《理论计算机科学》课程教学资源（阅读文献）Computational Complexity - A Modern Approach

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About this book Computational complexity theory has developed rapidly in the past three decades.The list of surprising and fundamental results proved since 1990 alone could fill a book:these include new probabilistic definitions of classical complexity classes (IP PSPACE and the PCP Theorems) and their implications for the field of approximation algorithms;Shor's algorithm to factor integers using a quantum computer;an understanding of why current approaches to the famous P versus NP will not be successful;a theory of derandomization and pseudorandomness based upon com- putational hardness;and beautiful constructions of pseudorandom objects such as extractors and expanders. This book aims to describe such recent achievements of complexity theory in the context of the classical results.It is intended to both serve as a textbook as a reference for self-study.This means it must simultaneously cater to many audiences,and it is carefully designed with that goal. Throughout the book we explain the context in which a certain notion is useful,and why things are defined in a certain way.Examples and solved exercises accompany key definitions.We assume essentially no computational background and very minimal mathematical background,which we review in Appendix A. We have also provided a web site for this book at http://www.cs.princeton.edu/theory/complexity/ with related auxiliary material.This includes web chapters on automata and computability theory, detailed teaching plans for courses based on this book,a draft of all the book's chapters,and links to other online resources covering related topics. The book is divided into three parts: Part I:Basic complexity classes.This volume provides a broad introduction to the field.Start- ing from the definition of Turing machines and the basic notions of computability theory,this volumes covers the basic time and space complexity classes,and also includes a few more modern topics such probabilistic algorithms,interactive proofs and cryptography. Part II:Lower bounds on concrete computational models.This part describes lower bounds on resources required to solve algorithmic tasks on concrete models such as circuits,decision trees,etc.Such models may seem at first sight very different from Turing machines,but looking deeper one finds interesting interconnections. Part III:Advanced topics.This part is largely devoted to developments since the late 1980s.It includes average case complexity,derandomization and pseudorandomness,the PCP theorem and hardness of approximation,proof complexity and quantum computing. Almost every chapter in the book can be read in isolation (though we recommend reading Chapters 1,2 and 7 before reading later chapters).This is important because the book is aimed Web draft2007-01-0821:59 in Complexity Theory:A Modern Approach.C2006 Sanjeev Arora and Boaz Barak.References and attributions are still incomplete

DRAFT About this book Computational complexity theory has developed rapidly in the past three decades. The list of surprising and fundamental results proved since 1990 alone could fill a book: these include new probabilistic definitions of classical complexity classes (IP = PSPACE and the PCP Theorems) and their implications for the field of approximation algorithms; Shor’s algorithm to factor integers using a quantum computer; an understanding of why current approaches to the famous P versus NP will not be successful; a theory of derandomization and pseudorandomness based upon computational hardness; and beautiful constructions of pseudorandom objects such as extractors and expanders. This book aims to describe such recent achievements of complexity theory in the context of the classical results. It is intended to both serve as a textbook as a reference for self-study. This means it must simultaneously cater to many audiences, and it is carefully designed with that goal. Throughout the book we explain the context in which a certain notion is useful, and why things are defined in a certain way. Examples and solved exercises accompany key definitions. We assume essentially no computational background and very minimal mathematical background, which we review in Appendix A. We have also provided a web site for this book at http://www.cs.princeton.edu/theory/complexity/ with related auxiliary material. This includes web chapters on automata and computability theory, detailed teaching plans for courses based on this book, a draft of all the book’s chapters, and links to other online resources covering related topics. The book is divided into three parts: Part I: Basic complexity classes. This volume provides a broad introduction to the field. Starting from the definition of Turing machines and the basic notions of computability theory, this volumes covers the basic time and space complexity classes, and also includes a few more modern topics such probabilistic algorithms, interactive proofs and cryptography. Part II: Lower bounds on concrete computational models. This part describes lower bounds on resources required to solve algorithmic tasks on concrete models such as circuits, decision trees, etc. Such models may seem at first sight very different from Turing machines, but looking deeper one finds interesting interconnections. Part III: Advanced topics. This part is largely devoted to developments since the late 1980s. It includes average case complexity, derandomization and pseudorandomness, the PCP theorem and hardness of approximation, proof complexity and quantum computing. Almost every chapter in the book can be read in isolation (though we recommend reading Chapters 1, 2 and 7 before reading later chapters). This is important because the book is aimed Web draft 2007-01-08 21:59 Complexity Theory: A Modern Approach. © 2006 Sanjeev Arora and Boaz Barak. References and attributions are still incomplete. iii

iv at many classes of readers: Physicists,mathematicians,and other scientists.This group has become increasingly inter- ested in computational complexity theory,especially because of high-profile results such as Shor's algorithm and the recent deterministic test for primality.This intellectually sophisti- cated group will be able to quickly read through Part I.Progressing on to Parts II and III they can read individual chapters and find almost everything they need to understand current research. Computer scientists (e.g.,algorithms designers)who do not work in complerity theory per se. They may use the book for self-study or even to teach a graduate course or seminar. All those-professors or students-who do research in complerity theory or plan to do so. They may already know Part I and use the book for Parts II and III,possibly in a seminar or reading course.The coverage of advanced topics there is detailed enough to allow this. This book can be used as a textbook for several types of courses.We will provide several teaching plans and material for such courses on the book's web site. Undergraduate Theory of Computation Course.Part I may be suitable for an undergraduate course that is an alternative to the more traditional Theory of Computation course currently taught in most computer science departments (and exemplified by Sipser's excellent book with the same name [SIP96]).Such a course would have a greater emphasis on modern topics such as probabilistic algorithms and cryptography.We note that in contrast to Sipser's book, the current book has a quite minimal coverage of computability and no coverage of automata theory,but we provide web-only chapters with more coverage of these topics on the book's web site.The prerequisite mathematical background would be some comfort with mathematical proofs and elementary probability on finite sample spaces,topics that are covered in typical “discrete math”/“math for CS”courses currently offered in most CS departments. Advanced undergraduate/beginning graduate introduction to complerity course.The book can be used as a text for an introductory complexity course aimed at undergraduate or non-theory graduate students (replacing Papadimitriou's 1994 book [Pap94]that does not contain many recent results).Such a course would probably include many topics from Part I and then a sprinkling from Parts II and III,and assume some background in algorithms and/or the theory of computation. Graduate Complerity course.The book can serve as a text for a graduate complexity course that prepares graduate students interested in theory to do research in complexity and related areas.Such a course can use parts of Part I to review basic material,and then move on to the advanced topics of Parts II and III.The book contains far more material than can be taught in one term,and we provide on our website several alternative outlines for such a course We hope that this book conveys our excitement about this new field and the insights it provides in a host of older disciplines. DRAFT Web draft2007-01-0821:59

