Parameterized Complexity News - Wikidot

Parameterized Complexity News Newsletter of the Parameterized Complexity Community

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Vol 13, No 2. June 2017 ISSN 2203–109X

Welcome Co-editors Valia Mitsou (LIRIS, CNRS & Univ Lyon) [email protected] and Frances Rosamond (Univ Bergen) [email protected]. Congratulations to all for many awards, graduations, and wonderful research. This issue features articles on lossy kernelisation by Fahad Panolan and FPT SAT encodings by Ronald de Haan. You can also find announcements for many exciting events.

New Research Center in Copenhagen Congratulations to Mikkel Thorup, Stephen Alstrup, Rasmus Pagh and Thore Husfeldt, who have attracted 40 million Danish kroner (5 million EUR) for establishing a basic research center: Basic Algorithms Research Copenhagen (BARC), with the explicit goal of attracting international researchers to Copenhagen for basic research. The center is expected to start during the Fall of 2017. See here and here.

Andreas Bj¨ orklund and Thore Husfeldt – SRC Congratulations to Andreas Bj¨ orklund and Thore Husfeldt. They have received a grant of 3.7 million Swedish kroner (370,000 EUR) from the Swedish Research Council (2017-2020) to continue their investigation into “algebraic graph algorithms”; this continues a line of work that led to Andreas’ Nerode Prize in 2016.

Contents of this issue: Welcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 New Research Center in Copenhagen . . . . . . . . . . . . 1 Andreas Bjrklund and Thore Husfeldt – SRC . . . . 1 Klaus Jansen – DFG Award . . . . . . . . . . . . . . . . . . . . . 1 George Mertzios – EPSRC Award . . . . . . . . . . . . . . . 1 Best Paper Award – STOC’17 . . . . . . . . . . . . . . . . . . . 2 Best Paper Award – ICALP’17 Track A . . . . . . . . . 2 Best Paper Award – CIAC’17 . . . . . . . . . . . . . . . . . . . 2 Best Student Paper Award – ITCS’17 . . . . . . . . . . . 2 Celebrate IPEC 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Nerode Prize Winners, Laudation, Tutorial . . . . . . 2 PACE Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 PC Summer School at IPEC/ALGO . . . . . . . . . . . . . 3 Lossy Kernelization, Fahad Panolan . . . . . . . . . . . . . 3 FPT SAT Encodings, Ronald de Haan . . . . . . . . . . . 5

Figure 1: Rasmus Pagh, Thore Husfeldt, Mikkel Thorup and Stephen Alstrup are establishing a new basic algorithms research center in Copenhagen

Klaus Jansen – DFG Award Congratulations to Klaus Jansen, who has received a DFG award (totaling 500.000 Euros) for “Structural results and their application in scheduling and packing problems”. The main focus of this project is to find structural results for integer linear programs (ILPs). Klaus has openings for a PhD and a postdoc position for three years. Contact [email protected] and see position description on the Jobs Page of www.fpt.wikidot.com.

George Mertzios – EPSRC Award Congratulations to George Mertzios (Durham University) who has received an EPSRC Award (the UK research funding body) for “Algorithmic Aspects of Temporal Graphs”. The project topic is algorithms and complexity for problems in temporal (i.e. dynamically changing) graphs, and there is opportunity for a postdoc. Con-

Grant opportunity Vienna . . . . . . . . . . . . . . . . . . . . . . . 6 JOBS Page on www.fpt.wikidot.com . . . . . . . . . . . . . 6 CS Theory Events site. . . . . . . . . . . . . . . . . . . . . . . . . . .6 June–August in Australia . . . . . . . . . . . . . . . . . . . . . . . 6 June–Birthday of Michael Fellows . . . . . . . . . . . . . . . 7 June–FAW’17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 June–Dagstuhl Voting . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 July–IWOCA’17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 August–COCOON’17 . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Sept–IPEC’17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Sept–3rd PC Summer School . . . . . . . . . . . . . . . . . . . . 7 Workshop on Games and Graphs . . . . . . . . . . . . . . . . 7 Dec–Tel Aviv, Israel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Moving Around . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Congratulations New PhDs . . . . . . . . . . . . . . . . . . . . . . 8

Parameterized Complexity News

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tact George Mertzios ([email protected]; and exact computations for real-world applications and see also here.) algorithmic engineering are especially encouraged.

