Departement Mathematik

Optimization seminar

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Herbstsemester 2009

Datum / Zeit Referent:in Titel Ort
21. September 2009
16:30-18:00 		Prof. Dr. Arkadi Nemirovski
Georgia Tech
Details

Optimization Seminar

Titel Lectures on Robust Convex Optimization
Referent:in, Affiliation Prof. Dr. Arkadi Nemirovski, Georgia Tech
Datum, Zeit 21. September 2009, 16:30-18:00
Ort HG G 19.1
Unterlagen course descriptionfile_download
Lectures on Robust Convex Optimizationread_more
HG G 19.1
28. September 2009
16:30-18:00 		Prof. Dr. Arkadi Nemirovski
Georgia Tech
Details

Optimization Seminar

Titel Lectures on Robust Convex Optimization
Referent:in, Affiliation Prof. Dr. Arkadi Nemirovski, Georgia Tech
Datum, Zeit 28. September 2009, 16:30-18:00
Ort HG G 19.1
Unterlagen course descriptionfile_download
Lectures on Robust Convex Optimizationread_more
HG G 19.1
5. Oktober 2009
16:30-18:00 		Prof. Dr. Arkadi Nemirovski
Georgia Tech
Details

Optimization Seminar

Titel Lectures on Robust Convex Optimization
Referent:in, Affiliation Prof. Dr. Arkadi Nemirovski, Georgia Tech
Datum, Zeit 5. Oktober 2009, 16:30-18:00
Ort HG G 19.1
Unterlagen course descriptionfile_download
Lectures on Robust Convex Optimizationread_more
HG G 19.1
12. Oktober 2009
16:30-18:00 		Prof. Dr. Arkadi Nemirovski
Georgia Tech
Details

Optimization Seminar

Titel Lectures on Robust Convex Optimization
Referent:in, Affiliation Prof. Dr. Arkadi Nemirovski, Georgia Tech
Datum, Zeit 12. Oktober 2009, 16:30-18:00
Ort HG G 19.1
Unterlagen course descriptionfile_download
Lectures on Robust Convex Optimizationread_more
HG G 19.1
19. Oktober 2009
16:30-18:00 		Prof. Dr. Arkadi Nemirovski
Georgia Tech
Details

Optimization Seminar

Titel Lectures on Robust Convex Optimization
Referent:in, Affiliation Prof. Dr. Arkadi Nemirovski, Georgia Tech
Datum, Zeit 19. Oktober 2009, 16:30-18:00
Ort HG G 19.1
Unterlagen course descriptionfile_download
Lectures on Robust Convex Optimizationread_more
HG G 19.1
26. Oktober 2009
16:30-18:00 		Dr. Laura Galli
University of Bologna
Details

Optimization Seminar

Titel Combinatorial and Robust Optimisation Techniques for the Train Routing Problem
Referent:in, Affiliation Dr. Laura Galli, University of Bologna
Datum, Zeit 26. Oktober 2009, 16:30-18:00
Ort HG G 19.1
Abstract Several planning problems arising in the railways have been tackled using Operational Research (OR) techniques. An important problem is the 'Train Routing Problem', which is the problem of assigning inbound and outbound routes to trains in a given railway station, for a fixed (or nearly fixed) timetable. This optimisation step follows the timetabling phase and defines a detailed routing of the trains through the station according to its layout. The train routing task can be remarkably difficult to solve, especially when the railway station has a complex topology, as is the case for many main European stations. This talk will introduce a very general version of train routing, inspired by the Italian case, and will show how OR techniques were used to find solutions that were significantly better than those obtained by the Italian Infrastructure Manager. The talk will also introduce a possible robust counterpart for the problem, applicable to scenarios with right-hand-side uncertainty.
Combinatorial and Robust Optimisation Techniques for the Train Routing Problemread_more
HG G 19.1
* 2. November 2009
17:00-17:45 		Prof. Dr. Karl Schmedders
Institute for Operations Research, University of Zurich
Details

