Propositional Proof Complexity Past Present and Future

Total Page:16

File Type:pdf, Size:1020Kb

Propositional Proof Complexity� Past� Present� and Future Prop ositional Pro of Complexity Past Present and Future Paul Beame and Toniann Pitassi Abstract Pro of complexity the study of the lengths of pro ofs in prop ositional logic is an area of study that is fundamentally connected b oth to ma jor op en questions of computational complexity theory and to practical prop erties of automated theorem provers In the last decade there have b een a number of signicant advances in pro of complexity lower b ounds Moreover new connections b etween pro of complexity and circuit complexity have b een uncovered and the interplay b etween these two areas has b ecome quite rich In addition attempts to extend existing lower b ounds to ever stronger systems of pro of have spurred the introduction of new and interesting pro of systems adding b oth to the practical asp ects of pro of complexity as well as to a rich theory This note attempts to survey these developments and to lay out some of the op en problems in the area Introduction One of the most basic questions of logic is the following Given a uni versally true statement tautology what is the length of the shortest pro of of the statement in some standard axiomatic pro of system The prop osi tional logic version of this question is particularly imp ortant in computer science for b oth theorem proving and complexity theory Imp ortant related algorithmic questions are Is there an ecient algorithm that will pro duce a pro of of any tautology Is there an ecient algorithm to pro duce the shortest pro of of any tautology Such questions of theorem proving and complexity inspired Co oks seminal pap er on NPcompleteness notably enti tled The complexity of theoremproving pro cedures and were contem plated even earlier by Go del in his now wellknown letter to von Neumann see The ab ove questions have fundamental implications for complexity the ory As formalized by Co ok and Reckhow there exists a prop ositional pro of system giving rise to short p olynomialsize pro ofs of all tautologies if and only if NP equals coNP Co ok and Reckhow were the rst to prop ose a program of research aimed at attacking the NP versus coNP problem by systematically studying and proving strong lower b ounds for standard pro of BEAME AND PITASSI systems of increasing complexity This program has several imp ortant side eects First standard pro of systems are interesting in their own right Almost all theoremproving systems implement a deterministic or randomized pro cedure that is based on a standard prop ositional pro of system and thus upp er and lower b ounds on these systems shed light on the inherent com plexity of any theoremproving system up on which it is based The most striking example is Resolution on which almost all prop ositional theorem provers and even rstorder theorem provers are based Secondly and of equal or greater imp ortance lower b ounds on standard pro of systems additionally prove that a certain class of algorithms for the satisability problem will fail to run in p olynomialtime This program has led to many b eautiful results as well as to new con nections with circuit complexity within the last twenty years In this article we will try to highlight some of the main discoveries with emphasis on the interplay b etween logic circuit complexity theory and combinatorics that has arisen We omit all pro ofs see for a quite readable survey that includes detailed pro ofs of many of the earlier results In section we dene various pro of systems that will b e discussed throughout this article In section we review some of the main lower b ounds that have b een proven for standard pro of systems emphasizing the combinatorial techniques and connections to circuit complexity that have b een shown Finally in section we list some of the main op en questions and promising directions in the area Prop ositional pro of systems What exactly is a prop ositional pro of Co ok and Reckhow were p ossibly the rst to make this and related questions precise They saw that it is useful to separate the idea of providing a pro of from that of b eing ecient Since there are only nitely many truth assignments to check why not allow the statement itself as a pro of What extra value is there in a lled out truth table or a derivation using some axiominference scheme The key observation is that a pro of is easy to check unlike the statement itself Of course we also need to know the format in which the pro of will b e presented in order to make this check That is in order to identify some character string as a pro of we must see it as an instance of some general format for presenting pro ofs Therefore a propositional proof system S is dened to b e a p olynomialtime computable predicate S such that for all F F TAUT p S F p That is we identify a pro of system with a p olynomial time pro cedure that checks the correctness of pro ofs Prop erty ensures that the system S is Co ok and Reckhows denition is formally dierent although essentially equivalent to this one They dene a pro of system as a p olynomialtime computable onto function f TAUT which can b e thought of as mapping each string viewed as p otential pro of PROPOSITIONAL PROOF COMPLEXITY PAST PRESENT AND FUTURE logically b oth sound and complete The complexity comp of a prop ositional S pro of system S is then dened to b e the smallest b ounding function b N N on the lengths of the pro ofs in S as a function of the tautologies b eing proved ie for all F F TAUT pjpj bjF j S F p Ecient pro of systems corresp ond to those of p olynomial complexity these are called pbounded Given these denitions