Best Paper Award – STOC’17 Congratulations to Cristian Calude, Sanjay Jain, Bakhadyr Khoussainov, Wei Li, and Frank Stephan for Best Paper Award at STOC 2017 for their paper Deciding Parity Games in Quasipolynomial Time. See www.comp.nus.edu.sg/∼fstephan/publications.html, publication number 181. See also the article in the Parameterized Complexity Newsletter [Vol 12 (2) Nov 2016].

Best Paper Award – ICALP’17 Track A Congratulations to Andreas Bj¨ orklund, Petteri Kaski and Ioannis Koutis for the Best Paper Award at ICALP Track A for their paper Directed Hamiltonicity and Out-Branchings via Generalized Laplacians. This is the second time Andreas will receive the ICALP Track A Best Paper Award. Andreas and Thore Hustfeldt received it in 2015.

(a) Naomi Nishimura

(b) Daniel Lokshtanov

Figure 2: IPEC 2017 co-chairs Important information

IPEC 2017 will be part of ALGO 2017, which also hosts ESA 2017 and a number of more specialized conferences and workshops. ALGO 2017 will take place September 4-8, 2017, Vienna, Austria. Abstract Submission: June 25, 2017 Paper Submission: June 28, 2017 Best Paper Award – CIAC’17 Notification of acceptance: July 25, 2017 Symposium: September 6-8, 2017 The best paper award for CIAC’17 was shared by two Register on the ALGO 2017 website (link). papers: Improved Lower Bounds for Graph Embedding Problems by Hans Bodlaender and Tom van der Zanden and Assessing the Computational Complexity of Best Paper Awards Multi-Layer Subgraph Detection by Robert Bredereck, Excellent Student Paper Award guidelines: at most Christian Komusiewicz, Stefan Kratsch, Hendrik one author has received a PhD degree before the paper Molter, Rolf Niedermeier and Manuel Sorge. Con- submission deadline. The students’ contributions must be gratulations to all! substantial, and a student must give the presentation at

Best Student Paper Award – ITCS’17

the conference. IMPORTANT Please identify if you are submitting a Student Paper. There will also be a Best Paper Award (not necessarily student).

Congratulations to Amir Abboud and Arturs Backurs for the Best Student Paper Award at ITCS’17 for PACE Challenge their paper Towards Hardness of Approximation for Polynomial Time Problems. The goal of the Parameterized Algorithms and Computational Experiments Challenge (PACE) is to investigate the applicability of algorithmic ideas studied and developed in the subfields of multivariate, fine-grained, Celebrate IPEC 2017 parameterized, or fixed-parameter tractable algorithms. The 12th International Symposium on Parameter- See https://pacechallenge.wordpress.com/pace-2017. We ized and Exact Computation (IPEC 2017) covers re- look forward to seeing all the participants at the Award search in all aspects of parameterized and exact algo- Ceremony at IPEC. rithms and complexity. Papers presenting original rePACE is happy to announce cooperation with OPsearch in the area are sought, including but not limited TIL.io to handle the submission process and the evaluato: new techniques for the design and analysis of param- tion of submissions. OPTIL.io is a website for organizeterized and exact algorithms, fixed-parameter tractabil- ing programming challenges for optimization problems, ity results, parameterized complexity theory, relationship created and maintained by the Institute of Computing between parameterized complexity and traditional com- Science of Poznan University of Technology. plexity classifications, applications of parameterized and PACE Program Committee: Track A (TREE exact computation, and implementation issues of param- WIDTH): Holger Dell (Saarland University and Cluseterized and exact algorithms. Studies on parameterized ter of Excellence (MMCI)). Track B (MIN FILL IN):

