Supercuspidal Part of the Mod L Cohomology of GU(1,N - 1)-Shimura Varieties
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
CS601 DTIME and DSPACE Lecture 5 Time and Space Functions: T, S
CS601 DTIME and DSPACE Lecture 5 Time and Space functions: t, s : N → N+ Definition 5.1 A set A ⊆ U is in DTIME[t(n)] iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. A = L(M) ≡ w ∈ U M(w)=1 , and 2. ∀w ∈ U, M(w) halts within c · t(|w|) steps. Definition 5.2 A set A ⊆ U is in DSPACE[s(n)] iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. A = L(M), and 2. ∀w ∈ U, M(w) uses at most c · s(|w|) work-tape cells. (Input tape is “read-only” and not counted as space used.) Example: PALINDROMES ∈ DTIME[n], DSPACE[n]. In fact, PALINDROMES ∈ DSPACE[log n]. [Exercise] 1 CS601 F(DTIME) and F(DSPACE) Lecture 5 Definition 5.3 f : U → U is in F (DTIME[t(n)]) iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. f = M(·); 2. ∀w ∈ U, M(w) halts within c · t(|w|) steps; 3. |f(w)|≤|w|O(1), i.e., f is polynomially bounded. Definition 5.4 f : U → U is in F (DSPACE[s(n)]) iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. f = M(·); 2. ∀w ∈ U, M(w) uses at most c · s(|w|) work-tape cells; 3. |f(w)|≤|w|O(1), i.e., f is polynomially bounded. (Input tape is “read-only”; Output tape is “write-only”. -
On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs*
On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs* Benny Applebaum† Eyal Golombek* Abstract We study the randomness complexity of interactive proofs and zero-knowledge proofs. In particular, we ask whether it is possible to reduce the randomness complexity, R, of the verifier to be comparable with the number of bits, CV , that the verifier sends during the interaction. We show that such randomness sparsification is possible in several settings. Specifically, unconditional sparsification can be obtained in the non-uniform setting (where the verifier is modelled as a circuit), and in the uniform setting where the parties have access to a (reusable) common-random-string (CRS). We further show that constant-round uniform protocols can be sparsified without a CRS under a plausible worst-case complexity-theoretic assumption that was used previously in the context of derandomization. All the above sparsification results preserve statistical-zero knowledge provided that this property holds against a cheating verifier. We further show that randomness sparsification can be applied to honest-verifier statistical zero-knowledge (HVSZK) proofs at the expense of increasing the communica- tion from the prover by R−F bits, or, in the case of honest-verifier perfect zero-knowledge (HVPZK) by slowing down the simulation by a factor of 2R−F . Here F is a new measure of accessible bit complexity of an HVZK proof system that ranges from 0 to R, where a maximal grade of R is achieved when zero- knowledge holds against a “semi-malicious” verifier that maliciously selects its random tape and then plays honestly. -
Lecture 10: Space Complexity III
Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Lecture 10: Space Complexity III Arijit Bishnu 27.03.2010 Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Outline 1 Space Complexity Classes: NL and L 2 Reductions 3 NL-completeness 4 The Relation between NL and coNL 5 A Relation Among the Complexity Classes Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Outline 1 Space Complexity Classes: NL and L 2 Reductions 3 NL-completeness 4 The Relation between NL and coNL 5 A Relation Among the Complexity Classes Definition for Recapitulation S c NPSPACE = c>0 NSPACE(n ). The class NPSPACE is an analog of the class NP. Definition L = SPACE(log n). Definition NL = NSPACE(log n). Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Space Complexity Classes Definition for Recapitulation S c PSPACE = c>0 SPACE(n ). The class PSPACE is an analog of the class P. Definition L = SPACE(log n). Definition NL = NSPACE(log n). Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Space Complexity Classes Definition for Recapitulation S c PSPACE = c>0 SPACE(n ). The class PSPACE is an analog of the class P. Definition for Recapitulation S c NPSPACE = c>0 NSPACE(n ). -
Lecture 4: Space Complexity II: NL=Conl, Savitch's Theorem
