Prerequisites
Basic knowledge of probability at the level:
S.M. Ross, Introduction to probability models, 9th edition, Academic Press, 2007 (chapters 13).
Aim of the course
To provide insight in the theory of queueing models. The following subjects will be treated:
 Fundamental queueing relations (Little's law, PASTA property)
 Markovian queues (M/M/1 queue, M/M/c queue, M/E_r/1 queue)
 M/G/1 queue and G/M/1 queue
 Mean value technique
 Priority queues
 Variations of the M/G/1 queue
 Insensitive queues (M/G/c/c queue and M/G/infinity queue)
Lecturer
Jacques Resing (TU/e) and Sindo NunezQueija (UvA)
 Docent: Sindo Nunez Queija
 Docent: Jacques Resing
Aim of the course
To provide insight in the theory of linear optimization and in the design of advanced practical methods for solving (integer) linear optimization problems.
Part 1: Basic theory and algorithms of linear optimization:  Linear optimization  polyhedra and polytopes  the simplex algorithm  duality  linear inequalities and Farkas' lemma – network flow problems – ellipsoid method and separation
Part 2: Advanced linear optimization methods  the revised simplex method and column generation  DantzigWolfe and Benders' decomposition  integer programming formulations and solution methods valid inequalities branchandcut
Prerequisites

Parts on Linear Programming in any book Introduction to Operations Research.

A course on Linear Algebra based on e.g. David C. Lay, Stephen R. Lay and Judi J. McDonald, Linear Algebra and its Applications . For mathematics students their knowledge of Linear Algebra should suffice to match up rather easily.