Parameterized Complexity News

Christian Komusiewicz (Chair) (Friedrich-SchillerUniversity Jena), Nimrod Talmon (Weizmann Institute of Science) Mathias Weller (Lab Informatics, Robotics, and Microelectronics of Montpellier (LIRMM)). PC Summer School at IPEC/ALGO The 3rd Parameterized Complexity Summer School will be held in Vienna, Austria, from 1 to 3 Sept 2017 (Friday-Sunday), co-located with ALGO 2017. See https://algo2017.ac.tuwien.ac.at/pcss/

Lossy Kernelization by Fahad Panolan, University of Bergen, Norway. Kernelization is an active and vibrant subfield of parameterized complexity which also has lower bound machinery to show that a problem does not admit a polynomial kernel, or a kernel of a specific size. Despite the success of kernelization, the basic definition has an important drawback: it does not combine well with approximation algorithms or with heuristics. This is a serious problem since, after all, the ultimate goal of parameterized algorithms, is to eventually solve the given input instance. In practice, even after applying kernelization, the reduced instance may not be small enough to be solved to optimality within a reasonable time bound. In these cases one gives up on optimality and resorts to approximation algorithms or heuristics instead. Thus it is crucial that the solution obtained by an approximation algorithm or heuristic when run on the reduced instance provides a good solution to the original instance, or at least some meaningful information about the original instance. By the current definition of kernels, the only thing guaranteed is that the reduced instance (I 0 , k 0 ) of a kernelization algorithm is a yes instance if and only if the original instance (I, k) is. If we have an α-approximate solution to (I 0 , k 0 ) there is no guarantee that we will be able to get an α-approximate solution to (I, k), or even able to get any feasible solution to (I, k). In [11], the authors build mathematical framework for analysing the performance of approximate kernelization, such that the framework combines well with approximation algorithms and heuristics. Moreover, a lower bound machinery for approximate kernelization has also been proposed based on the tools and notions from both kernelization and approximation algorithms. The main reason that the existing notion of kernelization does not combine well with approximation algorithms is that the definition of a kernel is deeply rooted in decision problems. The starting point of the new framework is an extension of kernelization to optimization problems. This allows us to define α-approximate kernels. Loosely speaking an (α)-approximate kernel of size g(k) is a polynomial time algorithm that given an instance (I, k) outputs an instance (I 0 , k 0 ) such that |I 0 | + k 0 ≤ g(k) and any c-approximate solution s0 to the instance (I 0 , k 0 ) can

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be turned in polynomial time into a (c · α)-approximate solution s to the original instance (I, k). One can show that the notion of (α)-approximate kernel is robust, versatile and natural. Robust. The key notions behave consistently with related notions from parameterized complexity, kernelization, approximation and FPT-approximation algorithms. For instance, a problem admits an α-approximate kernel if and only if it is FPT-α-approximable, mirroring the equivalence between FPT and kernelization. Versatile. The new framework can be deployed to measure the efficiency of pre-processing heuristics both in terms of the value of the optimum solution, and in terms of structural properties of the input instance that do not necessarily have any relation to the value of the optimum. Natural. There are several examples in the literature where approximate kernelization has already been used implicitly to design approximation algorithms and FPT-approximation algorithms. In particular, the best known approximation algorithm for Steiner Tree [1], and FPT-approximation for Partial Vertex Cover [2] and for Minimal Linear Arrangement parameterized by the vertex cover number [3] can be re-interpreted as running an approximate kernelization first and then running an FPT-approximation algorithm on the preprocessed instance. A common feature of the above examples of α-approximate kernels is that they beat both the known lower bounds on kernel size of traditional kernels and the lower bounds on approximation ratios of approximation algorithms. The observation that a lossy pre-processing can simultaneously achieve a better size bound than normal kernelization algorithms as well as a better approximation factor than the ratio of the best approximation algorithms is not new. In particular, motivated by this observation Fellows et al. [4] initiated the study of lossy kernelization. They proposed a definition of lossy kernelization called α-fidelity kernels. Essentially, an α-fidelity kernel is a polynomial time pre-processing procedure such that an optimal solution to the reduced instance translates to an α-approximate solution to the original, but nothing about the solution of the original instance can be deduced from an approximate solution of the reduced instance. It is important to note that even though the definition of α-approximate kernels crucially differs from the definition of α-fidelity kernels [4], it seems that most of the pre-processing algorithms that establish the existence of α-approximate kernels can be used to establish the existence of α-fidelity kernels and vice versa. In particular, all of the α-fidelity kernel results of Fellows et al. [4] can be translated to α-approximate kernels. In approximation algorithms, the best one can hope for is usually an approximation scheme, that is an approximation algorithm that can produce a (1+)-approximate solution for every  > 0. The algorithm runs in polynomial time for every fixed value of . However, as  tends