many natural questions arise How ecient are existing pro of systems How can one compare the relative eciencies of pro of systems Can one classify pro of systems using reduction as we do languages Is there a pro of system of optimal complexity up to a p olyno mial The key to ol for comparing pro of systems is psimulation A pro of sys tem T psimulates a pro of system S i there is a p olynomialtime computable function f mapping pro ofs in S into pro ofs in T that is for all F TAUT S F p T F f p We use nonstandard notation and write S T in p O this case Clearly it implies that comp comp One says that T T S weakly psimulates S i we have this latter condition but we do not know if such a reducing function f exists One says that S and T are pequivalent i each psimulates the other Obviously two pequivalent pro of systems either are b oth pb ounded or neither is Frege and extendedFrege pro ofs Co ok and Reckhow did more than merely formalize the intuitive general notions of the eciency of prop o sitional pro ofs They also identied two ma jor classes of pequivalent pro of systems which they called Frege and extendedFrege systems in honor of Gottlob Frege who made some of the rst attempts to formalize mathe matics based on logic and set theory and whose work is now b est known as the unfortunate victim of Russells famous paradox concerning the set of all sets that are not members of themselves A Frege system F is dened in terms of a nite implicationally complete enough to derive every true statement set A of axioms and inference rules F A A k The general form of an inference rule is written as where A A k B and B are prop ositional formulas the rule is an axiom if k A formula H follows from formulas G G using this inference rule if there is a k consistent set of substitutions of formulas for the variables app earing in for i k and H B the rule such that G A i i For a Frege system F a typical set of axioms A might include the F or identity as well as the cut rule axiom of the excluded middle AA AA AAB AC C B or modus ponens A pro of of a tautology F in F consists AB B of a nite sequence F F of formulas called lines such that F F r r onto the tautology it proves The analogous function f in our case would map F p to F if S F p were true and would map it to a trivial tautology x x otherwise In the converse direction one would dene S F p to b e true i f p F BEAME AND PITASSI and each F either is an an instance of an axiom in A or follows from some j F previous lines F F for i i j using some inference rule of A i i F k k An equivalent way of using a Frege system works backwards from F to derive a contradiction such as p p The size of a Frege pro of is typically dened to b e the total number of symbols o ccurring in the pro of The pro of can also b e treelike or daglike in the treelike case each intermediate formula can b e at used at most once in subsequent derivations in the more general daglike case an intermediate formula can b e used unboundedly many times Kra jcek has shown that for Frege systems there is not much loss in eciency in going from a daglike pro of to a treelike pro of Various Frege systems which have also b een called Hilb ert systems or Hilb ertstyle deduction systems app ear frequently in logic textb o oks How ever it is dicult to nd two logic textb o oks that dene precisely the same such system Co ok and Reckhow showed that these distinctions do not mat ter namely all Frege systems are pequivalent Furthermore they showed that Frege systems were also pequivalent to another class of pro of systems app earing frequently in logic textb o oks called sequent calculus or Gentzen systems These systems manipulate pairs of sequences or sets of formulas written as where and are sequences of formulas with the in tended interpretation b eing that the conjunction of the formulas in implies the disjunction of the formulas in Therefore to prove a
Recommended publications
  • Circuit Complexity, Proof Complexity and Polynomial Identity Testing
    Electronic Colloquium on Computational Complexity, Report No. 52 (2014) Circuit Complexity, Proof Complexity and Polynomial Identity Testing Joshua A. Grochow and Toniann Pitassi April 12, 2014 Abstract We introduce a new and very natural algebraic proof system, which has tight connections to (algebraic) circuit complexity. In particular, we show that any super-polynomial lower bound on any Boolean tautology in our proof system implies that the permanent does not have polynomial- size algebraic circuits (VNP 6= VP). As a corollary to the proof, we also show that super- polynomial lower bounds on the number of lines in Polynomial Calculus proofs (as opposed to the usual measure of number of monomials) imply the Permanent versus Determinant Conjecture. Note that, prior to our work, there was no proof system for which lower bounds on an arbitrary tautology implied any computational lower bound. Our proof system helps clarify the relationships between previous algebraic proof systems, and begins to shed light on why proof complexity lower bounds for various proof systems have been so much harder than lower bounds on the corresponding circuit classes. In doing so, we highlight the importance of polynomial identity testing (PIT) for understanding proof complex- ity. More specifically, we introduce certain propositional axioms satisfied by any Boolean circuit computing PIT. (The existence of efficient proofs for our PIT axioms appears to be somewhere in between the major conjecture that PIT2 P and the known result that PIT2 P=poly.) We use these PIT axioms to shed light on AC0[p]-Frege lower bounds, which have been open for nearly 30 years, with no satisfactory explanation as to their apparent difficulty.