DRAFT iv at many classes of readers: • Physicists, mathematicians, and other scientists. This group has become increasingly interested in computational complexity theory, especially because of high-profile results such as Shor’s algorithm and the recent deterministic test for primality. This intellectually sophisticated group will be able to quickly read through Part I. Progressing on to Parts II and III they can read individual chapters and find almost everything they need to understand current research. • Computer scientists (e.g., algorithms designers) who do not work in complexity theory per se. They may use the book for self-study or even to teach a graduate course or seminar. • All those —professors or students— who do research in complexity theory or plan to do so. They may already know Part I and use the book for Parts II and III, possibly in a seminar or reading course. The coverage of advanced topics there is detailed enough to allow this. This book can be used as a textbook for several types of courses. We will provide several teaching plans and material for such courses on the book’s web site. • Undergraduate Theory of Computation Course. Part I may be suitable for an undergraduate course that is an alternative to the more traditional Theory of Computation course currently taught in most computer science departments (and exemplified by Sipser’s excellent book with the same name [SIP96]). Such a course would have a greater emphasis on modern topics such as probabilistic algorithms and cryptography. We note that in contrast to Sipser’s book, the current book has a quite minimal coverage of computability and no coverage of automata theory, but we provide web-only chapters with more coverage of these topics on the book’s web site. The prerequisite mathematical background would be some comfort with mathematical proofs and elementary probability on finite sample spaces, topics that are covered in typical “discrete math”/“math for CS” courses currently offered in most CS departments. • Advanced undergraduate/beginning graduate introduction to complexity course. The book can be used as a text for an introductory complexity course aimed at undergraduate or non-theory graduate students (replacing Papadimitriou’s 1994 book [Pap94] that does not contain many recent results). Such a course would probably include many topics from Part I and then a sprinkling from Parts II and III, and assume some background in algorithms and/or the theory of computation. • Graduate Complexity course. The book can serve as a text for a graduate complexity course that prepares graduate students interested in theory to do research in complexity and related areas. Such a course can use parts of Part I to review basic material, and then move on to the advanced topics of Parts II and III. The book contains far more material than can be taught in one term, and we provide on our website several alternative outlines for such a course. We hope that this book conveys our excitement about this new field and the insights it provides in a host of older disciplines. Web draft 2007-01-08 21:59

DRAFT Contents About this book iii Introduction p0.1 (1) I Basic Complexity Classes p0.9 (9) 1 The computational model —and why it doesn’t matter p1.1 (11) 1.1 Encodings and Languages: Some conventions . . . . . . . . . . . . . . . . . . . . . . p1.2 (12) 1.1.1 Representing objects as strings . . . . . . . . . . . . . . . . . . . . . . . . . . p1.2 (12) 1.1.2 Decision problems / languages . . . . . . . . . . . . . . . . . . . . . . . . . . p1.3 (13) 1.1.3 Big-Oh notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.3 (13) 1.2 Modeling computation and efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.4 (14) 1.2.1 The Turing Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.5 (15) 1.2.2 Robustness of our definition. . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.9 (19) 1.2.3 The expressive power of Turing machines. . . . . . . . . . . . . . . . . . . . . p1.12 (22) 1.3 Machines as strings and the universal Turing machines. . . . . . . . . . . . . . . . . p1.12 (22) 1.3.1 The Universal Turing Machine . . . . . . . . . . . . . . . . . . . . . . . . . . p1.13 (23) 1.4 Uncomputable functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.15 (25) 1.4.1 The Halting Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.15 (25) 1.5 Deterministic time and the class P. . . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.17 (27) 1.5.1 On the philosophical importance of P . . . . . . . . . . . . . . . . . . . . . . p1.17 (27) 1.5.2 Criticisms of P and some efforts to address them . . . . . . . . . . . . . . . . p1.18 (28) 1.5.3 Edmonds’ quote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.19 (29) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.20 (30) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p1.21 (31) 1.A Proof of Theorem 1.13: Universal Simulation in O(T log T)-time . . . . . . . . . . . p1.25 (35) 2 NP and NP completeness p2.1 (39) 2.1 The class NP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.2 (40) 2.1.1 Relation between NP and P . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.3 (41) 2.1.2 Non-deterministic Turing machines. . . . . . . . . . . . . . . . . . . . . . . . p2.4 (42) 2.2 Reducibility and NP-completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.5 (43) Web draft 2007-01-08 21:59 Complexity Theory: A Modern Approach. © 2006 Sanjeev Arora and Boaz Barak. References and attributions are still incomplete. v