Parameterized Complexity News

to 0 the algorithm becomes progressively slower in such a way that the algorithm cannot be used to obtain optimal solutions in polynomial time. In the setting of approximate kernelization, we could end up in a situation where it is possible to produce a polynomial (1+)-approximate kernel for every fixed value of , but that the size of the kernel grows so fast when  tends to 0 that this algorithm cannot be used to give a polynomial size kernel (without any loss in solution quality). This leads to the concept of Polynomial Size Approximate Kernelization Scheme (PSAKS). In [11], the authors investigate the lossy kernelization complexity of several parameterized optimization problems, namely Connected Vertex Cover, Disjoint Cycle Packing, Disjoint Factors, Longest Path, Set Cover and Hitting Set. For all of these problems there are known lower bounds [5, 6, 7] precluding them from admitting polynomial kernels under widely believed complexity theoretic assumptions. For Connected Vertex Cover, Disjoint Cycle Packing and Disjoint Factors approximate kernels are designed that beat both the known lower bounds on kernel size and the lower bounds on approximation ratios of approximation algorithms. In fact, all these problems admit PSAKS. On the other hand, for Longest Path and Set Cover, even a constant factor approximate kernel of polynomial size would imply NP ⊆ coNP/Poly, collapsing the polynomial hierarchy. A constant factor approximate kernel for Hitting Set of polynomial size would violate the Exponential Time Hypothesis (ETH). The lower bounds of Longest Path and Set Cover are proved using the framework of gap creating cross compositions, an amalgamation of cross-compositions and gap-creating reductions. In particular these lower bounds also rule out approximate compressions to any other parameterized optimization problems. On the other hand, the lower bound for Hitting Set only rules out approximate kernels. As a consequence the lower bounds for Longest Path and Set Cover have more potential as starting points for reductions showing that even further problems do not admit approximate kernels. A list of recent works on lossy kernelization are [8, 9, 10]. For more details about lossy kernelization and for a set of concrete open problems on lossy kernelization we refer to [11].

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[4] M. R. Fellows, A. Kulik, F. A. Rosamond, and H. Shachnai, Parameterized approximation via fidelity preserving transformations, in ICALP 2012, Proceedings, Part I, pp. 351–362. [5] H. L. Bodlaender, R. G. Downey, M. R. Fellows, and D. Hermelin, On problems without polynomial kernels, J. Comput. Syst. Sci., 75 (2009), pp. 423–434. ´, and A. Yeo, Ker[6] H. L. Bodlaender, S. Thomasse nel bounds for disjoint cycles and disjoint paths, Theor. Comput. Sci., 412 (2011), pp. 4570–4578. [7] M. Dom, D. Lokshtanov, and S. Saurabh, Kernelization lower bounds through colors and IDs, ACM Transactions on Algorithms, 11 (2014), pp. 13:1–13:20. [8] R. Krithika, P. Misra, A. Rai and P. Tale, Lossy Kernels for Graph Contraction Problems, in FSTTCS 2016, Proceedings, pp. 23:1–23:14. [9] E. Eiben, M. Kumar, A.E. Mouawad and F. Panolan, Lossy Kernels for Connected Dominating Set on Sparse Graphs, Manuscript, 2017. [10] E. Eiben, D. Hermelin and M. S. Ramanujan, Lossy Kernels for Hitting Subgraphs, Private Communication, 2017. [11] D. Lokshtanov, F. Panolan, S. Saurabh and M. S. Ramanujan, Lossy Kernelization, To appear in STOC, 2017 (ArXiv: abs/1604.04111).

