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Autumn Semester 2012

Date / Time Speaker Title Location
8 October 2012
16:30-18:00 		Prof. Dr. Mirjam Dür
Universität Trier, Deutschland
Event Details

Optimization Seminar

Title Copositive optimization, the copositive cone, and its relation with Parrilo's approximations
Speaker, Affiliation Prof. Dr. Mirjam Dür, Universität Trier, Deutschland
Date, Time 8 October 2012, 16:30-18:00
Location HG G 19.1
Abstract A copositive optimization problem is a linear problem in matrix variables with the additional constraint that the variable is in the cone of copositive matrices. Here a matrix A is called copositive if the quadratic form (x^T)Ax is nonnegative for all nonnegative x. Copositive optimization problems are of interest, because many quadratic as well as combinatorial problems can be written in this form. A well studied example is the maximum clique problem from graph theory. However, the structure of the copositive cone is not well understood, and in general copositive problems are not tractable. For this reason, a number of relaxations have been proposed, among them a hierarchy of approximating cones was proposed by Parrilo. The talk will give an introduction to these topics and place particular emphasis on the relation between the cone and Parrilo's approximations.
Copositive optimization, the copositive cone, and its relation with Parrilo's approximationsread_more
HG G 19.1
15 October 2012
16:30-18:00 		Prof. Dr. Klaus Schittkowski
Universität Bayreuth, Bayreuth, Deutschland
Event Details

Optimization Seminar

Title A New Trust-Region-SQP Algorithm for the Efficient Solution of Non-Convex, Non-Relaxable Mixed-Integer Nonlinear Programming Problems
Speaker, Affiliation Prof. Dr. Klaus Schittkowski, Universität Bayreuth, Bayreuth, Deutschland
Date, Time 15 October 2012, 16:30-18:00
Location HG G 19.1
Abstract A new sequential quadratic programming (SQP) algorithm stabilized by trust-regions for solving nonlinear, non-convex and non-relaxable mixed-integer optimization problems is introduced. The mixed-integer quadratic programming subproblems are solved by a branch-and-cut algorithm. Second order information is updated by a modified quasi-Newton update formula (BFGS) applied to the Lagrange function for continuous, but also for integer variables. The design goal is to solve practical optimization problems with expensive executions of an underlying simulation program. Thus, the number of simulations or function evaluations, respectively, is our main performance criterion to measure the efficiency of the code. Numerical results are presented for a set of 186 mixed-integer test problems and different parameter settings of MISQP. The average total number of function evaluations of the new mixed-integer SQP code is less than 1,500 including those needed to approximate partial derivatives.
A New Trust-Region-SQP Algorithm for the Efficient Solution of Non-Convex, Non-Relaxable Mixed-Integer Nonlinear Programming Problemsread_more
HG G 19.1
22 October 2012
16:30-18:00 		Prof. Dr. Joydeep Dutta
Indian Institute of Technology Kanpur, India
Event Details

Optimization Seminar

Title Gap Functions and Error Bounds for Variational Inequalities
Speaker, Affiliation Prof. Dr. Joydeep Dutta, Indian Institute of Technology Kanpur, India
Date, Time 22 October 2012, 16:30-18:00
Location HG G 19.1
Abstract In this talk we focus on the variational inequalities on finite dimensions. In finite dimensions variational inequalities can be thought of as generalizations of the necessary optimality conditions for minimizing a convex function over a closed convex set. If we consider the convex function to be differentiable then it gives rise to the well studied Stampacchia variational inequality. Further if the convex function is not differentiable then it gives rise to so called generalized variational inequalities or variational inequalities with set-valued maps. It is natural from the point of view of computations to find an error bound to solution set of some well-behaved class of variational inequalities namely monotone and strongly monotone class of variational inequalities. This is usually achieved using the gap function. We motivate as to why we need to use the gap function to develop error bounds. We present some important class of gap functions and their properties which make them relevant in devising error bounds. At the end of the talk we discuss the recent research in the study of gap functions and their applications to error bounds for variational inequalities with set-valued maps and we shall end the talk with an open question.
Gap Functions and Error Bounds for Variational Inequalitiesread_more
HG G 19.1
29 October 2012
16:30-18:00 		Prof. Dr. Cordian Riener
Universität Konstanz
Event Details

Optimization Seminar

Title Bounding hard problems with semi-definite programming using symmetries
Speaker, Affiliation Prof. Dr. Cordian Riener, Universität Konstanz
Date, Time 29 October 2012, 16:30-18:00
Location HG G 19.1
Abstract Within recent years, a lot of nice results have been obtained by applying semidefinite relaxations to hard problems. An essential step in actually solving these relaxations is to use further knowledge about additional properties of the problem such as symmetries. The talk will introduce the ideas of how to exploit symmetries in problems which can be expressed as polynomial optimization problems.
Bounding hard problems with semi-definite programming using symmetriesread_more
HG G 19.1
5 November 2012
16:30-17:30 		Prof. Dr. Jeffrey T. Linderoth
University of Wisconsin-Madison, USA
Event Details