vi CONTENTS 2.3 The Cook-Levin Theorem:Computation is Local.··...·.. p2.6(44) 2.3.1 Boolean formulae and the CNF form..·········: p2.7(45) 2.3.2 The Cook-Levin Theorem...... p2.7(45) 2.3.3 Warmup:Expressiveness of boolean formulae p2.8(46) 2.3.4 Proof of Lemma2.12...................... .p2.9(47) 2.3.5 Reducing SAT to 3SAT. .p2.11(49) 2.3.6 More thoughts on the Cook-Levin theorem p2.11(49) 2.4 The web of reductions.......················· p2.12(50) n praise of reductions.·.·················· p2.16(54) Coping with NP hardness. p2.16(54) 2.5 Decision versus search .p2.17(55) 2.6 coNP,EXP and NEXP .p2.18(56) 2.6.1 coNP p2.18(56) 2.6.2 EXP and NEXP p2.19(57) 2.7 More thoughts about P,NP,and all that . p2.20(58) 2.7.1 The philosophical importance of NP p2.20(58) 2.7.2 NP and mathematical proofs p2.20(58) 2.7.3 What if P=NP?...... .p2.21(59) 2.7.4 What if NP coNP? .p2.21(59) Chapter notes and history.····· p2.22(60) Exercises p2.23(61) 3 Diagonalization p3.1(65) 3.1 Time Hierarchy Theorem ····p3.2(66) 3.2 Space Hierarchy Theorem.........··. p3.2(66) 3.3 Nondeterministic Time Hierarchy Theorem p3.3(67) 3.4 Ladner's Theorem:Existence of NP-intermediate problems.·..··.·· p3.4(68) 3.5 Oracle machines and the limits of diagonalization? .p3.6(70) Chapter notes and history p3.8(72) Exercises········ .p3.9(73) 4 Space complexity p4.1(75) 4.1 Configuration graphs. ..·p4.2(76) 4.2 Some space complexity classes.····· p4.4(78) 4.3 PSPACE completeness..··... p4.5(79) 4.3.1 Savitch's theorem. p4.8(82) 4.3.2 The essence of PSPACE:optimum strategies for game-playing. .p4.8(82) 4.4 NL completeness.:。···················· 。 .p4.10(84) 4.4.1 Certificate definition of NL:read-once certificates..... p4.12(86) 4.42NL=coNL..··.··.···…··············· .p4.13(87) Chapter notes and history............···. ·p4.14(88) Exercises ···············p4.14(88) Web draft2007-01-0821:59

DRAFT vi CONTENTS 2.3 The Cook-Levin Theorem: Computation is Local . . . . . . . . . . . . . . . . . . . . p2.6 (44) 2.3.1 Boolean formulae and the CNF form. . . . . . . . . . . . . . . . . . . . . . . p2.7 (45) 2.3.2 The Cook-Levin Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.7 (45) 2.3.3 Warmup: Expressiveness of boolean formulae . . . . . . . . . . . . . . . . . . p2.8 (46) 2.3.4 Proof of Lemma 2.12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.9 (47) 2.3.5 Reducing SAT to 3SAT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.11 (49) 2.3.6 More thoughts on the Cook-Levin theorem . . . . . . . . . . . . . . . . . . . p2.11 (49) 2.4 The web of reductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.12 (50) In praise of reductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.16 (54) Coping with NP hardness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.16 (54) 2.5 Decision versus search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.17 (55) 2.6 coNP, EXP and NEXP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.18 (56) 2.6.1 coNP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.18 (56) 2.6.2 EXP and NEXP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.19 (57) 2.7 More thoughts about P, NP, and all that . . . . . . . . . . . . . . . . . . . . . . . . p2.20 (58) 2.7.1 The philosophical importance of NP . . . . . . . . . . . . . . . . . . . . . . . p2.20 (58) 2.7.2 NP and mathematical proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.20 (58) 2.7.3 What if P = NP? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.21 (59) 2.7.4 What if NP = coNP? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.21 (59) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.22 (60) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p2.23 (61) 3 Diagonalization p3.1 (65) 3.1 Time Hierarchy Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p3.2 (66) 3.2 Space Hierarchy Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p3.2 (66) 3.3 Nondeterministic Time Hierarchy Theorem . . . . . . . . . . . . . . . . . . . . . . . p3.3 (67) 3.4 Ladner’s Theorem: Existence of NP-intermediate problems. . . . . . . . . . . . . . . p3.4 (68) 3.5 Oracle machines and the limits of diagonalization? . . . . . . . . . . . . . . . . . . . p3.6 (70) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p3.8 (72) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p3.9 (73) 4 Space complexity p4.1 (75) 4.1 Configuration graphs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p4.2 (76) 4.2 Some space complexity classes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p4.4 (78) 4.3 PSPACE completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p4.5 (79) 4.3.1 Savitch’s theorem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p4.8 (82) 4.3.2 The essence of PSPACE: optimum strategies for game-playing. . . . . . . . p4.8 (82) 4.4 NL completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p4.10 (84) 4.4.1 Certificate definition of NL: read-once certificates . . . . . . . . . . . . . . . p4.12 (86) 4.4.2 NL = coNL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p4.13 (87) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p4.14 (88) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p4.14 (88) Web draft 2007-01-08 21:59