About the author: Fahad is a Postdoctoral Fellow in the Algorithms Group at the Department of Informatics at the University of Bergen. He completed his PhD from the Institute of Mathematical Sciences, Chennai, India under the supervision of Prof. Saket Saurabh in 2015. His PhD thesis on Dynamic Programming using Representative Families is available on his website.

Fixed-Parameter Tractable SAT Encodings

by Ronald de Haan, University of Amsterdam, the Netherlands The paradigm of parameterized complexity has been extremely productive and successful in obtaining efficient algorithms for NP-complete problems. The approach of identifying algorithmically exploitable structure in probReferences. lem inputs in terms of parameters, and developing fpt` , algorithms based on these parameters, can in principle [1] J. Byrka, F. Grandoni, T. Rothvoß, and L. Sanita Steiner tree approximation via iterative randomized round- also be used for problems whose classical complexity lies ing, J. ACM, 60 (2013), p. 6. beyond NP—e.g., for problems that are complete for [2] D. Marx, Parameterized complexity and approximation higher levels of the Polynomial Hierarchy (PH). Many exalgorithms, The Computer Journal, 51 (2008), pp. 60–78. amples of such problems can be found in the areas of Artificial Intelligence (AI) and Knowledge Representation and [3] M. R. Fellows, D. Hermelin, F. A. Rosamond, and Reasoning (KRR). However, the parameterized complexH. Shachnai, Tractable parameterizations for the mini- ity analysis of such problems shows that in many cases, mum linear arrangement problem, in ESA 2013, Proceed- only very restrictive parameters (that are unlikely to have ings, pp. 457–468. small values in practical settings) lead to fpt-algorithms.

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One possible approach for developing efficient algorithms for such problems—that has been proposed recently [3, 10]—is to combine fpt-algorithms with SAT solving algorithms. In this newsletter contribution, I will give a brief overview of recent research that is aimed at investigating the power and limits of this new approach. The results that I discuss are joint work with Stefan Szeider—who put forward the approach of fpt-reductions to SAT—and are discussed in detail in my PhD thesis [4].

structures is an additional source of intractability. An indispensable method for solving this problem in practice is that of bounded model checking, where the search for counterexamples witnessing that the specification is not satisfied is encoded as a SAT instance [1]. For a restricted case of this symbolic LTL model checking problem, the SAT encoding of bounded model checking can be seen as an fpt-reduction to SAT, where the parameter is the size of the LTL formula [7].

Extending SAT Encodings with the Power of FptAlgorithms. Even though the satisfiability problem for propositional logic formulas (SAT) is NP-complete— and thus no algorithms exist that solve this problem in polynomial time (in the worst case) unless P = NP— algorithm engineering over the last few decades has yielded SAT solving algorithms that work astonishingly well for many inputs that arise in numerous practical settings. In fact, in various industrial domains the approach of encoding problems into SAT (in polynomial time) and subsequently solving the problem by calling a SAT solver competes with, and often outperforms, state-of-the-art algorithms [2, 9, 11]. For problems at higher levels of the PH, the approach of using polynomial-time encodings into SAT is not possible (unless the PH collapses). However, combining the SAT encoding method with the viewpoint of parameterized complexity opens the door to the possibility of reducing problems to SAT and subsequently solving the problem using highly optimized and effective off-the-shelf SAT solving algorithms. For this new technique to work, one needs to identify problem parameters that enable an fpt-reduction to SAT (rather than a polynomial-time reduction). Importantly, for this approach, parameters do not need to be as restrictive as parameters that yield fptalgorithms to solve the problem in its entirety. Thus, more problem inputs will be solvable using fpt-reductions to SAT than using traditional fpt-algorithms—an increase in power that is paid for by giving up running time guarantees for the SAT solving part of the algorithm.

