Found 28 relevant results in 2.72s where lecturer="Emo Welzl"
Page 1 of 2
No description available.
Students learn how to solve algorithmic problems given by a textual description (understanding problem setting, finding appropriate modeling, choosing suitable algorithms, and implementing them). Knowledge of basic algorithms and data structures is assumed; more advanced material and usage of standard libraries for combinatorial algorithms are introduced in tutorials.
Algorithms and Probability
Algorithmen und Wahrscheinlichkeit
Es werden klassische Algorithmen aus verschiedenen Anwendungsbereichen vorgestellt. In die diskrete Wahrscheinlichkeitstheorie wird eingeführt und das Konzept randomisierter Algorithmen an verschiedenen Beispielen vorgestellt.
Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).
Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Network Flows, Minimum Cut, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).
Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Network Flows, Minimum Cut, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).
The course is concerned with approximate geometric methods for the analysis of large data sets represented by point clouds. Concrete topics areLow Distortion Embedding, Approximate Nearest Neighbor Search, Semi Definite Programming, Approximations and Nets, Approximate Smallest Enclosing Balls and Boxes, Directional Width, Support Vector Machines.
Approximation: Theory and Algorithms
Approximation: Theorie & Algorithmen
Introduction to the theory of approximation algorithms and complexity classes, examples include knapsack, bin packing, metric TSP, TSP in planar graphs, Euclidean TSP, Steiner trees; PCP-theorem, APX-reductions; LP relaxation.
Basics (CNF, resolution), extremal properties (probabilistic method, derandomization, Local Lemma, partial satisfaction), 2-SAT algorithms (random walk, implication graph), NP-completeness (Cook-Levin), cube (facial structure, Kraft inequality, Hamming balls, covering codes), SAT algorithms (satisfiability coding lemma, Paturi-Pudlák-Zane, Hamming ball search, Schöning), constraint satisfaction.
Discrete Mathematics
Diskrete Mathematik
Foundations of Discrete Mathematics; combinatorics (elementary counting), graph theory (paths, walks, euler circuits, matchings, trees, planar graphs), algebra (modular arithmetic, groups, fields), applications (network flows, cryptography, coding theory).
Study and presentation of research papers from the literature on "Boolean Satisfiability-Combinatorics and Algorithms".
Geometric structures are useful in many areas, and there is a need to understand their structural properties, and to work with them algorithmically. The lecture addresses theoretical foundations concerning geometric structures. Central objects of interest are triangulations. We study combinatorial (Does a certain object exist?) and algorithmic questions (Can we find a certain object efficiently?)
This seminar complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent.
The main topics of the event are the basic research directions of the last two decades in optimization. Several new mathematical methods emerged across various fields that at the moment seem to be matured to be efficiently applied in optimization.
The main topics of the event are the basic research directions of the last two decades in optimization. Several new mathematical methods emerged across various fields that at the moment seem to be matured to be efficiently applied in optimization.
Students present current or classical results from theoretical computer science.
Presentations of important papers in the area of Discrete Mathematics and Theoretical Computer Science by PhD students
This seminar is held once a year and complements the course ``Approximate Methods in Geometry''. Students of the seminar will present original research papers on approximate methods, most of them very recent. The seminar is a good preparation for a master thesis in the area. In the Spring semester, we offer a similar seminar geared towards topics around the course ``Computational Geometry".
Study and presentation of research papers from the literature on "Boolean Satisfiability-Combinatorics and Algorithms".
Presentation of recent publications in theoretical computer science, including results by diploma, masters and doctoral candidates.
Page 1 of 2