COM S 6810 Theory of Computing January 29, 2009 Lecture 4: Space Complexity II Instructor: Rafael Pass Scribe: Shuang Zhao 1 Recall from last lecture Definition 1 (SPACE, NSPACE) SPACE(S(n)) := Languages decidable by some TM using S(n) space; NSPACE(S(n)) := Languages decidable by some NTM using S(n) space. Definition 2 (PSPACE, NPSPACE) [ [ PSPACE := SPACE(nc), NPSPACE := NSPACE(nc). c>1 c>1 Definition 3 (L, NL) L := SPACE(log n), NL := NSPACE(log n). Theorem 1 SPACE(S(n)) ⊆ NSPACE(S(n)) ⊆ DTIME 2 O(S(n)) . This immediately gives that N L ⊆ P. Theorem 2 STCONN is NL-complete. 2 Today’s lecture Theorem 3 N L ⊆ SPACE(log2 n), namely STCONN can be solved in O(log2 n) space. Proof. Recall the STCONN problem: given digraph (V, E) and s, t ∈ V , determine if there exists a path from s to t. 4-1 Define boolean function Reach(u, v, k) with u, v ∈ V and k ∈ Z+ as follows: if there exists a path from u to v with length smaller than or equal to k, then Reach(u, v, k) = 1; otherwise Reach(u, v, k) = 0. It is easy to verify that there exists a path from s to t iff Reach(s, t, |V |) = 1. Next we show that Reach(s, t, |V |) can be recursively computed in O(log2 n) space. For all u, v ∈ V and k ∈ Z+: • k = 1: Reach(u, v, k) = 1 iff (u, v) ∈ E; • k > 1: Reach(u, v, k) = 1 iff there exists w ∈ V such that Reach(u, w, dk/2e) = 1 and Reach(w, v, bk/2c) = 1. -
Complexity Theory Lectures 1–6
Complexity Theory 1 Complexity Theory Lectures 1–6 Lecturer: Dr. Timothy G. Griffin Slides by Anuj Dawar Computer Laboratory University of Cambridge Easter Term 2009 http://www.cl.cam.ac.uk/teaching/0809/Complexity/ Cambridge Easter 2009 Complexity Theory 2 Texts The main texts for the course are: Computational Complexity. Christos H. Papadimitriou. Introduction to the Theory of Computation. Michael Sipser. Other useful references include: Computers and Intractability: A guide to the theory of NP-completeness. Michael R. Garey and David S. Johnson. Structural Complexity. Vols I and II. J.L. Balc´azar, J. D´ıaz and J. Gabarr´o. Computability and Complexity from a Programming Perspective. Neil Jones. Cambridge Easter 2009 Complexity Theory 3 Outline A rough lecture-by-lecture guide, with relevant sections from the text by Papadimitriou (or Sipser, where marked with an S). Algorithms and problems. 1.1–1.3. • Time and space. 2.1–2.5, 2.7. • Time Complexity classes. 7.1, S7.2. • Nondeterminism. 2.7, 9.1, S7.3. • NP-completeness. 8.1–8.2, 9.2. • Graph-theoretic problems. 9.3 • Cambridge Easter 2009 Complexity Theory 4 Outline - contd. Sets, numbers and scheduling. 9.4 • coNP. 10.1–10.2. • Cryptographic complexity. 12.1–12.2. • Space Complexity 7.1, 7.3, S8.1. • Hierarchy 7.2, S9.1. • Descriptive Complexity 5.6, 5.7. • Cambridge Easter 2009 Complexity Theory 5 Complexity Theory Complexity Theory seeks to understand what makes certain problems algorithmically difficult to solve. In Data Structures and Algorithms, we saw how to measure the complexity of specific algorithms, by asymptotic measures of number of steps. -
An Entropy Proof of the Switching Lemma and Tight Bounds on the Decision-Tree Size of AC0
An entropy proof of the switching lemma and tight bounds on the decision-tree size of AC0 Benjamin Rossman∗ University of Toronto November 3, 2017 Abstract We first give a simple entropy argument showing that every m-clause DNF with expected value λ 2 [0; 1] under the uniform distribution has average sensitivity (a.k.a. total influence) at most 2λ log(m/λ). Using a similar idea, we then show the following switching lemma for an m-clause DNF (or CNF) formula F : P t (1) [ DTdepth(F Rp) ≥ t ] ≤ O(p log(m + 1)) : for all p 2 [0; 1] and t 2 N where Rp is the p-random restriction and DTdepth(·) denotes decision- tree depth. Our proof replaces the counting arguments in previous proofs of H˚astad's O(pw)t switching lemma for width-w DNFs [5,9,2] with an entropy argument that naturally applies to unbounded-width DNFs with a bounded number of clauses. With respect to AC0 circuits, our m-clause switching lemma has similar applications as H˚astad'swidth-w switching lemma, 1=(d−1) including a 2Ω(n ) lower bound for PARITY. An additional result of this paper extends inequality (1) to AC0 circuits via a combination of H˚astad'sswitching and multi-switching lemmas [5,6]. For boolean functions f : f0; 1gn ! f0; 1g computable by AC0 circuits of depth d and size s, we show that P d−1 t (2) [ DTdepth(fRp) ≥ t ] ≤ (p · O(log s) ) for all p 2 [0; 1] and t 2 N. -
18.405J S16 Lecture 3: Circuits and Karp-Lipton