    [Show full text]
  • Week 1: an Overview of Circuit Complexity 1 Welcome 2
    Topics in Circuit Complexity (CS354, Fall’11) Week 1: An Overview of Circuit Complexity Lecture Notes for 9/27 and 9/29 Ryan Williams 1 Welcome The area of circuit complexity has a long history, starting in the 1940’s. It is full of open problems and frontiers that seem insurmountable, yet the literature on circuit complexity is fairly large. There is much that we do know, although it is scattered across several textbooks and academic papers. I think now is a good time to look again at circuit complexity with fresh eyes, and try to see what can be done. 2 Preliminaries An n-bit Boolean function has domain f0; 1gn and co-domain f0; 1g. At a high level, the basic question asked in circuit complexity is: given a collection of “simple functions” and a target Boolean function f, how efficiently can f be computed (on all inputs) using the simple functions? Of course, efficiency can be measured in many ways. The most natural measure is that of the “size” of computation: how many copies of these simple functions are necessary to compute f? Let B be a set of Boolean functions, which we call a basis set. The fan-in of a function g 2 B is the number of inputs that g takes. (Typical choices are fan-in 2, or unbounded fan-in, meaning that g can take any number of inputs.) We define a circuit C with n inputs and size s over a basis B, as follows. C consists of a directed acyclic graph (DAG) of s + n + 2 nodes, with n sources and one sink (the sth node in some fixed topological order on the nodes).
    [Show full text]
  • The Complexity Zoo
    The Complexity Zoo Scott Aaronson www.ScottAaronson.com LATEX Translation by Chris Bourke [email protected] 417 classes and counting 1 Contents 1 About This Document 3 2 Introductory Essay 4 2.1 Recommended Further Reading ......................... 4 2.2 Other Theory Compendia ............................ 5 2.3 Errors? ....................................... 5 3 Pronunciation Guide 6 4 Complexity Classes 10 5 Special Zoo Exhibit: Classes of Quantum States and Probability Distribu- tions 110 6 Acknowledgements 116 7 Bibliography 117 2 1 About This Document What is this? Well its a PDF version of the website www.ComplexityZoo.com typeset in LATEX using the complexity package. Well, what’s that? The original Complexity Zoo is a website created by Scott Aaronson which contains a (more or less) comprehensive list of Complexity Classes studied in the area of theoretical computer science known as Computa- tional Complexity. I took on the (mostly painless, thank god for regular expressions) task of translating the Zoo’s HTML code to LATEX for two reasons. First, as a regular Zoo patron, I thought, “what better way to honor such an endeavor than to spruce up the cages a bit and typeset them all in beautiful LATEX.” Second, I thought it would be a perfect project to develop complexity, a LATEX pack- age I’ve created that defines commands to typeset (almost) all of the complexity classes you’ll find here (along with some handy options that allow you to conveniently change the fonts with a single option parameters). To get the package, visit my own home page at http://www.cse.unl.edu/~cbourke/.