CONTENTS vii 5 The Polynomial Hierarchy and Alternations p5.1(91) 5.1 The classes >2 and........·..·· ....p5.1(91) 5.2 The polynomial hierarchy....... 。。 p5.3(93) 5.2.1 Properties of the polynomial hierarchy. p5.3(93) 5.2.2 Complete problems for levels of PH .p5.4(94) 5.3 Alternating Turing machines ·p5.5(95) 5.3.1 Unlimited number of alternations? p5.6(96) 5.4 Time versus alternations:time-space tradeoffs for SAT.. p5.6(96) 5.5 Defining the hierarchy via oracle machines. .p5.8(98) Chapter notes and history.············ p5.9(99) Exercises p5.10(100) 6 Circuits p6.1(101) 6.1 Boolean circuits.....··.······· ··..p6.1(101) 6.1.1 Turing machines that take advice p6.5(105) 6.2 Karp-Lipton Theorem p6.6(106 6.3 Circuit lowerbounds 。。 p6.7(107) 6.4 Non-uniform hierarchy theorem ... p6.8(108) 6.5 Finer gradations among circuit classes p6.8(108) 6.5.1 Parallel computation and NC p6.9(109) 6.5.2P-completeness.·..······. .p6.10(110) 6.6 Circuits of exponential size 。，。。 p6.11(111) 6.7 Circuit Satisfiability and an alternative proof of the Cook-Levin Theorem.... p6.12(112) Chapter notes and history,································ p6.13(113) Exercises p6.13(113) 7 Randomized Computation p7.1(115) 7.1 Probabilistic Turing machines . ··.·p7.2(116) 7.2 Some examples of PTMs.............··.. p7.3(117) 7.2.1 Probabilistic Primality Testing....··.·. p7.3(117）) 7.2.2 Polynomial identity testing..·.··.·.·- p7.4(118 7.2.3 Testing for perfect matching in a bipartite graph. p7.5(119) 7.3 One-sided and zero-sided error:RP,coRP,ZPP p7.6(120) 7.4 The robustness of our definitions.....········· p7.7(121) 7.4.1 Role of precise constants,error reduction. .p7.7(121) 7.4.2 Expected running time versus worst-case running time..···. ·p7.10(124) 7.4.3 Allowing more general random choices than a fair random coin.... .p7.10(124) 7.5 Randomness efficient error reduction.. ,p7.11(125) 7.6 BPP C P/poly .p7.12(126) 7.7 BPP is in PH .p7.13(127) 7.8 State of our knowledge about BPP......... p7.14(128) Complete problems for BPP?...... ..p7.14(128) Does BPTIME have a hierarchy theorem?··············· .p7.15(129) Wab.draft2007-01-0821:59

DRAFT CONTENTS vii 5 The Polynomial Hierarchy and Alternations p5.1 (91) 5.1 The classes Σ p 2 and Π p 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p5.1 (91) 5.2 The polynomial hierarchy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p5.3 (93) 5.2.1 Properties of the polynomial hierarchy. . . . . . . . . . . . . . . . . . . . . . . p5.3 (93) 5.2.2 Complete problems for levels of PH . . . . . . . . . . . . . . . . . . . . . . . p5.4 (94) 5.3 Alternating Turing machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p5.5 (95) 5.3.1 Unlimited number of alternations? . . . . . . . . . . . . . . . . . . . . . . . . p5.6 (96) 5.4 Time versus alternations: time-space tradeoffs for SAT. . . . . . . . . . . . . . . . . . p5.6 (96) 5.5 Defining the hierarchy via oracle machines. . . . . . . . . . . . . . . . . . . . . . . . p5.8 (98) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p5.9 (99) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p5.10 (100) 6 Circuits p6.1 (101) 6.1 Boolean circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p6.1 (101) 6.1.1 Turing machines that take advice . . . . . . . . . . . . . . . . . . . . . . . . . p6.5 (105) 6.2 Karp-Lipton Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p6.6 (106) 6.3 Circuit lowerbounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p6.7 (107) 6.4 Non-uniform hierarchy theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p6.8 (108) 6.5 Finer gradations among circuit classes . . . . . . . . . . . . . . . . . . . . . . . . . . p6.8 (108) 6.5.1 Parallel computation and NC . . . . . . . . . . . . . . . . . . . . . . . . . . . p6.9 (109) 6.5.2 P-completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p6.10 (110) 6.6 Circuits of exponential size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p6.11 (111) 6.7 Circuit Satisfiability and an alternative proof of the Cook-Levin Theorem . . . . . . p6.12 (112) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p6.13 (113) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p6.13 (113) 7 Randomized Computation p7.1 (115) 7.1 Probabilistic Turing machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.2 (116) 7.2 Some examples of PTMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.3 (117) 7.2.1 Probabilistic Primality Testing . . . . . . . . . . . . . . . . . . . . . . . . . . p7.3 (117) 7.2.2 Polynomial identity testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.4 (118) 7.2.3 Testing for perfect matching in a bipartite graph. . . . . . . . . . . . . . . . . p7.5 (119) 7.3 One-sided and zero-sided error: RP, coRP, ZPP . . . . . . . . . . . . . . . . . . . p7.6 (120) 7.4 The robustness of our definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.7 (121) 7.4.1 Role of precise constants, error reduction. . . . . . . . . . . . . . . . . . . . . p7.7 (121) 7.4.2 Expected running time versus worst-case running time. . . . . . . . . . . . . p7.10 (124) 7.4.3 Allowing more general random choices than a fair random coin. . . . . . . . . p7.10 (124) 7.5 Randomness efficient error reduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.11 (125) 7.6 BPP ⊆ P/poly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.12 (126) 7.7 BPP is in PH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.13 (127) 7.8 State of our knowledge about BPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.14 (128) Complete problems for BPP? . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.14 (128) Does BPTIME have a hierarchy theorem? . . . . . . . . . . . . . . . . . . . p7.15 (129) Web draft 2007-01-08 21:59

