Departments Algorithms and Complexity Department 1: Algorithms and Complexity The department investigates a broad range of theoretical and practical aspects of modern algorithmics. We design new algorithms and [...] learning algorithms have many applications. Can we theoretically prove they are consistent and helpful? We focus on two paradigms: algorithmic stability and algorithms with predictions. Stable algorithms, which [...] Approximation Algorithms Most interesting optimization problems are NP-Hard. For such problems, unless P=NP, exact algorithms cannot be efficient. In the field of approximation algorithms, we take the reverse
Processing Algorithms & Complexity People Current Members Former Members Research Algorithmic Game Theory Clustering Distributed Computing Fine-Grained Complexity and Algorithm Design Graph Algorithms Learn [...] Learning-Augmented Algorithms Optimization Parameterized and Counting Algorithms and Complexity String Algorithms and Data Compression Offers Postdoc Application Teaching Winter 2023/24 Algorithms and Data Structures [...] Randomized Algorithms and Probabilistic Analysis of Algorithms Approximation Algorithms Ideen und Konzepte der Informatik Summer 2022 Winter 2021/22 Summer 2021 Winter 2020/21 Summer 2020 Winter 2019/20
Research Departments Algorithms and Complexity Teaching Teaching The members of the algorithms group of MPI-INF offer courses and thesis (bachelor, master, and PhD) topics in algorithmics. We offer the core [...] Course type Speaker / Tutor Randomized Algorithms and Probabilistic Analysis of Algorithms Advanced Course Philip Wellnitz , Tutor: Baris Can Esmer Approximation Algorithms Advanced Course Joachim Spoerhase [...] Karrenbauer Parameterized Algorithms Advanced Course Pranabendu Misra and Dániel Marx Reading Group Algorithms Seminar Bhaskar Ray Chaudhury and Karl Bringmann Sublinear Algorithms Advanced Course Karl Bringmann
learning algorithms have many applications. Can we theoretically prove they are consistent and helpful? We focus on two paradigms: algorithmic stability and algorithms with predictions. Stable algorithms, which [...] Approximation Algorithms Most interesting optimization problems are NP-Hard. For such problems, unless P=NP, exact algorithms cannot be efficient. In the field of approximation algorithms, we take the reverse [...] perspective: efficient algorithms cannot be exact. But if we naturally insist on efficient algorithms, how close can we get to an optimal solution? In this group we design efficient algorithms with provable guarantees
Research Departments Algorithms and Complexity Seminars Algorithms with Predictions Algorithms with Predictions Schedule Date Speaker Title November 5 Nicole Megow Non-Clairvoyant Scheduling with Predictions [...] computer science, which analyzes the performance of algorithms based on worst-case instances, recent work on “algorithms with predictions” has developed algorithms that are enhanced with machine-learned predictions [...] utilize results from the theory of online algorithms in order to develop a learning augmented algorithm that "combines" (i) a prediction-sensitive online algorithm that yields enhanced performance when these
Research Departments Algorithms and Complexity Teaching Winter 2018/19 Randomized and Approximation Algorithms Randomized and Approximation Algorithms Advanced Course, 2+2 Basic Information Given by: Antonios [...] relevant algorithmic problems are unfortunately not known to have deterministic efficient algorithms. More specifically, for several important problems, it is highly unlikely that an efficient algorithm exists [...] provably "close" in value to the optimal one. Randomized Algorithms, and Probabilistic Analysis of Algorithms: Often, allowing an algorithm to make random choices during its execution leads to significantly
Research Departments Algorithms and Complexity Teaching Randomized Algorithms and Probabilistic Analysis of Algorithms Randomized Algorithms and Probabilistic Analysis of Algorithms Advanced Course, 2+1 [...] sense to analyze algorithms under probabilistic input models. In this course, we will introduce you to the foundations of randomized algorithms and probabilistic analysis of algorithms. We will cover different [...] Theory of Polynomial-Time Problems Randomized Algorithms and Probabilistic Analysis of Algorithms Mathematik für Informatiker 2 Reading Group Algorithms State of the Art Linear Programming Theory
Analysis of Algorithms, Springer Verlag, 1991 Algorithms and Data Structures Approximation Algorithms Theory of Distributed Systems Ideen und Konzepte der Informatik Reading Group AlgorithmsAlgorithmic Lower [...] Research Departments Algorithms and Complexity Teaching Algorithms and Data Structures Algorithms and Data Structures Core Course, 4+2 Basic Information Lectures: 29.02. - 01.04.2016 Lecturers: Martin [...] union-find, etc.), graph algorithms (shortest path, minimum spanning tree, matching, flow, etc.), optimization techniques (divide-and-conquer, linear programming, approximation algorithms, etc.), analysis techniques
Approximation Algorithms " by V. Vazirani ( pdf ) " The Design of Approximation Algorithms " by David P. Williamson and David B. Shmoys ( pdf ) Algorithms and Data Structures Approximation Algorithms Theory of [...] Research Departments Algorithms and Complexity Teaching Approximation Algorithms Approximation Algorithms Advanced Course, 2+1 Basic Information Lectures: Tuesday 10:15-11:45, room 024 in the ground floor [...] leads to approximation algorithms which are algorithms that run fast and still give a guarantee on the quality of the solution for any input instance. The area of approximation algorithms is one of the core
Departments IMPRS, ALGO Algorithmic Game Theory Approximation Algorithms Fine-Grained Complexity and Algorithm Design Graph Algorithms Optimization Parameterized and Counting Algorithms and Complexity Robust [...] Departments Algorithms and Complexity Research Approximation Algorithms Approximation Algorithms Most interesting optimization problems are NP-Hard. For such problems, unless P=NP, exact algorithms cannot be [...] efficient. In the field of approximation algorithms, we take the reverse perspective: efficient algorithms cannot be exact. But if we naturally insist on efficient algorithms, how close can we get to an optimal