    [Show full text]
  • Automating Cutting Planes Is NP-Hard
    Automating Cutting Planes is NP-Hard Mika G¨o¨os† Sajin Koroth Ian Mertz Toniann Pitassi Stanford University Simon Fraser University Uni. of Toronto Uni. of Toronto & IAS April 20, 2020 Abstract We show that Cutting Planes (CP) proofs are hard to find: Given an unsatisfiable formula F , (1) it is NP-hard to find a CP refutation of F in time polynomial in the length of the shortest such refutation; and (2) unless Gap-Hitting-Set admits a nontrivial algorithm, one cannot find a tree-like CP refutation of F in time polynomial in the length of the shortest such refutation. The first result extends the recent breakthrough of Atserias and M¨uller (FOCS 2019) that established an analogous result for Resolution. Our proofs rely on two new lifting theorems: (1) Dag-like lifting for gadgets with many output bits. (2) Tree-like lifting that simulates an r-round protocol with gadgets of query complexity O(log r) independent of input length. Contents 1 Introduction1 4.1 Subcubes from simplices . .9 1.1 Cutting Planes . .1 4.2 Simplified proof . 11 1.2 Dag-like result . .2 4.3 Accounting for error . 12 1.3 Tree-like result . .2 5 Tree-like definitions 13 2 Overview of proofs3 5.1 Tree-like dags/proofs . 13 2.1 Dag-like case . .3 5.2 Real protocols . 14 arXiv:2004.08037v1 [cs.CC] 17 Apr 2020 2.2 Tree-like case . .4 6 Tree-like lifting 14 3 Dag-like definitions6 6.1 Proof of real lifting . 14 3.1 Standard models .
    [Show full text]
  • Reflection Principles, Propositional Proof Systems, and Theories
    Reflection principles, propositional proof systems, and theories In memory of Gaisi Takeuti Pavel Pudl´ak ∗ July 30, 2020 Abstract The reflection principle is the statement that if a sentence is provable then it is true. Reflection principles have been studied for first-order theories, but they also play an important role in propositional proof complexity. In this paper we will revisit some results about the reflection principles for propositional proofs systems using a finer scale of reflection principles. We will use the result that proving lower bounds on Resolution proofs is hard in Resolution. This appeared first in the recent article of Atserias and M¨uller [2] as a key lemma and was generalized and simplified in some spin- off papers [11, 13, 12]. We will also survey some results about arithmetical theories and proof systems associated with them. We will show a connection between a conjecture about proof complexity of finite consistency statements and a statement about proof systems associated with a theory. 1 Introduction arXiv:2007.14835v1 [math.LO] 29 Jul 2020 This paper is essentially a survey of some well-known results in proof complexity supple- mented with some observations. In most cases we will also sketch or give an idea of the proofs. Our aim is to focus on some interesting results rather than giving a complete ac- count of known results. We presuppose knowledge of basic concepts and theorems in proof complexity. An excellent source is Kraj´ıˇcek’s last book [19], where you can find the necessary definitions and read more about the results mentioned here.
    [Show full text]
  • Contents Part 1. a Tour of Propositional Proof Complexity. 418 1. Is There A
    The Bulletin of Symbolic Logic Volume 13, Number 4, Dec. 2007 THE COMPLEXITY OF PROPOSITIONAL PROOFS NATHAN SEGERLIND Abstract. Propositional proof complexity is the study of the sizes of propositional proofs, and more generally, the resources necessary to certify propositional tautologies. Questions about proof sizes have connections with computational complexity, theories of arithmetic, and satisfiability algorithms. This is article includes a broad survey of the field, and a technical exposition of some recently developed techniques for proving lower bounds on proof sizes. Contents Part 1. A tour of propositional proof complexity. 418 1. Is there a way to prove every tautology with a short proof? 418 2. Satisfiability algorithms and theories of arithmetic 420 3. A menagerie of Frege-like proof systems 427 4. Reverse mathematics of propositional principles 434 5. Feasible interpolation 437 6. Further connections with satisfiability algorithms 438 7. Beyond the Frege systems 441 Part 2. Some lower bounds on refutation sizes. 444 8. The size-width trade-off for resolution 445 9. The small restriction switching lemma 450 10. Expansion clean-up and random 3-CNFs 459 11. Resolution pseudowidth and very weak pigeonhole principles 467 Part 3. Open problems, further reading, acknowledgments. 471 Appendix. Notation. 472 References. 473 Received June 27, 2007. This survey article is based upon the author’s Sacks Prize winning PhD dissertation, however, the emphasis here is on context. The vast majority of results discussed are not the author’s, and several results from the author’s dissertation are omitted. c 2007, Association for Symbolic Logic 1079-8986/07/1304-0001/$7.50 417 418 NATHAN SEGERLIND Part 1.