vi进i CONTENTS 7.9 Randomized reductions ··...p7.15(129) 7.10 Randomized space-bounded computation .·..·p7.15(129) Chapter notes and history....··..····· p7.17(131) Exercises。。·····················“ p7.18(132) 7.A Random walks and eigenvalues .p7.21(135) 7.A.1 Distributions as vectors and the parameter A(G).·.·.····. .p7.21(135) 7.A.2 Analysis of the randomized algorithm for undirected connectivity. .p7.24(138) 7 B Expander graphs...·..··.······················· .p7.25(139) 7.B.1 The Algebraic Definition.. .p7.25(139) 7.B.2 Combinatorial expansion and existence of expanders.··.····· p7.27(141) 7.B.3 Error reduction using expanders..·..·...·..·..··.·· p7.29(143) 8 Interactive proofs p8.1(147) 8.1 Warmup:Interactive proofs with a deterministic verifier........ p8.1(147) 8.2 The class IP p8.3(149) 8.3 Proving that graphs are not isomorphic.···.··. p8.4(150) 8.4 Public coins and AM................. p8.5(151) 8.4.1 Set Lower Bound Protocol. p8.6(152) Tool:Pairwise independent hash functions. p8.7(153) The lower-bound protocol...·......·····..·····. p8.9(155) 8.4.2 Some properties of IP and AM.................. ·p8.10(156) 8.4.3 Can Gl be NP-complete? p8.11(157) 8.5IP=PSPACE········· .p8.11(157) 8.5.1 Arithmetization..·.· p8.12(158) 8.5.2 Interactive protocol for #SATp p8.12(158) Sumcheck protocol............. p8.13(159) 8.5.3 Protocol for TQBF:proof of Theorem 8.17 p8.14(160) 8.6 The power of the prover .............. p8.15(161) 8.7 Program Checking············· p8.16(162) 8.7.1 Languages that have checkers p8.17(163) 8.8 Multiprover interactive proofs (MIP) .p8.18(164) Chapter notes and history......... .p8.19(165) Exercises ... .p8.20(166) 8.A Interactive proof for the Permanent. .p8.21(167) 8.A.1 The protocol..··.··.·· .p8.23(169) 9 Complexity of counting p9.1(171) 9.1 The class#P.·.· ···p9.2(172) 9.l.1 The class PP:decision-problem analog for#P...··.·. .p9.3(173) 9.2 #P completeness.···· ..p9.4(174) 9.2.1 Permanent and Valiant's Theorem........... .....p9.4(174) 9.2.2 Approximate solutions to#P problems··.··..··.······.·..·p9.8(178) 9.3 Toda's Theorem:PHC P#SAT 。·····························p99(179) Web draft2007-01-0821:59

DRAFT viii CONTENTS 7.9 Randomized reductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.15 (129) 7.10 Randomized space-bounded computation . . . . . . . . . . . . . . . . . . . . . . . . p7.15 (129) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.17 (131) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.18 (132) 7.A Random walks and eigenvalues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.21 (135) 7.A.1 Distributions as vectors and the parameter λ(G). . . . . . . . . . . . . . . . . p7.21 (135) 7.A.2 Analysis of the randomized algorithm for undirected connectivity. . . . . . . p7.24 (138) 7.B Expander graphs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.25 (139) 7.B.1 The Algebraic Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7.25 (139) 7.B.2 Combinatorial expansion and existence of expanders. . . . . . . . . . . . . . . p7.27 (141) 7.B.3 Error reduction using expanders. . . . . . . . . . . . . . . . . . . . . . . . . . p7.29 (143) 8 Interactive proofs p8.1 (147) 8.1 Warmup: Interactive proofs with a deterministic verifier . . . . . . . . . . . . . . . . p8.1 (147) 8.2 The class IP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.3 (149) 8.3 Proving that graphs are not isomorphic. . . . . . . . . . . . . . . . . . . . . . . . . . p8.4 (150) 8.4 Public coins and AM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.5 (151) 8.4.1 Set Lower Bound Protocol. . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.6 (152) Tool: Pairwise independent hash functions. . . . . . . . . . . . . . . . . . . . p8.7 (153) The lower-bound protocol. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.9 (155) 8.4.2 Some properties of IP and AM . . . . . . . . . . . . . . . . . . . . . . . . . . p8.10 (156) 8.4.3 Can GI be NP-complete? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.11 (157) 8.5 IP = PSPACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.11 (157) 8.5.1 Arithmetization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.12 (158) 8.5.2 Interactive protocol for #SATD . . . . . . . . . . . . . . . . . . . . . . . . . . p8.12 (158) Sumcheck protocol. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.13 (159) 8.5.3 Protocol for TQBF: proof of Theorem 8.17 . . . . . . . . . . . . . . . . . . . p8.14 (160) 8.6 The power of the prover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.15 (161) 8.7 Program Checking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.16 (162) 8.7.1 Languages that have checkers . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.17 (163) 8.8 Multiprover interactive proofs (MIP) . . . . . . . . . . . . . . . . . . . . . . . . . . p8.18 (164) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.19 (165) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.20 (166) 8.A Interactive proof for the Permanent . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.21 (167) 8.A.1 The protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8.23 (169) 9 Complexity of counting p9.1 (171) 9.1 The class #P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p9.2 (172) 9.1.1 The class PP: decision-problem analog for #P. . . . . . . . . . . . . . . . . . p9.3 (173) 9.2 #P completeness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p9.4 (174) 9.2.1 Permanent and Valiant’s Theorem . . . . . . . . . . . . . . . . . . . . . . . . p9.4 (174) 9.2.2 Approximate solutions to #P problems . . . . . . . . . . . . . . . . . . . . . p9.8 (178) 9.3 Toda’s Theorem: PH ⊆ P#SAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p9.9 (179) Web draft 2007-01-08 21:59