18.405J/6.841J: Advanced Complexity Theory Spring 2016 Lecture 3: Circuits and Karp-Lipton Scribe: Anonymous Student Prof. Dana Moshkovitz Scribe Date: Fall 2012 1 Overview In the last lecture we examined relativization. We found that a common method (diagonalization) for proving non-inclusions between classes of problems was, alone, insufficient to settle the P versus NP problem. This was due to the existence of two orcales: one for which P = NP and another for which P =6 NP . Therefore, in our search for a solution, we must look into the inner workings of computation. In this lecture we will examine the role of Boolean Circuits in complexity theory. We will define the class P=poly based on the circuit model of computation. We will go on to give a weak circuit lower bound and prove the Karp-Lipton Theorem. 2 Boolean Circuits Turing Machines are, strange as it may sound, a relatively high level programming language. Because we found that diagonalization alone is not sufficient to settle the P versus NP problem, we want to look at a lower level model of computation. It may, for example, be easier to work at the bit level. To this end, we examine boolean circuits. Note that any function f : f0; 1gn ! f0; 1g is expressible as a boolean circuit. 2.1 Definitions Definition 1. Let T : N ! N.A T -size circuit family is a sequence fCng such that for all n, Cn is a boolean circuit of size (in, say, gates) at most T (n). SIZE(T ) is the class of languages with T -size circuits. -
PSPACE-Complete Languages
CSCI 1590 Intro to Computational Complexity PSPACE-Complete Languages John E. Savage Brown University February 11, 2009 John E. Savage (Brown University) CSCI 1590 Intro to Computational Complexity February 11, 2009 1 / 10 Summary 1 Complexity Class Containment 2 Polynomial Time Hierarchy 3 PH-Complete Languages 4 Games and tqbf 5 tqbf is PSPACE-Complete John E. Savage (Brown University) CSCI 1590 Intro to Computational Complexity February 11, 2009 2 / 10 Complexity Classes from Last Lecture P, coNP, p p Πi and Σi PH PSPACE NP ∪ coNP PSPACE = NPSPACE NP ∩ coNP NP coNP L2 P NL L John E. Savage (Brown University) CSCI 1590 Intro to Computational Complexity February 11, 2009 3 / 10 Polynomial Time Hierarchy A language is in NP(coNP) if and only if it can be reduced in polynomial time to a statement of the form ∃x b(x) (∀x b(x)) By adding additional levels of quantification, as shown below, potentially new complexity classes are added. ∀x1 ∃x2 b(x1, x2) ∃x1 ∀x2 b(x1, x2) The sets of languages PTIME reducible to statements of this form are p p denoted Πi and Σi respectively, when there are i alternations of existential and universal quantifiers and the outermost quantifier is ∀ and ∃, respectively. Definition The Polynomial Hierarchy (PH) is defined as p PH = [ Σi i John E. Savage (Brown University) CSCI 1590 Intro to Computational Complexity February 11, 2009 4 / 10 PH and PSPACE Definition A language L is PH-complete if a) L ∈ PH and b) all languages in PH are ptime reducible to L. It is not hard to see that PH ⊆ PSPACE. -
PSPACE-Completeness & Savitch's Theorem 1 Recap
CSE 200 Computability and Complexity Wednesday, April 24, 2013 Lecture 8: PSPACE-Completeness & Savitch's Theorem Instructor: Professor Shachar Lovett Scribe: Dongcai Shen 1 Recap: Space Complexity Recall the following definitions on space complexity we learned in the last class: SPACE(S(n))def= \languages computable by a TM where input tape is readonly and work tape size S(n)" LOGSPACEdef= SPACE(O(log n)) def c PSPACE = [c≥1 SPACE(n ) For example, nowadays, people are interested in streaming algorithms, whose space requirements are low. So researching on space complexity can have real-world influence, especially in instructing people the way an algorithm should be designed and directing people away from some impossible attempts. Definition 1 (Nondeterministic Space NSPACE) The nondeterministic space NSPACE(S(n))def= • Input tape, read-only. • Proof tape, read-only & read-once. • Work tape size S(n). So, we can define • NLdef= NSPACE(O(log n)). def c • NPSPACE = [c≥1 NSPACE(n ). def Theorem 2 PATH = f(G; s; t): G directed graph, s; t vertices, there is a path s t in Gg is NL-complete un- der logspace reduction. Corollary 3 If we could find a deterministic algorithm for PATH in SPACE(O(log n)), then NL = L. Remark. Nondeterminism is pretty useless w.r.t. space. We don't know whether NL = L yet. 2 Outline of Today Definition 4 (Quantified Boolean formula) A quantified Boolean formula is 8x19x28x39x4 ··· φ(x1; ··· ; xn) def where φ is a CNF. Let TQBF = fA true QBFg. Theorem 5 TQBF is PSPACE-complete under poly-time reduction (and actually under logspace reductions). -
The Complexity of Equivalent Classes Frank Vega
