MATH 411 HOMEWORK 2 SOLUTIONS 2.13.3. Let X Be a Set
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Valuative Characterization of Central Extensions of Algebraic Tori on Krull
Valuative characterization of central extensions of algebraic tori on Krull domains ∗ Haruhisa Nakajima † Department of Mathematics, J. F. Oberlin University Tokiwa-machi, Machida, Tokyo 194-0294, JAPAN Abstract Let G be an affine algebraic group with an algebraic torus G0 over an alge- braically closed field K of an arbitrary characteristic p. We show a criterion for G to be a finite central extension of G0 in terms of invariant theory of all regular 0 actions of any closed subgroup H containing ZG(G ) on affine Krull K-schemes such that invariant rational functions are locally fractions of invariant regular func- tions. Consider an affine Krull H-scheme X = Spec(R) and a prime ideal P of R with ht(P) = ht(P ∩ RH ) = 1. Let I(P) denote the inertia group of P un- der the action of H. The group G is central over G0 if and only if the fraction e(P, P ∩ RH )/e(P, P ∩ RI(P)) of ramification indices is equal to 1 (p = 0) or to the p-part of the order of the group of weights of G0 on RI(P)) vanishing on RI(P))/P ∩ RI(P) (p> 0) for an arbitrary X and P. MSC: primary 13A50, 14R20, 20G05; secondary 14L30, 14M25 Keywords: Krull domain; ramification index; algebraic group; algebraic torus; char- acter group; invariant theory 1 Introduction 1.A. We consider affine algebraic groups and affine schemes over a fixed algebraically closed field K of an arbitrary characteristic p. For an affine group G, denote by G0 its identity component. -
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/. -
Lecture 11 — October 16, 2012 1 Overview 2
6.841: Advanced Complexity Theory Fall 2012 Lecture 11 | October 16, 2012 Prof. Dana Moshkovitz Scribe: Hyuk Jun Kweon 1 Overview In the previous lecture, there was a question about whether or not true randomness exists in the universe. This question is still seriously debated by many physicists and philosophers. However, even if true randomness does not exist, it is possible to use pseudorandomness for practical purposes. In any case, randomization is a powerful tool in algorithms design, sometimes leading to more elegant or faster ways of solving problems than known deterministic methods. This naturally leads one to wonder whether randomization can solve problems outside of P, de- terministic polynomial time. One way of viewing randomized algorithms is that for each possible setting of the random bits used, a different strategy is pursued by the algorithm. A property of randomized algorithms that succeed with bounded error is that the vast majority of strategies suc- ceed. With error reduction, all but exponentially many strategies will succeed. A major theme of theoretical computer science over the last 20 years has been to turn algorithms with this property into algorithms that deterministically find a winning strategy. From the outset, this seems like a daunting task { without randomness, how can one efficiently search for such strategies? As we'll see in later lectures, this is indeed possible, modulo some hardness assumptions. There are two main objectives in this lecture. The first objective is to introduce some probabilis- tic complexity classes, by bounding time or space. These classes are probabilistic analogues of deterministic complexity classes P and L. -
R Z- /Li ,/Zo Rl Date Copyright 2012
Hearing Moral Reform: Abolitionists and Slave Music, 1838-1870 by Karl Byrn A thesis submitted to Sonoma State University in partial fulfillment of the requirements for the degree of MASTER OF ARTS III History Dr. Steve Estes r Z- /Li ,/zO rL Date Copyright 2012 By Karl Bym ii AUTHORIZATION FOR REPRODUCTION OF MASTER'S THESIS/PROJECT I grant permission for the reproduction ofparts ofthis thesis without further authorization from me, on the condition that the person or agency requesting reproduction absorb the cost and provide proper acknowledgement of authorship. Permission to reproduce this thesis in its entirety must be obtained from me. iii Hearing Moral Reform: Abolitionists and Slave Music, 1838-1870 Thesis by Karl Byrn ABSTRACT Purpose ofthe Study: The purpose of this study is to analyze commentary on slave music written by certain abolitionists during the American antebellum and Civil War periods. The