CONTENTS i议 9.3.1 The class P and hardness of satisfiability with unique solutions. ······p9.9(179) Proof of Theorem9.l5.·..·.···. p9.11(181) 9.3.2Step1:Randomized reduction from PH to⊕P.....·...·.·..·. p9.11(181) 9.3.3 Step 2:Making the reduction deterministic........... .p9.13(183) 9.4 Open Problems......·.....··.··.· .p9.14(184 Chapter notes and history ..... p9.14(184) Exercises......··. p9.15(185) 10 Cryptography p10.1(187） 10.1 Hard-on-average problems and one-way functions .. ···.·p10.2(188) l0.l.1 Discussion of the definition of one-way function,········ p10.4(190) l0.l.2 Random self-reducibility.............·.··..··.· p10.5(191) l0.2 What is a random-enough string？..·...·. p10.5(191) 10.2.1 Blum-Micali and Yao definitions p10.6(192 10.2.2 Equivalence of the two definitions........ .p10.8(194) 10.3 One-way functions and pseudorandom number generators p10.10(196 l0.3.1 Goldreich-Levin hardcore bit.............·.···· p10.10(196) 10.3.2 Pseudorandom number generation p10.13(199) l0.4 Applications.·················· .p10.13(199 10.4.1 Pseudorandom functions.... p10.13(199) 10.4.2 Private-key encryption:definition of security p10.14(200) l0.4.3 Derandomization..............·.·.····.··· p10.15(201) 10.4.4 Tossing coins over the phone and bit commitment p10.16(202) l0.4.5 Secure multiparty computations.·.....···.··. p10.16(202) 10.4.6 Lowerbounds for machine learning ,p10.17(203) 10.5 Recent developments..··.·····.· p10.17(203) Chapter notes and history p10.17(203) Exercises .p10.18(204) II Lowerbounds for Concrete Computational Models p10.21(207) 11 Decision Trees p11.2(211) 11.1 Certificate Complexity ... ·..·p11.4(213) 11.2 Randomized Decision Trees p11.6(215) 11.3 Lowerbounds on Randomized Complexity p11.6(215) 11.4 Some techniques for decision tree lowerbounds. p11.8(217) 11.5 Comparison trees and sorting lowerbounds... p11.9(218) ll.6Yao's MinMax Lemma.·.········· .p11.9(218) Exercises p11.9(218) Chapter notes and history p11.10(219) Web draft2007-01-0821:59

DRAFT CONTENTS ix 9.3.1 The class ⊕P and hardness of satisfiability with unique solutions. . . . . . . p9.9 (179) Proof of Theorem 9.15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p9.11 (181) 9.3.2 Step 1: Randomized reduction from PH to ⊕P . . . . . . . . . . . . . . . . . p9.11 (181) 9.3.3 Step 2: Making the reduction deterministic . . . . . . . . . . . . . . . . . . . p9.13 (183) 9.4 Open Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p9.14 (184) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p9.14 (184) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p9.15 (185) 10 Cryptography p10.1 (187) 10.1 Hard-on-average problems and one-way functions . . . . . . . . . . . . . . . . . . . . p10.2 (188) 10.1.1 Discussion of the definition of one-way function . . . . . . . . . . . . . . . . . p10.4 (190) 10.1.2 Random self-reducibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p10.5 (191) 10.2 What is a random-enough string? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p10.5 (191) 10.2.1 Blum-Micali and Yao definitions . . . . . . . . . . . . . . . . . . . . . . . . . p10.6 (192) 10.2.2 Equivalence of the two definitions . . . . . . . . . . . . . . . . . . . . . . . . . p10.8 (194) 10.3 One-way functions and pseudorandom number generators . . . . . . . . . . . . . . . p10.10 (196) 10.3.1 Goldreich-Levin hardcore bit . . . . . . . . . . . . . . . . . . . . . . . . . . . p10.10 (196) 10.3.2 Pseudorandom number generation . . . . . . . . . . . . . . . . . . . . . . . . p10.13 (199) 10.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p10.13 (199) 10.4.1 Pseudorandom functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p10.13 (199) 10.4.2 Private-key encryption: definition of security . . . . . . . . . . . . . . . . . . p10.14 (200) 10.4.3 Derandomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p10.15 (201) 10.4.4 Tossing coins over the phone and bit commitment . . . . . . . . . . . . . . . p10.16 (202) 10.4.5 Secure multiparty computations . . . . . . . . . . . . . . . . . . . . . . . . . p10.16 (202) 10.4.6 Lowerbounds for machine learning . . . . . . . . . . . . . . . . . . . . . . . . p10.17 (203) 10.5 Recent developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p10.17 (203) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p10.17 (203) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p10.18 (204) II Lowerbounds for Concrete Computational Models p10.21 (207) 11 Decision Trees p11.2 (211) 11.1 Certificate Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p11.4 (213) 11.2 Randomized Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p11.6 (215) 11.3 Lowerbounds on Randomized Complexity . . . . . . . . . . . . . . . . . . . . . . . . p11.6 (215) 11.4 Some techniques for decision tree lowerbounds . . . . . . . . . . . . . . . . . . . . . . p11.8 (217) 11.5 Comparison trees and sorting lowerbounds . . . . . . . . . . . . . . . . . . . . . . . . p11.9 (218) 11.6 Yao’s MinMax Lemma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p11.9 (218) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p11.9 (218) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p11.10 (219) Web draft 2007-01-08 21:59