Optimization Seminar

Titel Inaugural Lecture: (Über)Regulierung von Märkten durch Preisobergrenzen
Referent:in, Affiliation Prof. Dr. Karl Schmedders, Institute for Operations Research, University of Zurich
Datum, Zeit 2. November 2009, 17:00-17:45
Ort KOL G 201
Inaugural Lecture: (Über)Regulierung von Märkten durch Preisobergrenzen
KOL G 201
9. November 2009
16:30-18:00 		Prof. Dr. Marco Campi
University of Brescia
Details

Optimization Seminar

Titel The Scenario Technology: A Viable Approach to Robust Optimization
Referent:in, Affiliation Prof. Dr. Marco Campi, University of Brescia
Datum, Zeit 9. November 2009, 16:30-18:00
Ort HG G 19.1
Abstract Many robust optimization problems cannot be solved due to computational intractability. In this talk, a new probabilistic solution framework is introduced for robust convex optimization problems. This includes for instance robust linear and quadratic programs, and the wide class of optimization problems representable by means of parameter-dependent linear matrix inequalities (LMIs). By appropriate sampling of the constraints, one obtains a convex optimization problem (the scenario problem) that can be solved by means of standard optimization solvers. The solution of the scenario problem bears probabilistic robustness properties that are chosen by the user. A rich family of optimization problems which are in general hard to solve in a deterministically robust sense is therefore amenable to polynomial-time solution if robustness is intended in the proposed probabilistic sense. This opens-up new avenues for addressing problems in data compression, finance, control, and many more fields.
The Scenario Technology: A Viable Approach to Robust Optimizationread_more
HG G 19.1
16. November 2009
16:30-18:00 		Prof. Dr. Robert Freund
MIT
Details

Optimization Seminar

Titel Primal-Dual Geometry of Level Sets in Linear and Conic Optimization, and their Explanatory Value in the Practical Efficiency of Interior-Point Algorithms
Referent:in, Affiliation Prof. Dr. Robert Freund, MIT
Datum, Zeit 16. November 2009, 16:30-18:00
Ort HG G 19.1
Unterlagen abstractfile_download
slidesfile_download
Primal-Dual Geometry of Level Sets in Linear and Conic Optimization, and their Explanatory Value in the Practical Efficiency of Interior-Point Algorithmsread_more
HG G 19.1
23. November 2009
16:30-18:00 		Prof. Dr. Jie Sun
National University of Singapore
Details

Optimization Seminar

Titel On Methods for Solving Nonlinear Semidefinite Optimization Problems
Referent:in, Affiliation Prof. Dr. Jie Sun, National University of Singapore
Datum, Zeit 23. November 2009, 16:30-18:00
Ort HG G 19.1
Abstract Abstract. The nonlinear semidefinite optimization problem arises from applications in system control, structural design, financial management, and other fields. However, much work is yet to be done to effectively solve this problem. We introduce some new theoretical and algorithmic development in this field. In particular, we discuss first and second-order algorithms that appear to be promising, which include the alternating direction method, the augmented Lagrangian method, and the smoothing Newton method. Convergence theorems are presented and preliminary numerical results are reported.
On Methods for Solving Nonlinear Semidefinite Optimization Problemsread_more
HG G 19.1
30. November 2009
16:30-18:00 		Prof. Dr. Detlof von Winterfeldt
IIASA
Details

Optimization Seminar

Titel Research for a World in Transition
Referent:in, Affiliation Prof. Dr. Detlof von Winterfeldt, IIASA
Datum, Zeit 30. November 2009, 16:30-18:00
Ort HG G 19.1
Unterlagen abstractfile_download
Research for a World in Transitionread_more
HG G 19.1
7. Dezember 2009
16:30-18:00 		Prof. Dr. John Lygeros
ETH Zürich
Details