Augmented Intractability Theory. Like in the case of fpt-algorithms, in order to productively investigate in what settings problems can be solved using fptreductions to SAT, it is vital to have a completeness theory with intractability classes containing parameterized problems that are likely not fpt-reducible to SAT. The class para-NP consists of all parameterized problems that are (many-to-one) fpt-reducible to SAT, and therefore the required intractability classes cannot be contained in para-NP. So, in particular, the conventional parameterized intractability classes of the Weft hierarchy cannot be used to give evidence that an fpt-reduction to SAT is not possible. Nevertheless, the classes of the Weft hierarchy do provide a foundation for a suitable intractability theory: analogues of the classes W[t] between the first and the second level of the PH have been developed [6, 8]. These are the classes Σp2 [k∗] and Σp2 [∗k, t], that are based on parameterized weighted quantified Boolean circuit satisfiability problems, similarly to the way the classes W[t] are based on weighted variants of circuit satisfiability. Interestingly, it turns out that there are numerous natural parameterized problems that are complete for these new classes [4, 5].

Example: LTL Model Checking. The approach of solving problems by means of fpt-reductions to SAT is not only a theoretical gimmick, but has been used to develop practically relevant algorithms that compete with the state-of-the-art (albeit without using the terminology of ‘fpt-reductions to SAT’). An important example of a successful algorithm that is a product of this methodology can be found in the setting of LTL model checking for symbolically represented structures. Deciding whether a system (modelled using a Kripke structure) satisfies some desired specification (expressed in the temporal logic LTL) is an important problem that arises in the area of formal verification. Structures used to model software or hardware systems are often of exponential size, and therefore often represented succinctly using a symbolic formalism. This succinct representation of the

Some Future Research Directions. The first results investigating the potential and limits of fpt-reductions to SAT bring forth many exciting (experimental and theoretical) questions for future research, including the following. • For what additional problems could the approach of fpt-time SAT encodings prove useful to obtain algorithms that compete with the state-of-the-art in practice? • To what extent can we employ algorithmic techniques that are used in the development of fptalgorithms to construct fpt-reductions to SAT? In particular, what role could kernelization play in the development and study of (efficient) fpt-time SAT encodings? • How does the theoretical picture of the power of fptreductions to SAT change if we allow more general types of reductions (e.g., reductions that themselves can query SAT solvers)? • What happens if we would restrict the encodings to restricted types of SAT inputs (e.g., linear size, or

Parameterized Complexity News

a linear number of propositional variables)?

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Wien. Submit proposals by July 13, 2017. Detailed information is available online (www.wwtf.at).

References [1] Armin Biere. Bounded model checking. In Handbook of Satisfiability [2], pages 457–481. IOS Press, 2009. [2] Armin Biere, Marijn Heule, Hans van Maaren, and Toby Walsh, editors. Handbook of Satisfiability, volume 185 of Frontiers in Artificial Intelligence and Applications. IOS Press, 2009. [3] Johannes Klaus Fichte and Stefan Szeider. Backdoors to normality for disjunctive logic programs. In Proceedings of AAAI 2013, pages 320–327. AAAI Press, 2013. [4] Ronald de Haan. Parameterized Complexity in the Polynomial Hierarchy. PhD thesis, Technische Universit¨ at Wien, 2016. http://www.ronalddehaan.eu/files/ Dissertation_deHaan_2016.pdf. [5] Ronald de Haan and Stefan Szeider. Compendium of parameterized problems at higher levels of the Polynomial Hierarchy. Technical Report TR14–143, Electronic Colloquium on Computational Complexity (ECCC), 2014. [6] Ronald de Haan and Stefan Szeider. The parameterized complexity of reasoning problems beyond NP. In Chitta Baral, Giuseppe De Giacomo, and Thomas Eiter, editors, Proceedings of KR 2014. AAAI Press, 2014. [7] Ronald de Haan and Stefan Szeider. Parameterized complexity results for symbolic model checking of temporal logics. In Proceedings of KR 2016, pages 453–462. AAAI Press, 2016. [8] Ronald de Haan and Stefan Szeider. Parameterized complexity classes beyond para-NP. J. of Computer and System Sciences, 87:16–57, 2017. [9] Sharad Malik and Lintao Zhang. Boolean satisfiability from theoretical hardness to practical success. Communications of the ACM, 52(8):76–82, 2009. [10] Andreas Pfandler, Stefan R¨ ummele, and Stefan Szeider. Backdoors to abduction. In Proceedings of IJCAI 2013. AAAI Press/IJCAI, 2013. [11] Moshe Y. Vardi. Boolean satisfiability: theory and engineering. Communications of the ACM, 57(3):5, March 2014.