    [Show full text]
  • A Short History of Computational Complexity
    The Computational Complexity Column by Lance FORTNOW NEC Laboratories America 4 Independence Way, Princeton, NJ 08540, USA [email protected] http://www.neci.nj.nec.com/homepages/fortnow/beatcs Every third year the Conference on Computational Complexity is held in Europe and this summer the University of Aarhus (Denmark) will host the meeting July 7-10. More details at the conference web page http://www.computationalcomplexity.org This month we present a historical view of computational complexity written by Steve Homer and myself. This is a preliminary version of a chapter to be included in an upcoming North-Holland Handbook of the History of Mathematical Logic edited by Dirk van Dalen, John Dawson and Aki Kanamori. A Short History of Computational Complexity Lance Fortnow1 Steve Homer2 NEC Research Institute Computer Science Department 4 Independence Way Boston University Princeton, NJ 08540 111 Cummington Street Boston, MA 02215 1 Introduction It all started with a machine. In 1936, Turing developed his theoretical com- putational model. He based his model on how he perceived mathematicians think. As digital computers were developed in the 40's and 50's, the Turing machine proved itself as the right theoretical model for computation. Quickly though we discovered that the basic Turing machine model fails to account for the amount of time or memory needed by a computer, a critical issue today but even more so in those early days of computing. The key idea to measure time and space as a function of the length of the input came in the early 1960's by Hartmanis and Stearns.
    [Show full text]
  • Lower Bounds from Learning Algorithms
    Circuit Lower Bounds from Nontrivial Learning Algorithms Igor C. Oliveira Rahul Santhanam Charles University in Prague University of Oxford Motivation and Background Previous Work Lemma 1 [Speedup Phenomenon in Learning Theory]. From PSPACE BPTIME[exp(no(1))], simple padding argument implies: DSPACE[nω(1)] BPEXP. Some connections between algorithms and circuit lower bounds: Assume C[poly(n)] can be (weakly) learned in time 2n/nω(1). Lower bounds Lemma [Diagonalization] (3) (the proof is sketched later). “Fast SAT implies lower bounds” [KL’80] against C ? Let k N and ε > 0 be arbitrary constants. There is L DSPACE[nω(1)] that is not in C[poly]. “Nontrivial” If Circuit-SAT can be solved efficiently then EXP ⊈ P/poly. learning algorithm Then C-circuits of size nk can be learned to accuracy n-k in Since DSPACE[nω(1)] BPEXP, we get BPEXP C[poly], which for a circuit class C “Derandomization implies lower bounds” [KI’03] time at most exp(nε). completes the proof of Theorem 1. If PIT NSUBEXP then either (i) NEXP ⊈ P/poly; or 0 0 Improved algorithmic ACC -SAT ACC -Learning It remains to prove the following lemmas. upper bounds ? (ii) Permanent is not computed by poly-size arithmetic circuits. (1) Speedup Lemma (relies on recent work [CIKK’16]). “Nontrivial SAT implies lower bounds” [Wil’10] Nontrivial: 2n/nω(1) ? (Non-uniform) Circuit Classes: If Circuit-SAT for poly-size circuits can be solved in time (2) PSPACE Simulation Lemma (follows [KKO’13]). 2n/nω(1) then NEXP ⊈ P/poly. SETH: 2(1-ε)n ? ? (3) Diagonalization Lemma [Folklore].