Optimization Seminar

Titel Stochastic Model Predictive Control: Tractability and constraint satisfaction
Referent:in, Affiliation Prof. Dr. John Lygeros, ETH Zürich
Datum, Zeit 7. Dezember 2009, 16:30-18:00
Ort HG G 19.1
Abstract Exploiting advances in optimization, especially convex and multi-parametric optimization, model predictive control (MPC) for deterministic systems has matured into a powerful methodology with a wide range of applications. Recent activity in robust optimization has also enabled the formulation and solution of robust MPC problems for systems subject to various kinds of worst case uncertainty. For systems subject to stochastic uncertainty, however, the formulation and solution of MPC problems still poses fundamental conceptual challenges. Optimization over open loop controls, for example, tends to lead to excessively conservative solutions, so optimization over an appropriate class of feedback policies is often necessary. As in the case of robust MPC, the selection of policies one considers is crucial and represents a trade-off between the tractability of the optimization problem and the optimality of the solution. Moreover, in the presence of stochastic disturbances hard state and input constraints need to be re-interpreted as chance constraints, or integrated chance constraints, which may be violated with a certain tolerance. This interpretation, however, makes it difficult to enforce hard input constraints dictated by the capabilities of the system and the actuators, especially if one considers desirable classes of feedback policies such as affine policies. And what guarantees can one provide in the infinite horizon case, given that the system evolution is obtained by solving an infinite sequence of finite horizon problems each of which may violate its constraints with a finite probability? This talk will outline these challenges and propose solutions for some. The resulting stochastic MPC methods will be illustrated on benchmark problems and compared with alternatives.
Stochastic Model Predictive Control: Tractability and constraint satisfactionread_more
HG G 19.1
14. Dezember 2009
16:30-18:00 		Dr. Monaldo Mastrolilli
IDSIA
Details

Optimization Seminar

Titel Precedence Constrained Scheduling: the solution of some open problems with connections to dimension theory of partial orders
Referent:in, Affiliation Dr. Monaldo Mastrolilli, IDSIA
Datum, Zeit 14. Dezember 2009, 16:30-18:00
Ort HG G 19.1
Abstract In this talk I consider two classical precedence constrained scheduling problems. The first part of the seminar is devoted to the single machine scheduling problem to minimize the weighted sum of completion times. I start presenting the solution of a conjecture by Correa & Schulz that implies that the considered scheduling problem is a special case of the minimum weighted vertex cover. It turns out that the vertex cover graph associated with the scheduling problem is exactly the graph of incomparable pairs defined in dimension theory of partial orders. Exploiting this relationship allows us to present a framework for obtaining $(2-2/f)$-approximation algorithms provided that the set of precedence constraints has fractional dimension~$f$. This approach yields the best known approximation ratios for all previously considered classes of precedence constraints we are aware of. The second part of the seminar is devoted to the job shop scheduling problem. Understanding the approximability of the job shop problem is considered one of the most prominent open problems in scheduling theory. Even though the best approximation algorithms have worse than logarithmic performance guarantee, the only known inapproximability result says that it is NP-hard to approximate acyclic job shops within a factor less than~$5/4$. We solve this open problem by presenting almost tight inapproximability results for acyclic job shops and for the generalized version of flow shop.
Precedence Constrained Scheduling: the solution of some open problems with connections to dimension theory of partial ordersread_more
HG G 19.1

Hinweise: rot markierte Ereignisse sind wichtig, mit einem Stern gekennzeichnete Ereignisse (*) zeigen an, dass die Zeit und/oder der Ort von der üblichen Zeit und/oder dem üblichen Ort abweichen und wenn Sie möchten, können Sie den iCal/ics-Kalender abonnieren.

Organisatoren:innen: Komei Fukuda, Bernd Gärtner, Diethard Klatte, Hans-Jakob Lüthi, John Lygeros, János Mayer, Manfred Morari

Archiv: FS 22 FS 20 HS 19 FS 19 HS 18 FS 18 HS 17 FS 17 HS 16 FS 16 HS 15 FS 15 HS 14 FS 14 HS 13 FS 13 HS 12 FS 12 HS 11 FS 11 HS 10 FS 10 HS 09

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