About the author: Ronald is a Postdoctoral Fellow with Ulle Endriss in Amsterdam, investigating complexity issues in computational social choice. He completed his PhD from the TU Wien under the supervision of Prof. Stefan Szeider in 2016. The title of his PhD thesis is Parameterized Complexity in the Polynomial Hierarchy and is available online here.

JOBS Page on www.fpt.wikidot.com If you are on the job market or if you have positions to fill, please have a look at the JOBS page on the wiki.

CONFERENCES A new CS Theory Events site has been launched by Alexander S. Kulikov and Shachar Lovett: http://cstheory-events.org. Its goal is to allow the TCS community to advertise and learn about relevant events (workshops, schools, etc), with a focus on algorithms and complexity. The top theory conferences are listed for convenience, but the goal is to mainly focus on events that do not repeat annually and that people may not be aware of. The site allows one to subscribe to new events (to get a notification when a new event is published), to see the list of upcoming events, and to see/export a calendar with relevant dates (such as deadlines for submission/registration).

June–August in Australia There is a lot happening in Australia this summer: 1. Computational & Algorithmic Topology (CATS), Univ of Sydney, 27 June–1 July 2017. Serge Gaspers will give an introductory lecture on parameterized complexity. 2. Computational Geometry Week (CG Week2017), the premier international forum for advances in comp geometry and its many applications. Univ of Queensland, Brisbane, July 4–7. Benjamin Burton, local organizer. 3. The 28th International Workshop on Combinatorial Algorithms (IWOCA2017), Newcastle, July 17-21. Special memorial to Mirka Miller. 4. ICML and UAI in Sydney, August 6–15. 5. The big IJCAI / SAT / CP / ICLP event in Melbourne, August 19–September 1.

Grant opportunity Vienna Stefan Szeider would like to bring to the attention of the Parameterized Complexity community the call for the 8th Vienna Research Groups for Young Investigators. The level of excellence is similar to an ERC starting grant, but the setup is slightly different. A crossover between mathematics and CS might be an interesting combination for the call. A successful candidate has good chances for negotiating a permanent contract with TU

June–Birthday of Michael Fellows A two-day conference to celebrate the birthday of Michael Fellows will be held June 15– 16 at the University of Bergen. Registration is free. Speakers include: Hubert Chan, Fedor Fomin, Petr Golovach, Bart Jansen, Mamadou Kant´e, Daniel Lokshtanov, Amer Mouawad, Saket Saurabh, and Meirav Zehavi. See here. Organizers: The Algorithms Group at UiB. Happy Birthday, Mike!