    [Show full text]
  • Some Hardness Escalation Results in Computational Complexity Theory Pritish Kamath
    Some Hardness Escalation Results in Computational Complexity Theory by Pritish Kamath B.Tech. Indian Institute of Technology Bombay (2012) S.M. Massachusetts Institute of Technology (2015) Submitted to Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering & Computer Science at Massachusetts Institute of Technology February 2020 ⃝c Massachusetts Institute of Technology 2019. All rights reserved. Author: ............................................................. Department of Electrical Engineering and Computer Science September 16, 2019 Certified by: ............................................................. Ronitt Rubinfeld Professor of Electrical Engineering and Computer Science, MIT Thesis Supervisor Certified by: ............................................................. Madhu Sudan Gordon McKay Professor of Computer Science, Harvard University Thesis Supervisor Accepted by: ............................................................. Leslie A. Kolodziejski Professor of Electrical Engineering and Computer Science, MIT Chair, Department Committee on Graduate Students Some Hardness Escalation Results in Computational Complexity Theory by Pritish Kamath Submitted to Department of Electrical Engineering and Computer Science on September 16, 2019, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science & Engineering Abstract In this thesis, we prove new hardness escalation results in computational complexity theory; a phenomenon where hardness results against seemingly weak models of computation for any problem can be lifted, in a black box manner, to much stronger models of computation by considering a simple gadget composed version of the original problem. For any unsatisfiable CNF formula F that is hard to refute in the Resolution proof system, we show that a gadget-composed version of F is hard to refute in any proof system whose lines are computed by efficient communication protocols.
    [Show full text]
  • A Superpolynomial Lower Bound on the Size of Uniform Non-Constant-Depth Threshold Circuits for the Permanent
    A Superpolynomial Lower Bound on the Size of Uniform Non-constant-depth Threshold Circuits for the Permanent Pascal Koiran Sylvain Perifel LIP LIAFA Ecole´ Normale Superieure´ de Lyon Universite´ Paris Diderot – Paris 7 Lyon, France Paris, France [email protected] [email protected] Abstract—We show that the permanent cannot be computed speak of DLOGTIME-uniformity), Allender [1] (see also by DLOGTIME-uniform threshold or arithmetic circuits of similar results on circuits with modulo gates in [2]) has depth o(log log n) and polynomial size. shown that the permanent does not have threshold circuits of Keywords-permanent; lower bound; threshold circuits; uni- constant depth and “sub-subexponential” size. In this paper, form circuits; non-constant depth circuits; arithmetic circuits we obtain a tradeoff between size and depth: instead of sub-subexponential size, we only prove a superpolynomial lower bound on the size of the circuits, but now the depth I. INTRODUCTION is no more constant. More precisely, we show the following Both in Boolean and algebraic complexity, the permanent theorem. has proven to be a central problem and showing lower Theorem 1: The permanent does not have DLOGTIME- bounds on its complexity has become a major challenge. uniform polynomial-size threshold circuits of depth This central position certainly comes, among others, from its o(log log n). ]P-completeness [15], its VNP-completeness [14], and from It seems to be the first superpolynomial lower bound on Toda’s theorem stating that the permanent is as powerful the size of non-constant-depth threshold circuits for the as the whole polynomial hierarchy [13].