X CONTENTS 12 Communication Complexity p12.1(221) 12.1 Definition........··. ···.·p12.1(221) 12.2 Lowerbound methods.··. p12.2(222) 12.2.1 Fooling set p12.2(222) 12.2.2 The tiling lowerbound p12.3(223 12.2.3 Rank lowerbound..... ,p12.4(224) 12.2.4 Discrepancy...···· .p12.5(225) A technique for upperbounding the discrepancy. ,p12.6(226) 12.2.5 Comparison of the lowerbound methods·········· p12.7(227) 12.3 Multiparty communication complexity p12.8(228) Discrepancy-based lowerbound p12.9(229) 12.4 Probabilistic Communication Complexity p12.10(230) 12.5 Overview of other communication models p12.10(230) 12.6 Applications of communication complexity p12.11(231) Exercises p12.11(231) Chapter notes and history p12.12(232) 13 Circuit lowerbounds p13.1(235) 13.1 ACo and Hastad's Switching Lemma..... p13.1(235) 13.1.1 The switching lemma p13.2(236) 13.1.2 Proof of the switching lemma (Lemma 13.2) .p13.3(237) l3.2 Circuits With“Counters”:ACC... p13.5(239) 13.3 Lowerbounds for monotone circuits. p13.8(242) 13.3.1 Proving Theorem13.9····· p13.8(242) Clique Indicators..···.······· p13.8(242) Approximation by clique indicators. p13.9(243) 13.4 Circuit complexity:The frontier p13.11(245) 13.4.1 Circuit lowerbounds using diagonalization... p13.11(245) 13.4.2 Status of ACC versus P....... p13.12(246) 13.4.3 Linear Circuits With Logarithmic Depth p13.13(247) 13.4.4 Branching Programs p13.13(247) 13.5 Approaches using communication complexity 。 p13.14(248) 13.5.1 Connection to ACCo Circuits.... p13.14(248) 13.5.2 Connection to Linear Size Logarithmic Depth Circuits p13.15(249) l3.5.3 Connection to branching programs··.·····. p13.15(249) 13.5.4 Karchmer-Wigderson communication games and depth lowerbounds p13.15(249) Chapter notes and history······························· p13.17(251) Exercises 。。。。 p13.18(252) 14 Algebraic computation models p14.1(255) 14.1 Algebraic circuits........ ··.··p14.2(256) 14.2 Algebraic Computation Trees ......p14.4(258) 14.3 The Blum-Shub-Smale Model ···············p14.8(262) Web draft2007-01-0821:59

DRAFT x CONTENTS 12 Communication Complexity p12.1 (221) 12.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p12.1 (221) 12.2 Lowerbound methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p12.2 (222) 12.2.1 Fooling set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p12.2 (222) 12.2.2 The tiling lowerbound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p12.3 (223) 12.2.3 Rank lowerbound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p12.4 (224) 12.2.4 Discrepancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p12.5 (225) A technique for upperbounding the discrepancy . . . . . . . . . . . . . . . . . p12.6 (226) 12.2.5 Comparison of the lowerbound methods . . . . . . . . . . . . . . . . . . . . . p12.7 (227) 12.3 Multiparty communication complexity . . . . . . . . . . . . . . . . . . . . . . . . . . p12.8 (228) Discrepancy-based lowerbound . . . . . . . . . . . . . . . . . . . . . . . . . . p12.9 (229) 12.4 Probabilistic Communication Complexity . . . . . . . . . . . . . . . . . . . . . . . . p12.10 (230) 12.5 Overview of other communication models . . . . . . . . . . . . . . . . . . . . . . . . p12.10 (230) 12.6 Applications of communication complexity . . . . . . . . . . . . . . . . . . . . . . . . p12.11 (231) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p12.11 (231) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p12.12 (232) 13 Circuit lowerbounds p13.1 (235) 13.1 AC0 and H˚astad’s Switching Lemma . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.1 (235) 13.1.1 The switching lemma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.2 (236) 13.1.2 Proof of the switching lemma (Lemma 13.2) . . . . . . . . . . . . . . . . . . . p13.3 (237) 13.2 Circuits With “Counters”:ACC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.5 (239) 13.3 Lowerbounds for monotone circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.8 (242) 13.3.1 Proving Theorem 13.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.8 (242) Clique Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.8 (242) Approximation by clique indicators. . . . . . . . . . . . . . . . . . . . . . . . p13.9 (243) 13.4 Circuit complexity: The frontier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.11 (245) 13.4.1 Circuit lowerbounds using diagonalization . . . . . . . . . . . . . . . . . . . . p13.11 (245) 13.4.2 Status of ACC versus P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.12 (246) 13.4.3 Linear Circuits With Logarithmic Depth . . . . . . . . . . . . . . . . . . . . . p13.13 (247) 13.4.4 Branching Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.13 (247) 13.5 Approaches using communication complexity . . . . . . . . . . . . . . . . . . . . . . p13.14 (248) 13.5.1 Connection to ACC0 Circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.14 (248) 13.5.2 Connection to Linear Size Logarithmic Depth Circuits . . . . . . . . . . . . . p13.15 (249) 13.5.3 Connection to branching programs . . . . . . . . . . . . . . . . . . . . . . . . p13.15 (249) 13.5.4 Karchmer-Wigderson communication games and depth lowerbounds . . . . . p13.15 (249) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.17 (251) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p13.18 (252) 14 Algebraic computation models p14.1 (255) 14.1 Algebraic circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p14.2 (256) 14.2 Algebraic Computation Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p14.4 (258) 14.3 The Blum-Shub-Smale Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p14.8 (262) Web draft 2007-01-08 21:59

