Computational complexity studies the amount of resources (such as time, space, randomness, or communication) that are necessary to solve computational problems in various models of computation. Finding efficient algorithms for solving computational tasks is crucial for practical applications. Despite a long line of research, for many problems that arise in practice it is not known if they can be solved efficiently – in particular, in polynomial time. Beside questions about the existence of polynomial time algorithms for problems like Satisfiability or Factoring, where the best known algorithms run in exponential time, there is a huge class of practical problems where algorithms with polynomial running time (such as cubic or even quadratic time) are known, but it would be important to establish whether these running times are best possible, to what extent they can be improved, and whether parallel algorithms allow improvements of the runtime.

These fundamental questions motivate developments in various areas from algorithm design to circuit complexity, communication complexity and proof complexity. During this Dagstuhl Seminar, we plan to discuss some of the most exciting recent developments in those areas related to computational complexity.

The bulk of the seminar will be taken up by talks and discussions. The topics will depend on and be driven by the participants, who will share their current research interests in talks, open problem sessions, and smaller group research. The list of topics will include subjects such as new lower bounds on formula size and circuit size, complexity measures of Boolean functions, the algorithmic method for proving lower bounds, fixed parameter tractability and hardness magnification, communication complexity and lifting techniques, as well as proof complexity.

The seminar is a continuation of the series of Dagstuhl Seminars entitled “Computational Complexity of Discrete Problems”. It will build on the long experience and retain the features that have made the series strong in the past, while also capitalizing on new and exciting developments in the area such as the proof of the Sensitivity Conjecture, new lower bounds and results on matrix rigidity via the algorithmic method, and the remarkable success of lifting techniques in translating lower bounds from query complexity to communication complexity and from proof complexity to circuit complexity.

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Anna Gál, Meena Mahajan, Rahul Santhanam, and Till Tantau

• Eric Allender (Rutgers University - Piscataway, US) [dblp]
• Max Bannach (Universität zu Lübeck, DE) [dblp]
• Olaf Beyersdorff (Universität Jena, DE) [dblp]
• Harry Buhrman (CWI - Amsterdam, NL) [dblp]
• Igor Carboni Oliveira (University of Warwick - Coventry, GB) [dblp]
• Amit Chakrabarti (Dartmouth College - Hanover, US) [dblp]
• Sourav Chakraborty (Indian Statistical Institute - Kolkata, IN) [dblp]
• Arkadev Chattopadhyay (TIFR - Mumbai, IN) [dblp]
• Lijie Chen (MIT - Cambridge, US) [dblp]
• Anindya De (University of Pennsylvania - Philadelphia, US) [dblp]
• Yuval Filmus (Technion - Haifa, IL) [dblp]
• Lance Fortnow (Illinois Institute of Technology, US) [dblp]
• Anna Gál (University of Texas - Austin, US) [dblp]
• Alexander Golovnev (Georgetown University - Washington, DC, US) [dblp]
• Mika Göös (EPFL Lausanne, CH) [dblp]
• Rohit Gurjar (Indian Institute of Technology - Mumbai, IN) [dblp]
• Kristoffer Arnsfelt Hansen (Aarhus University, DK) [dblp]
• Prahladh Harsha (TIFR - Mumbai, IN) [dblp]
• Johan Hastad (KTH Royal Institute of Technology - Stockholm, SE) [dblp]
• Shuichi Hirahara (National Institute of Informatics - Tokyo, JP) [dblp]
• Rahul Ilango (MIT - Cambridge, US) [dblp]
• Stacey Jeffery (CWI - Amsterdam, NL) [dblp]
• Gillat Kol (Princeton University, US) [dblp]
• Swastik Kopparty (Rutgers University - Piscataway, US) [dblp]
• Michal Koucký (Charles University - Prague, CZ) [dblp]
• Alexander S. Kulikov (Steklov Institute - St. Petersburg, RU) [dblp]
• Sophie Laplante (University Paris Diderot, FR) [dblp]
• Nutan Limaye (Indian Institute of Technology - Mumbai, IN) [dblp]
• Meena Mahajan (Institute of Mathematical Sciences - Chennai, IN) [dblp]
• Ian Mertz (University of Toronto, CA)
• Jakob Nordström (University of Copenhagen, DK & Lund University, SE) [dblp]
• Ramamohan Paturi (University of California - San Diego, US) [dblp]
• Toniann Pitassi (University of Toronto, CA) [dblp]
• Pavel Pudlák (The Czech Academy of Sciences - Prague, CZ) [dblp]
• Oded Regev (New York University, US) [dblp]
• Rüdiger Reischuk (Universität zu Lübeck, DE) [dblp]
• Robert Robere (McGill University - Montreal, CA) [dblp]
• Michael E. Saks (Rutgers University - Piscataway, US) [dblp]
• Rahul Santhanam (University of Oxford, GB) [dblp]
• Shubhangi Saraf (Rutgers University - Piscataway, US) [dblp]
• Amit Sinhababu (Hochschule Aalen, DE)
• Srikanth Srinivasan (Aarhus University, DK) [dblp]
• Amnon Ta-Shma (Tel Aviv University, IL) [dblp]
• Li-Yang Tan (Stanford University, US) [dblp]
• Till Tantau (Universität zu Lübeck, DE) [dblp]
• Thomas Thierauf (Hochschule Aalen, DE) [dblp]
• Jacobo Torán (Universität Ulm, DE) [dblp]
• Virginia Vassilevska Williams (MIT - Cambridge, US) [dblp]
• Ben Lee Volk (University of Texas - Austin, US) [dblp]
• Omri Weinstein (Columbia University - New York, US) [dblp]
• Ryan Williams (MIT - Cambridge, US) [dblp]
• Amir Yehudayoff (Technion - Haifa, IL) [dblp]