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Lukas Baumgartner

Publications and source records attributed to Lukas Baumgartner.

3 recordsLinked to original sources

Different Singular Limits in a Gene Regulatory Network with Multiple Small Parameters

We consider a planar ODE system from an important class of models for gene regulatory dynamics. The system depends singularly on the steepness parameters $0<\varepsilon_1,\varepsilon_2 \ll 1$ and converges to a piecewise-smooth system as these parameters tend to zero. Unlike previous studies, we do not assume that $\varepsilon_1 = \varepsilon_2$. As a consequence, the dynamics when $(\varepsilon_1, \varepsilon_2) \to (0,0)$ depends upon how the limit is taken. Using a preliminary blow-up in parameter space, we identify three distinct singular limits. We perform a two-parameter bifurcation analysis in each case, and apply multiple geometric blow-ups in variable and parameter space to determine the bifurcation structure and the associated global dynamics. Bogdanov-Takens bifurcations are revealed in two of three cases, and in one case in particular, a regularised visible-invisible two-fold singularity is shown to organise the unfolding of singular bifurcations in the vicinity of canards. Our results show that the qualitative dynamics and overall sensitivity of the system to parameter variation depends on the relative size of the steepness parameters. More generally, the analytical framework developed herein provides a systematic approach to singular perturbation problems with multiple independent small parameters that should apply well beyond gene regulatory network models.

math.DS

A Multi-Parameter Singular Perturbation Analysis of the Robertson Model

The Robertson model describing a chemical reaction involving three reactants is one of the classical examples of stiffness in ODEs. The stiffness is caused by the occurrence of three reaction rates $k_1,\,k_2$, and $k_3$, with largely differing orders of magnitude, acting as parameters. The model has been widely used as a numerical test problem. Surprisingly, no asymptotic analysis of this multiscale problem seems to exist. In this paper we provide a full asymptotic analysis of the Robertson model under the assumption $k_1, k_3 \ll k_2$. We rewrite the equations as a two-parameter singular perturbation problem in the rescaled small parameters $(\varepsilon_1,\varepsilon_2):=(k_1/k_2,k_3/k_2)$, which we then analyze using geometric singular perturbation theory (GSPT). To deal with the multi-parameter singular structure, we perform blow-ups in parameter- and variable space. We identify four distinct regimes in a neighbourhood of the singular limit $(\varepsilon_1,\varepsilon_2)= (0,0)$. Within these four regimes we use GSPT and additional blow-ups to analyze the dynamics and the structure of solutions. Our asymptotic results are in excellent qualitative and quantitative agreement with the numerics.

math.DS

On Solving the Oriented Two-Dimensional Bin Packing Problem under Free Guillotine Cutting: Exploiting the Power of Probabilistic Solution Construction

Two-dimensional bin packing problems are highly relevant combinatorial optimization problems. They find a large number of applications, for example, in the context of transportation or warehousing, and for the cutting of different materials such as glass, wood or metal. In this work we deal with the oriented two-dimensional bin packing problem under free guillotine cutting. In this specific problem a set of oriented rectangular items is given which must be packed into a minimum number of bins of equal size. The first algorithm proposed in this work is a randomized multi-start version of a constructive one-pass heuristic from the literature. Additionally we propose the use of this randomized one-pass heuristic within an evolutionary algorithm. The results of the two proposed algorithms are compared to the best approaches from the literature. In particular the evolutionary algorithm compares very favorably to current state-of-the-art approaches. The optimal solution for 4 previously unsolved instances could be found.

cs.AI