analysis is not intended to follow existing scholarship regarding the origins and meanings of African-American music, but rather to treat this music commentary as a new source for understanding the abolitionist movement. By treating abolitionists' reports of slave music as records of their own culture, this study will identify larger intellectual and cultural currents that determined their listening conditions and reactions. Procedure: The primary sources involved in this study included journals, diaries, letters, articles, and books written before, during, and after the American Civil War. I found, in these sources, that the responses by abolitionists to the music of slaves and freedmen revealed complex ideologies and motivations driven by religious and reform traditions, antebellum musical cultures, and contemporary racial and social thinking. -
Lecture 13: Basis for a Topology
Lecture 13: Basis for a Topology 1 Basis for a Topology Lemma 1.1. Let (X; T) be a topological space. Suppose that C is a collection of open sets of X such that for each open set U of X and each x in U, there is an element C 2 C such that x 2 C ⊂ U. Then C is the basis for the topology of X. Proof. In order to show that C is a basis, need to show that C satisfies the two properties of basis. To show the first property, let x be an element of the open set X. Now, since X is open, then, by hypothesis there exists an element C of C such that x 2 C ⊂ X. Thus C satisfies the first property of basis. To show the second property of basis, let x 2 X and C1;C2 be open sets in C such that x 2 C1 and x 2 C2. This implies that C1 \ C2 is also an open set in C and x 2 C1 \ C2. Then, by hypothesis, there exists an open set C3 2 C such that x 2 C3 ⊂ C1 \ C2. Thus, C satisfies the second property of basis too and hence, is indeed a basis for the topology on X. On many occasions it is much easier to show results about a topological space by arguing in terms of its basis. For example, to determine whether one topology is finer than the other, it is easier to compare the two topologies in terms of their bases. -
Advance Topics in Topology - Point-Set
ADVANCE TOPICS IN TOPOLOGY - POINT-SET NOTES COMPILED BY KATO LA 19 January 2012 Background Intervals: pa; bq “ tx P R | a ă x ă bu ÓÓ , / / calc. notation set theory notation / / \Open" intervals / pa; 8q ./ / / p´8; bq / / / -/ ra; bs; ra; 8q: Closed pa; bs; ra; bq: Half-openzHalf-closed Open Sets: Includes all open intervals and union of open intervals. i.e., p0; 1q Y p3; 4q. Definition: A set A of real numbers is open if @ x P A; D an open interval contain- ing x which is a subset of A. Question: Is Q, the set of all rational numbers, an open set of R? 1 1 1 - No. Consider . No interval of the form ´ "; ` " is a subset of . We can 2 2 2 Q 2 ˆ ˙ ask a similar question in R . 2 Is R open in R ?- No, because any disk around any point in R will have points above and below that point of R. Date: Spring 2012. 1 2 NOTES COMPILED BY KATO LA Definition: A set is called closed if its complement is open. In R, p0; 1q is open and p´8; 0s Y r1; 8q is closed. R is open, thus Ø is closed. r0; 1q is not open or closed. In R, the set t0u is closed: its complement is p´8; 0q Y p0; 8q. In 2 R , is tp0; 0qu closed? - Yes. Chapter 2 - Topological Spaces & Continuous Functions Definition:A topology on a set X is a collection T of subsets of X satisfying: (1) Ø;X P T (2) The union of any number of sets in T is again, in the collection (3) The intersection of any finite number of sets in T , is again in T Alternative Definition: ¨ ¨ ¨ is a collection T of subsets of X such that Ø;X P T and T is closed under arbitrary unions and finite intersections. -
Simple Doubly-Efficient Interactive Proof Systems for Locally
