of Trier in 1997\, and his venia legendi from RWTH Aachen University in 2002.\nHis research covers algorithms and their theoretical foundation for continuous and mixed-integer nonlinear optimization problems\,\nparametric optimization\, multi-leader-multi-follower games\, and multi-objective optimization. Oliver was fellow of the Friedrich-Ebert\nFoundation\, the Alexander-von-Humboldt Foundation\, and the German Research Foundation (Heisenberg followship)\, and received various\nteaching awards. Oliver is member of MOS\, SIAM\, GAMM\, GOR\, and DMV. Since 2015 he acts as Editor-in-Chief of MMOR.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-by-prof-oliver-stein/
LOCATION:CPSE lecture theatre
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240119T140000
DTEND;TZID=UTC:20240119T150000
DTSTAMP:20260405T053616
CREATED:20240117T221445Z
LAST-MODIFIED:20240117T221445Z
UID:1630-1705672800-1705676400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Bayesian Optimization in Molecule Space: Challenges and Opportunities
DESCRIPTION:Title: Bayesian Optimization in Molecule Space: Challenges and Opportunities\nSpeaker: Austin Tripp\nAffiliation: University of Cambridge\nLocation: Huxley 315 \nAbstract. Rational design of experiments in chemistry is one of the most commonly mentioned applications of Bayesian optimization (BO). Therefore you might presume that existing BO algorithms for chemistry are well-developed. In this talk I explain how performing BO on the discrete\, structured space of molecules introduces extra complexity to BO which standard methods do not handle well. I will outline specific problems and potential avenues for solving them\, in addition to covering some recent work in this area. All are welcome\, but the target audience for this talk is optimization researchers interested in the fundamental algorithmic problems which chemistry applications present. \nBiography. Austin Tripp is a final-year PhD student at Cambridge researching ML methods for molecules. More info on his website austintripp.ca \n 
URL:https://optimisation.doc.ic.ac.uk/event/seminar-bayesian-optimization-in-molecule-space-challenges-and-opportunities/
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END:VCALENDAR