The course is organized by the Dutch Network for the Mathematics of Operations Research and as such meant, among others for Master students in Operations Research.
Lecture notes/Literature
The course is covered by the book `Introduction to Linear Optimization', Bertsimas and Tsitsiklis, Athena Scientific 1997, ISBN 1886529191. According to some students the book cannot be ordered through ball.com or similar companies. It can be ordered directly from the publisher Dynamic Ideas. Since the past has taught that many students do not have the book yet at the start of the course, I make a copy available of the first five chapters (see the section on Lecture Notes/Literature a bit further).
Next to the book, we make as much as possible lecture notes available that you find in the week descriptions. There, but also in the lecture notes, you also find the Exercises for the week. The notes you see there now are those of last year and may be subject to small changes.
For a more basic course on linear programming I refer to books titled Introduction to Operations Research , e.g. by Hillier and Lieberman, or by Taha. Another very good book on linear programming is written by Chvatal. Everything you ever wish to know about the Theory of Linear and Integer Programming is found in the more advanced book with that title by Lex Schrijver.
Lecturers
L. Stougie (VU/CWI)
 Docent: Christopher Hojny
 Docent: Leen Stougie
Introduction
The term scheduling represents the assignment of resources over time to perform tasks, jobs or activities. Feasible schedules are compared with respect to a given optimality criterion. Mostly, the optimization problem is combinatorial and very complex. From a computational point of view, many of these problems are hard (NPhard). In this course, an overview on the most classical scheduling models is given and exact as well as some heuristic solution methods are discussed for these models.
In detail, the following issues are treated:
 Classification of scheduling models
 Singlemachine models
 Parallelmachines models
 Open shop, flow shop and job shop models
Aim of the course
In this course, students will learn techniques for a broad variety of scheduling problems. In particular, it is expected that after this course students will be able to construct mathematical models for the basic problems, classify them, address the questions on computational complexity of the problems, and apply standard algorithmic techniques to solve the problems.
Prerequisites
Basic knowledge (bachelor level) of analysis and linear algebra. Linear programming (modelling, not necessarily solving, see e.g. Chapter 1 of Linear Programming: Foundations and Extensions by Robert J. Vanderbei) and dynamic programming (see e.g. Chapter 5 of Integer Programming by Laurence A. Wolsey).
Lecturers
Theresia van Essen (Delft University of Technology)
Johann Hurink (University of Twente)
Video Recordings
Video recordings are available on Vimeo: https://vimeo.com/showcase/6774759
Password: a4ki
 Docent: Johann Hurink
 Docent: Theresia van Essen
Prerequisites
 Linear algebra (eigenvectors, eigenvalues, matrix algebra, Gershgorin's circle theorem, inner products, projections)
 Calculus (differentiation, integration, integration over lines, surfaces and domains, integral theorems (Gauss, Green))
 Partial differential equations (definition, heat, Laplace, Poisson, wave equation))
 Introductory numerical analysis (numerical time integration, interpolation, finite differences, quadrature, approximation methods for nonlinear equations)
Aim of the course
After completion of the course, the particpant will be able to construct and to use finiteelement methods to solve partial differential equations. Furthermore, the student will be able to assess the quality of the obtained numerical approximations.
The course aims at learning how to apply and construct finiteelement methods to various kinds of partial differential equations. The emphasis will be on the application and implementation of the finiteelement methods. The finiteelement formalisms due to Ritz and Galerkin will be treated. The course will include linear, quadratic, bilinear Lagrangian elements for timeindependent and time dependent problems. Next to Galerkin frameworks, PetrovGalerkin frameworks will be considered for the treatment of convectiondominated cases. Theoretical issues will be assessed in terms of convergence and error analysis, although this will not be the main focus of the course. Several lab assignments will be helpful in gaining understanding in the development of finiteelement methods.
After completion of the course, the participant will be able to construct and to use finiteelement methods to solve partial differential equations. Furthermore, the student will be able to assess the quality of the obtained numerical approximations.
Lecturers
Fred Vermolen (TUD), Jaap van der Vegt (UT)
 Docent: Hanne Kekkonen
 Docent: Paulo Serra
Aim of the course
This course is about inverse problems in imaging. The mathematical reconstruction and processing of images is of fundamental importance in state of the art applications in health and geosciences, e.g. in medical tomography, in highresolution microscopy or in geophysical inversion. In many cases, underlying inverse problems can be formulated and solved using variational methods and partial differential equations. This course offers a theoretical as well as an applied insight into inverse problems and variational methods for mathematical imaging. It addresses reconstruction problems of different imaging modalities (e.g. CT or PET) in biomedicine and geophysics. The course covers the full chain of solving inverse problems in imaging, namely
Problem identification → Modeling and discretization → Analysis → Numerical optimization
Where variational principles, regularization theory and numerical optimization (scientific computing) form the underlying joint core. The course connects and extends upon the main concepts of basic courses on differential equations and numerics. The main learning goal for the students is to model, analyze and use stateoftheart variational methods, PDEs and optimization techniques to solve challenging inverse problems in imaging. Upon completing this course, students achieved the following learning goals:
 Problem identification: Identification of imaging problems as mathematical inverse operator problems (e.g. integral equations, dynamical systems);
 Modeling and discretization: Problem formulation arising in applications using the language of nonlinear variational methods and partial differential equations; use Bayesian modeling to take data and model uncertainty into account; continuous versus discrete modeling;
 Analysis: Understanding the main concepts of nonlinear regularization theory in an analogous way in PDEs as well as in variational methods, and how it influences existence (duality, weak topologies, Theorem of BanachAlaoglu) and uniqueness results;
 Numerical optimization: Formulating optimality conditions (variations) for constrained convex variational methods (saddle point problems) and to solve them via primaldual methods or discretized higherorder methods.
At the end of the course participants will be able to tackle inverse problems for imaging in biomedicine or geophysics with a new repertoire of stateoftheart mathematical tools.
Prerequisites
The course is aimed at Master and starting PhD students in Mathematics (and Technical Medicine) at the comprehensive as well as the technical universities. Solid knowledge of linear algebra and calculus are essential. Furthermore, knowledge of differential equations and numerics is desirable. All at the Bachelor level.
Course material
We will use (digital) lectures notes which will be made available at the start of the course.
Lecturers
Christoph Brune (Mathematics, University of Twente) c.brune@utwente.nl
Tristan van Leeuwen (Mathematics, University of Utrecht) t.vanleeuwen@uu.nl
 Docent: Christoph Brune
 Docent: Tristan van Leeuwen