Electronic Colloquium on Computational Complexity, Revision 3 of Report No. 18 (2017) Simple doubly-efficient interactive proof systems for locally-characterizable sets Oded Goldreich∗ Guy N. Rothblumy September 8, 2017 Abstract A proof system is called doubly-efficient if the prescribed prover strategy can be implemented in polynomial-time and the verifier’s strategy can be implemented in almost-linear-time. We present direct constructions of doubly-efficient interactive proof systems for problems in P that are believed to have relatively high complexity. Specifically, such constructions are presented for t-CLIQUE and t-SUM. In addition, we present a generic construction of such proof systems for a natural class that contains both problems and is in NC (and also in SC). The proof systems presented by us are significantly simpler than the proof systems presented by Goldwasser, Kalai and Rothblum (JACM, 2015), let alone those presented by Reingold, Roth- blum, and Rothblum (STOC, 2016), and can be implemented using a smaller number of rounds. Contents 1 Introduction 1 1.1 The current work . 1 1.2 Relation to prior work . 3 1.3 Organization and conventions . 4 2 Preliminaries: The sum-check protocol 5 3 The case of t-CLIQUE 5 4 The general result 7 4.1 A natural class: locally-characterizable sets . 7 4.2 Proof of Theorem 1 . 8 4.3 Generalization: round versus computation trade-off . 9 4.4 Extension to a wider class . 10 5 The case of t-SUM 13 References 15 Appendix: An MA proof system for locally-chracterizable sets 18 ∗Department of Computer Science, Weizmann Institute of Science, Rehovot, Israel. -
Glossary of Complexity Classes
App endix A Glossary of Complexity Classes Summary This glossary includes selfcontained denitions of most complexity classes mentioned in the b o ok Needless to say the glossary oers a very minimal discussion of these classes and the reader is re ferred to the main text for further discussion The items are organized by topics rather than by alphab etic order Sp ecically the glossary is partitioned into two parts dealing separately with complexity classes that are dened in terms of algorithms and their resources ie time and space complexity of Turing machines and complexity classes de ned in terms of nonuniform circuits and referring to their size and depth The algorithmic classes include timecomplexity based classes such as P NP coNP BPP RP coRP PH E EXP and NEXP and the space complexity classes L NL RL and P S P AC E The non k uniform classes include the circuit classes P p oly as well as NC and k AC Denitions and basic results regarding many other complexity classes are available at the constantly evolving Complexity Zoo A Preliminaries Complexity classes are sets of computational problems where each class contains problems that can b e solved with sp ecic computational resources To dene a complexity class one sp ecies a mo del of computation a complexity measure like time or space which is always measured as a function of the input length and a b ound on the complexity of problems in the class We follow the tradition of fo cusing on decision problems but refer to these problems using the terminology of promise problems -
User's Guide for Complexity: a LATEX Package, Version 0.80
User’s Guide for complexity: a LATEX package, Version 0.80 Chris Bourke April 12, 2007 Contents 1 Introduction 2 1.1 What is complexity? ......................... 2 1.2 Why a complexity package? ..................... 2 2 Installation 2 3 Package Options 3 3.1 Mode Options .............................. 3 3.2 Font Options .............................. 4 3.2.1 The small Option ....................... 4 4 Using the Package 6 4.1 Overridden Commands ......................... 6 4.2 Special Commands ........................... 6 4.3 Function Commands .......................... 6 4.4 Language Commands .......................... 7 4.5 Complete List of Class Commands .................. 8 5 Customization 15 5.1 Class Commands ............................ 15 1 5.2 Language Commands .......................... 16 5.3 Function Commands .......................... 17 6 Extended Example 17 7 Feedback 18 7.1 Acknowledgements ........................... 19 1 Introduction 1.1 What is complexity? complexity is a LATEX package that typesets computational complexity classes such as P (deterministic polynomial time) and NP (nondeterministic polynomial time) as well as sets (languages) such as SAT (satisfiability). In all, over 350 commands are defined for helping you to typeset Computational Complexity con- structs. 1.2 Why a complexity package? A better question is why not? Complexity theory is a more recent, though mature area of Theoretical Computer Science. Each researcher seems to have his or her own preferences as to how to typeset Complexity Classes and has built up their own personal LATEX commands file. This can be frustrating, to say the least, when it comes to collaborations or when one has to go through an entire series of files changing commands for compatibility or to get exactly the look they want (or what may be required). -