    [Show full text]
  • IAN MERTZ Contact Research Theory Group [email protected] Department of Computer Science University of Toronto
    IAN MERTZ Contact Research www.cs.toronto.edu/∼mertz Theory Group [email protected] Department of Computer Science University of Toronto EDUCATION University of Toronto, Toronto, ON ................................................................... Winter 2018- Ph.D. in Computer Science Advisor: Toniann Pitassi University of Toronto, Toronto, ON .......................................................... Fall 2016-Winter 2018 Masters in Science, Computer Science Advisor: Toniann Pitassi, Stephen Cook Rutgers University, Piscataway, NJ ........................................................... Fall 2012-Spring 2016 B.S. in Computer Science, B.A. in Mathematics, B.A. in East Asian Language Studies (concentration in Japanese) Summa cum laude, honors program, Dean's List PUBLICATIONS Lifting is as easy as 1,2,3 Ian Mertz, Toniann Pitassi In submission, STOC '21. Automating Cutting Planes is NP-hard Mika G¨o¨os,Sajin Koroth, Ian Mertz, Toniann Pitassi In Proc. 52nd ACM Symposium on Theory of Computing (STOC '20), Association for Computing Machinery (ACM), 84 (132) pp. 68-77, 2020. Catalytic Approaches to the Tree Evaluation Problem James Cook, Ian Mertz In Proc. 52nd ACM Symposium on Theory of Computing (STOC '20), Association for Computing Machinery (ACM), 84 (132) pp. 752-760, 2020. Short Proofs Are Hard to Find Ian Mertz, Toniann Pitassi, Yuanhao Wei In Proc. 46th International Colloquium on Automata, Languages and Programming (ICALP '19), Leibniz International Proceedings in Informatics (LIPIcs), 84 (132) pp. 1-16, 2019. Dual VP Classes Eric Allender, Anna G´al,Ian Mertz computational complexity, 25 pp. 1-43, 2016. An earlier version appeared in Proc. 40th International Symposium on Mathematical Foundations of Computer Science (MFCS '15), Lecture Notes in Computer Science, 9235 pp. 14-25, 2015.
    [Show full text]
  • ECC 2015 English
    © Springer-Verlag 2015 SpringerMedizin.at/memo_inoncology SpringerMedizin.at 2/15 /memo_inoncology memo – inOncology SPECIAL ISSUE Congress Report ECC 2015 A GLOBAL CONGRESS DIGEST ON NSCLC Report from the 18th ECCO- 40th ESMO European Cancer Congress, Vienna 25th–29th September 2015 Editorial Board: Alex A. Adjei, MD, PhD, FACP, Roswell Park, Cancer Institute, New York, USA Wolfgang Hilbe, MD, Departement of Oncology, Hematology and Palliative Care, Wilhelminenspital, Vienna, Austria Massimo Di Maio, MD, National Institute of Tumor Research and Th erapy, Foundation G. Pascale, Napoli, Italy Barbara Melosky, MD, FRCPC, University of British Columbia and British Columbia Cancer Agency, Vancouver, Canada Robert Pirker, MD, Medical University of Vienna, Vienna, Austria Yu Shyr, PhD, Department of Biostatistics, Biomedical Informatics, Cancer Biology, and Health Policy, Nashville, TN, USA Yi-Long Wu, MD, FACS, Guangdong Lung Cancer Institute, Guangzhou, PR China Riyaz Shah, PhD, FRCP, Kent Oncology Centre, Maidstone Hospital, Maidstone, UK Filippo de Marinis, MD, PhD, Director of the Th oracic Oncology Division at the European Institute of Oncology (IEO), Milan, Italy Supported by Boehringer Ingelheim in the form of an unrestricted grant IMPRESSUM/PUBLISHER Medieninhaber und Verleger: Springer-Verlag GmbH, Professional Media, Prinz-Eugen-Straße 8–10, 1040 Wien, Austria, Tel.: 01/330 24 15-0, Fax: 01/330 24 26-260, Internet: www.springer.at, www.SpringerMedizin.at. Eigentümer und Copyright: © 2015 Springer-Verlag/Wien. Springer ist Teil von Springer Science + Business Media, springer.at. Leitung Professional Media: Dr. Alois Sillaber. Fachredaktion Medizin: Dr. Judith Moser. Corporate Publishing: Elise Haidenthaller. Layout: Katharina Bruckner. Erscheinungsort: Wien. Verlagsort: Wien. Herstellungsort: Linz. Druck: Friedrich VDV, Vereinigte Druckereien- und Verlags-GmbH & CO KG, 4020 Linz; Die Herausgeber der memo, magazine of european medical oncology, übernehmen keine Verantwortung für diese Beilage.
    [Show full text]