Current doctoral students
- Fred Mayambala, 2012-
The topic of Fred's thesis work is the classic mean-variance portfolio optimization problem introduced by Markowitz in 1952, and he is in particular developing tailored solution methods for computationally challenging large scale instances. He is also approaching the cardinality-constrained version of the problem, which is even more challenging. The assistant supervisor is Elina Rönnberg. Licentiate thesis: Mean-Variance Portfolio Optimization: Eigendecomposition-Based Methods, 2015.
- Björn Morén, 2016-
Björn's research considers mathematical models and algorithms for optimization of brachytherapy treatment, which is a radiation therapy in which sources of ionizing radiation are inserted within a tumour. We consider anatomy-based inverse treatment planning for intensity modulated brachytherapy, and optimize with respect to dwell positions and dwell times. The objectives and constraints operate on dosimetric indices that are derived from dose-volume histograms and used in clinical evaluations. The research aims at developing semi-automatic decision support that can be used in clinical practice. The assistant supervisor is Åsa Carlsson Tedgren. Licentiate thesis: Mathematical Modelling of Dose Planning in High Dose-Rate Brachytherapy, 2019.
- Uledi Ngulo, 2016-
The thesis project aims at research in the field of large scale multi-level optimization models and methods, that is, optimization problems and methods that involve two or more coupled optimization problems; this structure appears frequently in applications. A commonly used approach for multi-level optimization is to decompose the overall problem into a sequence of single-level problems, that are coordinated through a feedback mechanism in order to yield overall optimality. The project includes both basic research on the development of decomposition techniques and research on multi-level applications. The assistant supervisor is Nils-Hassan Quttineh.
- Biressaw Chali Wolde, 2017-
Biressaw's thesis work aims at developing a global pricing principle for the simplex method for linear programming, with the goal of enhancing the performance of the method. The choice of entering variable in the simplex method is traditionally based on a pricing mechanism that takes only active constraints at the current extreme point into account. Hence, only local properties of the feasible set are considered. In global pricing, global properties of the feasible set are also considered. To achieve this, the traditional linear price function is extended with nonlinear terms. The research includes the derivation of a theoretical basis for the global pricing principle and a computational study of its application. The assistant supervisor is Michael Patriksson.
Page responsible: Torbjörn Larsson
Last updated: 2019-11-29