The complexity of equivalent classes Frank Vega To cite this version: Frank Vega. The complexity of equivalent classes. 2016. hal-01302609v2 HAL Id: hal-01302609 https://hal.archives-ouvertes.fr/hal-01302609v2 Preprint submitted on 16 Apr 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THE COMPLEXITY OF EQUIVALENT CLASSES FRANK VEGA Abstract. We consider two new complexity classes that are called equivalent- P and equivalent-UP which have a close relation to the P versus NP problem. The class equivalent-P contains those languages that are ordered-pairs of in- stances of two specific problems in P, such that the elements of each ordered- pair have the same solution, which means, the same certificate. The class equivalent-UP has almost the same definition, but in each case we define the pair of languages explicitly in UP. In addition, we define the class double-NP as the set of languages that contain each instance of another language in NP, but in a double way, that is, in form of a pair with two identical instances. We demonstrate that double-NP is a subset of equivalent-P. -
Comparing Complexity Classes*
JOURNAL OF COMPUTER AND SYSTEM SCIENCES 9, 213-229 (1974) Comparing Complexity Classes* RONALD V. BOOKt Center for Research in Computing Technology, Harvard University, Cambridge, Massachusetts 02138 Received January 20, 1973 Complexity classes defined by time-bounded and space-bounded Turing acceptors are studied in order to learn more about the cost of deterministic simulation of non- deterministic processes and about time-space tradeoffs. Here complexity classes are compared by means of reducibilities and class-complete sets. The classes studied are defined by bounds of the order n, n ~, 2 n, 2 n~. The results do not establish the existence of possible relationships between these classes; rather, they show the consequences of such relationships, in some cases offering circumstantial evidence that these relation- ships do not hold and that certain pairs of classes are set-theoretically incomparable. INTRODUCTION Certain long-standing open questions in automata-based complexity have resurfaced recently due to the work by Cook [9] and Karp [17] on efficient reducibilities among combinatorial problems. In particular, questions regarding time-space tradeoffs and the cost of deterministic simulation of nondeterministic machines have received renewed attention. The purpose of this paper is to study relationships between certain classes of languages accepted by time- and space-bounded Turing machines in order to learn more about these questions. The questions of time-space tradeoffs and deterministic simulation of nondeter- ministic processes can be studied on an ad hoc basis, e.g., a particular problem can be solved via a nondeterministic process and then an efficient deterministic process might be shown to realize the result. -
6.080 / 6.089 Great Ideas in Theoretical Computer Science Spring 2008
MIT OpenCourseWare http://ocw.mit.edu 6.080 / 6.089 Great Ideas in Theoretical Computer Science Spring 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 6.080/6.089 GITCS DATE Lecture 12 Lecturer: Scott Aaronson Scribe: Mergen Nachin 1 Review of last lecture • NP-completeness in practice. We discussed many of the approaches people use to cope with NP-complete problems in real life. These include brute-force search (for small instance sizes), cleverly-optimized backtrack search, fixed-parameter algorithms, approximation algorithms, and heuristic algorithms like simulated annealing (which don’t always work but often do pretty well in practice). • Factoring, as an intermediate problem between P and NP-complete. We saw how factoring, though believed to be hard for classical computers, has a special structure that makes it different from any known NP-complete problem. • coNP. 2 Space complexity Now we will categorize problems in terms of how much memory they use. Definition 1 L is in PSPACE if there exists a poly-space Turing machine M such that for all x, x is in L if and only if M(x) accepts. Just like with NP, we can define PSPACE-hard and PSPACE-complete problems. An interesting example of a PSPACE-complete problem is n-by-n chess: Given an arrangement of pieces on an n-by-n chessboard, and assuming a polynomial upper bound on the number of moves, decide whether White has a winning strategy. (Note that we need to generalize chess to an n-by-n board, since standard 8-by-8 chess is a finite game that’s completely solvable in O(1) time.) Let’s now define another complexity class called EXP, which is apparently even bigger than PSPACE.