Optimization Seminar

Title Mixed-Integer Nonlinear Programs with On-Off Constraints
Speaker, Affiliation Prof. Dr. Jeffrey T. Linderoth, University of Wisconsin-Madison, USA
Date, Time 5 November 2012, 16:30-17:30
Location HG G 19.1
Abstract We study optimization problems that have both nonlinear functional relationships between decision variables and 0-1 indicator variables that turn on and off these relationships. Problems of this class occur in many areas, including statistics, financial engineering, and engineering design. After reviewing earlier work on a reformulation technique applicable to the case when the nonlinear functions are separable, we discuss on-going research aimed at attacking the non-separable case. Our primary focus will be on the case when the nonlinearities are quadratic.
Mixed-Integer Nonlinear Programs with On-Off Constraintsread_more
HG G 19.1
12 November 2012
16:30-18:00 		Prof. Dr. Andreas Krause
Computer Science of ETH Zurich
Event Details

Optimization Seminar

Title Sequential Decision Making in Uncertain Domains via Adaptive Submodularity
Speaker, Affiliation Prof. Dr. Andreas Krause, Computer Science of ETH Zurich
Date, Time 12 November 2012, 16:30-18:00
Location HG G 19.1
Abstract Solving sequential decision problems under partial observability is a fundamental but notoriously difficult challenge. In this talk, I will introduce the new concept of adaptive submodularity, generalizing the classical notion of submodular set functions to adaptive policies. We prove that, if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. The concept allows to recover, generalize, and extend existing results in diverse applications, including sensor management, viral marketing, and active learning. I will focus on two case studies. In an application to Bayesian experimental design, we show how greedy optimization of a novel adaptive submodular criterion outperforms standard myopic techniques based on information gain and value of information. I will also discuss how adaptive submodularity can help to address problems in computational sustainability by presenting results on conservation planning for three rare species in the Pacific Northwest of the United States.
Sequential Decision Making in Uncertain Domains via Adaptive Submodularityread_more
HG G 19.1
19 November 2012
16:30-18:00 		Prof. Dr. Mark H.A. Davis
Imperial College London
Event Details

Optimization Seminar

Title Risk Management with Behavioural Factors
Speaker, Affiliation Prof. Dr. Mark H.A. Davis, Imperial College London
Date, Time 19 November 2012, 16:30-18:00
Location HG G 19.1
Abstract In several countries a major factor contributing to the current economic crisis was massive borrowing to fund investment projects on the basis of, in retrospect, grossly optimistic valuations. We develop an approach to project valuation and risk management in which 'behavioural' factors can be explicitly included. An appropriate framework is risk-neutral valuation based on the use of the numeraire portfolio -- the 'benchmark' approach advocated by Platen and Heath (2006). We start by discussing the ingredients of the problem: 'animal spirits', financial instability, market-consistent valuation, the numeraire portfolio and structural models of credit risk. We then study a project finance problem in which a bank lends money to an entrepreneur, collateralized by the value of the latter's investment project. This contains all the components of our approach in a simple setting and illustrates what steps are required. In a final section, we outline the computational and econometric problems that arise in real applications. This is joint work with Sebastien Lleo and Grzegorz Andruszkiewicz [http://ssrn.com/abstract=2038327]
Risk Management with Behavioural Factorsread_more
HG G 19.1
* 26 November 2012
17:15-18:00
Event Details

Optimization Seminar

Title No Seminar in favor of the Introductory Lecture of Mustafa H. Khammash: Mathematical Modelling, Analysis, and Computer Control of Gene Expression
Speaker, Affiliation
Date, Time 26 November 2012, 17:15-18:00
Location HG F 30
No Seminar in favor of the Introductory Lecture of Mustafa H. Khammash: Mathematical Modelling, Analysis, and Computer Control of Gene Expression
HG F 30
3 December 2012
16:30-18:00 		Prof. Dr. Alexander Martin
Friedrich-Alexander-Universität Erlangen-Nürnberg
Event Details

Optimization Seminar

Title May Mixed Integer Programming help to sove MINLPs? A Case Study from Gas Network Optimization
Speaker, Affiliation Prof. Dr. Alexander Martin, Friedrich-Alexander-Universität Erlangen-Nürnberg
Date, Time 3 December 2012, 16:30-18:00
Location HG G 19.1
Abstract Mixed integer nonlinear programs are currently in the focus of mathematical optimization. The sizes of problems that are actually solvable, however, are still low compared to those solvable when only linear inequalities are involved. We propose a discretization technique to approximate the nonlinear functions by linear ones in an iterative way to meet some given tolerance. This way we are able to apply MIP solution techniques. We will demonstrate the success of this approach on a particular class of applications resulting from gas network optimization.
May Mixed Integer Programming help to sove MINLPs? A Case Study from Gas Network Optimizationread_more
HG G 19.1

Notes: events marked with an asterisk (*) indicate that the time and/or location are different from the usual time and/or location and if you want you can subscribe to the iCal/ics Calender.

Organizers: Komei Fukuda, Bernd Gärtner, Diethard Klatte, John Lygeros, Manfred Morari, Karl Schmedders, Robert Weismantel

Archive: SS 22 SS 20 AS 19 SS 19 AS 18 SS 18 AS 17 SS 17 AS 16 SS 16 AS 15 SS 15 AS 14 SS 14 AS 13 SS 13 AS 12 SS 12 AS 11 SS 11 AS 10 SS 10 AS 09

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