B. Gärtner

Publications
Influence

Advances in Neural Information Processing Systems (NIPS)

H. Tyagi, B. Gärtner, Andreas Krause
Computer Science
13 December 2014

A Novel Type of Skeleton for Polygons

O. Aichholzer, F. Aurenhammer, David Alberts, B. Gärtner
Computer Science
1996

A new internal structure for simple polygons, the straight skeleton, is introduced and discussed. Expand

Understanding and using linear programming

B. Gärtner, J. Matousek
Computer Science, Mathematics
Universitext
2007

What Is It, and What For?.- Examples.- Integer Programming and LP Relaxation.- Theory of Linear Programming. Expand

A Novel Type of Skeleton for Polygons

O. Aichholzer, F. Aurenhammer, David Alberts, B. Gärtner
Computer Science
J. Univers. Comput. Sci.
1995

A new internal structure for simple polygons, the straight skeleton, is introduced. Expand

Fast and Robust Smallest Enclosing Balls

B. Gärtner
Computer Science
ESA
16 July 1999

I describe a C++ program for computing the smallest enclosing ball of a point set in d-dimensional space, using floating-point arithmetic only. Expand

Coresets for polytope distance

B. Gärtner, Martin Jaggi
Mathematics, Computer Science
SCG '09
8 June 2009

We prove asymptotically matching upper and lower bounds on the size of coresets, stating that µ-coresets of size (1+o(1)) E*/µ do always exist as µ-0, and that this is best possible. Expand

Straight skeletons of simple polygons

O. Aichholzer, David Alberts, F. Aurenhammer, B. Gärtner
Mathematics
1995

Fast Smallest-Enclosing-Ball Computation in High Dimensions

K. Fischer, B. Gärtner, M. Kutz
Computer Science, Mathematics
ESA
16 September 2003

We develop a simple combinatorial algorithm for computing the smallest enclosing ball of a set of points in high dimensional Euclidean space. Expand

A Simple Sampling Lemma: Analysis and Applications in Geometric Optimization

B. Gärtner, E. Welzl
Mathematics, Computer Science
Discret. Comput. Geom.
1 April 2001

We provide the first theoretical analysis of multiple pricing , a heuristic used in the simplex method for linear programming in order to reduce a large problem to few small ones. Expand

Optimization of Convex Functions with Random Pursuit

S. Stich, C. Müller, B. Gärtner
Mathematics, Computer Science
SIAM J. Optim.
1 November 2011

We consider unconstrained randomized optimization of convex objective functions. We analyze the Random Pursuit algorithm, which iteratively computes an approximate solution to the optimization… Expand

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