Solve programming problems from previous ACM Programming Contests (seehttp://acm.uva.es/problemset/); learn and use efficient programming methods and algorithms.

Presentation of recent publications in discrete mathematics; this term topics focus on the Combinatorial Nullstellensatz and its applications.

Presentation of recent publications in discrete mathematics; topics focus on extremal graph theory.

Presentation of recent publications in discrete mathematics; topics focus on property testing and sublinear algorithms.

Introduction: RAM machine, data structures; Algorithms: sorting, median, matrix multiplication, shortest paths, minimal spanning trees; Paradigms: divide & conquer, dynamic programming, greedy algorithms; Data Structures: search trees, dictionaries, priority queues; Complexity Theory: P and NP, NP-completeness, Cook's theorem, reductions; Outlook: optimization problems, approximation algorithms

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.

Introduction: RAM machine, data structures; Algorithms: sorting, median, matrix multiplication, shortest paths, minimal spanning trees; Paradigms: divide & conquer, dynamic programming, greedy algorithms; Data Structures: search trees, dictionaries, priority queues; Complexity Theory: P and NP, NP-completeness, Cook's theorem, reductions, cryptography and zero-knowledge proofs.

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).

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.

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).

The courses "Foundations of Computer Science" cover material that all students of computer science should know. The courses are self study courses and based on material which we assume that students know from their Bachelor program. The main aim of these courses is to ensure that all our Master students have a solid knowledge all over computer science and not just in their area of expertise.

The courses "Foundations of Computer Science" cover material that all students of computer science should know. The courses are self study courses and based on material which we assume that students know from their Bachelor program. The main aim of these courses is to ensure that all our Master students have a solid knowledge all over computer science and not just in their area of expertise.

The courses "Foundations of Computer Science" cover material that all students of computer science should know. The courses are self study courses and based on material which we assume that students know from their Bachelor program. The main aim of these courses is to ensure that all our Master students have a solid knowledge all over computer science and not just in their area of expertise.

The courses "Foundations of Computer Science" cover material that all students of computer science should know. The courses are self study courses and based on material which we assume that students know from their Bachelor program. The main aim of these courses is to ensure that all our Master students have a solid knowledge all over computer science and not just in their area of expertise.