CONTENTS xi l4.3.1 Complexity Classes over the Complex Numbers...··.····. .··.p14.9(263) l4.3.2 Hilbert's Nullstellensatz......··.....·.··············· p14.10(264) 14.3.3 Decidability Questions:Mandelbrot Set............... p14.10(264) Exercises································· p14.11(265) Chapter notes and history.································· .p14.11(265) III Advanced topics p14.13(267) 15 Average Case Complexity:Levin's Theory p15.1(269) 15.1 Distributional Problems.. ·····p15.2(270) 15.1.1 Formalizations of "real-life distributions." ·····p15.3(271) l5.2 DistNP and its complete problems.......··.··.······· p15.4(272) 15.2.1 Polynomial-Time on Average p15.4(272) 15.2.2 Reductions...........·..······.······ p15.5(273) 15.2.3 Proofs using the simpler definitions................ p15.8(276) 15.3 Existence of Complete Problems p15.10(278) 15.4 Polynomial-Time Samplability p15.10(278) Exercises··· p15.11(279) Chapter notes and history...... p15.11(279) 16 Derandomization,Expanders and Extractors p16.1(281) 16.1 Pseudorandom Generators and Derandomization p16.3(283) 16.1.1 Hardness and Derandomization.......... p16.5(285) 16.2 Proof of Theorem 16.10:Nisan-Wigderson Construction p16.7(287) l6.2.1 WVarmup:two toy examples···· p16.8(288) Extending the input by one bit using Yao's Theorem. p16.8(288) Extending the input by two bits using the averaging principle. p16.9(289) Beyond two bits:.·.····················· p16.10(290) l6.2.2 The NW Construction....·.···· p16.10(290) Conditions on the set systems and function.... .p16.11(291) Putting it all together:Proof of Theorem 16.10 from Lemmas 16.18 and 16.19p16.12(292) Construction of combinatorial designs.......................p16.13(293) 16.3 Derandomization requires circuit lowerbounds.. p16.13(293) 16.4 Explicit construction of expander graphs... p16.16(296) 16.4.1 Rotation maps....... p16.17(297) 16.4.2 The matrix/path product p16.17(297) l6.4.3 The tensor product.··.···· p16.18(298) 16.4.4 The replacement product 4 .p16.19(299 16.4.5 The actual construction....... .p16.21(301) 16.5 Deterministic logspace algorithm for undirected connectivity. p16.22(302) l6.6 Weak Random Sources and Extractors..·..·..······.··.. ..p16.25(305) l66.1 Min Entropy······················· p16.25(305) l6.6.2 Statistical.distance and Extractors··················· p16.26(306) Web draft2007-01-0821:59

DRAFT CONTENTS xi 14.3.1 Complexity Classes over the Complex Numbers . . . . . . . . . . . . . . . . . p14.9 (263) 14.3.2 Hilbert’s Nullstellensatz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p14.10 (264) 14.3.3 Decidability Questions: Mandelbrot Set . . . . . . . . . . . . . . . . . . . . . p14.10 (264) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p14.11 (265) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p14.11 (265) III Advanced topics p14.13 (267) 15 Average Case Complexity: Levin’s Theory p15.1 (269) 15.1 Distributional Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p15.2 (270) 15.1.1 Formalizations of “real-life distributions.” . . . . . . . . . . . . . . . . . . . . p15.3 (271) 15.2 DistNP and its complete problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . p15.4 (272) 15.2.1 Polynomial-Time on Average . . . . . . . . . . . . . . . . . . . . . . . . . . . p15.4 (272) 15.2.2 Reductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p15.5 (273) 15.2.3 Proofs using the simpler definitions . . . . . . . . . . . . . . . . . . . . . . . . p15.8 (276) 15.3 Existence of Complete Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p15.10 (278) 15.4 Polynomial-Time Samplability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p15.10 (278) Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p15.11 (279) Chapter notes and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p15.11 (279) 16 Derandomization, Expanders and Extractors p16.1 (281) 16.1 Pseudorandom Generators and Derandomization . . . . . . . . . . . . . . . . . . . . p16.3 (283) 16.1.1 Hardness and Derandomization . . . . . . . . . . . . . . . . . . . . . . . . . . p16.5 (285) 16.2 Proof of Theorem 16.10: Nisan-Wigderson Construction . . . . . . . . . . . . . . . . p16.7 (287) 16.2.1 Warmup: two toy examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . p16.8 (288) Extending the input by one bit using Yao’s Theorem. . . . . . . . . . . . . . p16.8 (288) Extending the input by two bits using the averaging principle. . . . . . . . . p16.9 (289) Beyond two bits: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p16.10 (290) 16.2.2 The NW Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p16.10 (290) Conditions on the set systems and function. . . . . . . . . . . . . . . . . . . . p16.11 (291) Putting it all together: Proof of Theorem 16.10 from Lemmas 16.18 and 16.19p16.12 (292) Construction of combinatorial designs. . . . . . . . . . . . . . . . . . . . . . . p16.13 (293) 16.3 Derandomization requires circuit lowerbounds . . . . . . . . . . . . . . . . . . . . . . p16.13 (293) 16.4 Explicit construction of expander graphs . . . . . . . . . . . . . . . . . . . . . . . . . p16.16 (296) 16.4.1 Rotation maps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p16.17 (297) 16.4.2 The matrix/path product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p16.17 (297) 16.4.3 The tensor product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p16.18 (298) 16.4.4 The replacement product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p16.19 (299) 16.4.5 The actual construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p16.21 (301) 16.5 Deterministic logspace algorithm for undirected connectivity. . . . . . . . . . . . . . p16.22 (302) 16.6 Weak Random Sources and Extractors . . . . . . . . . . . . . . . . . . . . . . . . . . p16.25 (305) 16.6.1 Min Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p16.25 (305) 16.6.2 Statistical distance and Extractors . . . . . . . . . . . . . . . . . . . . . . . . p16.26 (306) Web draft 2007-01-08 21:59

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