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Bernd Gärtner

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers
2 author rows

Possible papers

8

TCS Journal 2021 Journal Article

Majority rule cellular automata

  • Bernd Gärtner
  • Ahad N. Zehmakan

Consider a graph G = ( V, E ) and a random initial coloring where each vertex is black independently with probability p b, and white with probability p w = 1 − p b. In each step, all vertices change their current color synchronously to the most frequent color in their neighborhood and in case of a tie, a vertex keeps its current color. This model is called the majority model. If in case of a tie a vertex always selects black color, it is called the biased majority model. We are interested in the behavior of these two processes, especially when the underlying graph is a two-dimensional torus (cellular automaton with (biased) majority rule). In the present paper, as our main result we prove that both majority and biased majority cellular automata exhibit a threshold behavior with two phase transitions. More precisely, we prove for a two-dimensional torus T n, n, there are two threshold values 0 ≤ p 1, p 2 ≤ 1 such that p b ≪ p 1, p 1 ≪ p b ≪ p 2, and p 2 ≪ p b result in final complete occupancy by white, stable coexistence of both colors, and final complete occupancy by black, respectively in O ( n 2 ) number of steps. (For two functions f ( n ) and g ( n ), we shortly write f ( n ) ≪ g ( n ) instead of f ( n ) ∈ o ( g ( n ) ).) We finally argue that our proof techniques can be used to prove a similar threshold behavior for a larger class of models.

NeurIPS Conference 2014 Conference Paper

Efficient Sampling for Learning Sparse Additive Models in High Dimensions

  • Hemant Tyagi
  • Bernd Gärtner
  • Andreas Krause

We consider the problem of learning sparse additive models, i. e. , functions of the form: $f(\vecx) = \sum_{l \in S} \phi_{l}(x_l)$, $\vecx \in \matR^d$ from point queries of $f$. Here $S$ is an unknown subset of coordinate variables with $\abs{S} = k \ll d$. Assuming $\phi_l$'s to be smooth, we propose a set of points at which to sample $f$ and an efficient randomized algorithm that recovers a \textit{uniform approximation} to each unknown $\phi_l$. We provide a rigorous theoretical analysis of our scheme along with sample complexity bounds. Our algorithm utilizes recent results from compressive sensing theory along with a novel convex quadratic program for recovering robust uniform approximations to univariate functions, from point queries corrupted with arbitrary bounded noise. Lastly we theoretically analyze the impact of noise -- either arbitrary but bounded, or stochastic -- on the performance of our algorithm.

TCS Journal 2001 Journal Article

A new lower bound for the list update problem in the partial cost model

  • Christoph Ambühl
  • Bernd Gärtner
  • Bernhard von Stengel

The optimal competitive ratio for a randomized online list update algorithm is known to be at least 1. 5 and at most 1. 6, but the remaining gap is not yet closed. We present a new lower bound of 1. 50084 for the partial cost model. The construction is based on game trees with incomplete information, which seem to be generally useful for the competitive analysis of online algorithms.

FOCS Conference 1994 Conference Paper

Randomized Simplex Algorithms on Klee-Mintny Cubes

  • Bernd Gärtner
  • Günter M. Ziegler

We investigate the behavior of randomized simplex algorithms on special linear programs. For this, we develop combinatorial models for the Klee-Minty cubes (1972) and similar linear programs with exponential decreasing paths. The analysis of two most natural randomized pivot rules on the Klee-Minty cubes leads to (nearly) quadratic lower bounds for the complexity of linear programming with random pivots. Thus we disprove two bounds conjectured in the literature. At the same lime, we establish quadratic upper bounds for random pivots on the linear programs under investigation. This motivates the question whether some randomized pivot rules possibly have quadratic worst-case behavior on general linear programs. >

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