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June–FAW’17

natorial game theory; graph game parameters (game chromatic number, game domination number, . . . ), alThe 11th International Frontiers of Algorithmics gorithmic complexity of games (exact, approximation, Workshop June 23–25, Chengdu, China. parameterized), links between games and other fields Program Co-Chairs: Frances Rosamond (Univ of Bergen) (including but not limited to: logic, language theand Mingyu Xiao (Univ of Electronic Science and Techory, number theory, artificial intelligence). Registranology of China). tion is free of charge. For more information and to register and submit a proposal for a talk, please visit June–Dagstuhl Seminar 17261 https://liris.cnrs.fr/gag/workshop/index.html. Dagstuhl Seminar 17261: Voting: Beyond Simple Majorities and Single-Winner Elections, June 25– Dec–Tel Aviv, Israel 30. Organizers: Dorothea Baumeister (Heinrich–Heine– Recent Advances in Parameterized Complexity is Univ Dusseldorf), Piotr Faliszewski (AGH Univ of Sciheld from 3–7 December 2017 in Tel Aviv, Israel. The ence & Technology, Krakow), Annick Laruelle (Univ of purpose of Recent Advances in Parameterized Complexthe Basque Country, Bilbao), Toby Walsh (TU Berlin). ity is twofold. First, the event will present an overview of recent, exciting advances in the field of Parameterized July–IWOCA’17 Complexity. Second, to attract new researchers to work The 28th International Workshop on Combinato- on topics in this field of research, the program will also rial Algorithms is held from 17–21 July in Newcastle, include a preparatory school at the level of an introducAustralia. This is a very special IWOCA, dedicated to the tory course. We thus invite both graduate students and memory of Prof Mirka Miller, one of IWOCA’s founders. established researchers to join. Mirka was diagnosed with gastro-oesophageal cancer in 2015 and died of this disease on 2 January, 2016. This particular IWOCA gathering is as much about celebrat- Moving Around – Congratulations to all ing the life and work of this wonderful person as it is in continuing her legacy, of which IWOCA is just one aspect. Congratulations to Prof Rolf Niedermeier who has become Research Dean for the Institute of Software Engineering and Theoretical Computer Science, Technische August–COCOON’17 Universit¨at Berlin (TU-Berlin). The 23rd Annual International Computing and Combinatorics Conference COCOON’17 will be held in Hong Kong during August 3–5, 2017. Areas of interest include CONGRATULATIONS New PhDs theoretical results as well as reports on experimental and applied research of general algorithmic interest and re- Amir Abboud, Hardness in P, 2017, Stanford Universearch that is motivated by real-world problems. Invited sity. Advisor: Prof. Virginia Vassilevska. Congratulaspeakers are D´ aniel Marx (Hungarian Academy of Sci- tions, Dr. Abboud. Amir will be joining IBM Research ences), Shang-Hua Teng (Univ of Southern California), in Almaden as a Research Staff member. Virginia Vassilevska Williams (Stanford Univ). Program Co-Chairs are Yixin Cao (Hong Kong Polytechnic Kim Manuel Klein, About the Structure and Sensitivity of Integer Linear Programs and their Application in Univ) and Jianer Chen (Texas A&M Univ). Combinatorial Optimization, 2017, Christian-AlbrechtsUniversitt zu Kiel, Germany. Advisor: Prof. Klaus Sept–IPEC’17 Jansen. Congratulations, Dr. Klein. Kim will join Fritz The 12th IPEC is part of ALGO 2017 is held from 4–8 Eisenbrand (EPFL Lausanne) beginning October. September 2017 in Vienna, Austria. Mithilesh Kumar, Multivariate Algorithmic Analysis of Hitting Small Sets, 2017, University of Bergen, Norway. Sept–3rd PC Summer School Advisor: Prof. Daniel Lokshtanov. Congratulations, Dr. The 3rd Parameterized Complexity Summer School will Kumar. Mithilesh is now employed as Research Engineer be held in Vienna, Austria, from 1 to 3 Sept 2017 (Friday- at Simula@UiB in Bergen. Sunday) co-located with ALGO 2017 and will take place in the same venue. See https://algo2017.ac.tuwien. Simon Mackenzie, Upper bounds for cake cutting, 2017, ac.at/pcss/ UNSW Sydney. Supervisor: Dr. Serge Gaspers, cosupervisor: Prof. Toby Walsh. Congratulations, Dr. Mackenzie. Simon has taken up a postdoctoral position Oct–Workshop on Games and Graphs with Ariel Procaccia at Carnegie Mellon University. The Workshop on Games and Graphs will be held from 23 to 25 October in Lyon. Topics covered: combi-