Concurrent PAC RL
Concurrent PAC RL Zhaohan Guo and Emma Brunskill Carnegie Mellon University 5000 Forbes Ave. Pittsburgh PA, 15213 United States Abstract environment, whereas we consider the different problem of one agent / decision maker simultaneously acting in mul- In many real-world situations a decision maker may make decisions across many separate reinforcement tiple environments. The critical distinction here is that the learning tasks in parallel, yet there has been very little actions and rewards taken in one task do not directly impact work on concurrent RL. Building on the efficient explo- the actions and rewards taken in any other task (unlike multi- ration RL literature, we introduce two new concurrent agent settings) but information about the outcomes of these RL algorithms and bound their sample complexity. We actions may provide useful information to other tasks, if the show that under some mild conditions, both when the tasks are related. agent is known to be acting in many copies of the same One important exception is recent work by Silver et al. MDP, and when they are not the same but are taken from a finite set, we can gain linear improvements in the (2013) on concurrent reinforcement learning when interact- sample complexity over not sharing information. This is ing with a set of customers in parallel. This work nicely quite exciting as a linear speedup is the most one might demonstrates the substantial benefit to be had by leveraging hope to gain. Our preliminary experiments confirm this information across related tasks while acting jointly in these result and show empirical benefits. -
Math 553 - Topology Todd Riggs Assignment 1 Sept 10, 2014
Math 553 - Topology Todd Riggs Assignment 1 Sept 10, 2014 Problems: Section 13 - 1, 3, 4, 5, 6; Section 16 - 1, 5, 8, 9; 13.1) Let X be a topological space and let A be a subset of X. Suppose that for each x 2 A there is an open set U containing x such that U ⊂ A. Show that A is open in X. Proof: Since for each x 2 A there exist Ux 2 T such that x 2 Ux and Ux ⊂ A, we have S A = Ux. That is, A is the union of open sets in X and thus by definition of the x2A topology on X; A is open. 13.3) Show that the collection Tc given in example 4 (pg 77) is a topology on the set X. Is the collection T1 = fUjX − U is infinite or empty or all of Xg a topology on X. Proof: PART (1) From example 4 Tc = fUjX − U is countable or is all of Xg. To show that Tc is a topology on X, we must show we meet all 3 conditions of the definition of topology. First, since X −X = ; is countable and X −; = X is all of X we have X and ; 2 Tc. Second, let Uα 2 Tc for α 2 I. We must look at two cases: (1) X − Uα = X for all α and (2) There exist α such that X − Uα is countable. Case (1): X − Uα = X S T This implies Uα = ; for all α. So X − ( Uα) = (X − Uα) = X and thus, α2I α2I S Uα 2 Tc. -
Some Examples and Counterexamples of Advanced Compactness in Topology
International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 www.ijres.org Volume 9 Issue 1 ǁ 2021 ǁ PP. 10-16 Some Examples and Counterexamples of Advanced Compactness in Topology Pankaj Goswami Department of Mathematics, University of Gour Banga, Malda, 732102, West Bengal, India ABSTRACT: The examples and counter examples are always usefull for better comprehension of underlying concept in a theorem or definition .Compactness has come to be one of the most importent and useful topic in advanced mathematics.This paper is an attempt to fill in some of the information that the standard textbook treatment of compactness leaves out, and giving some constructive significative counterexamples of Advanced Compactness in Topology . KEYWORDS: open cover, compact, connected, topological space, example, counterexamples, continuous function, locally compact, countably compact, limit point compact, sequentially compact, lindelöf , paracompact --------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: 15-01-2021 Date of acceptance: 30-01-2021 --------------------------------------------------------------------------------------------------------------------------------------- I. INTRODUCTION We begin by presenting some definitions, notations,examples and some theorems that are essential for concept of compactness in advanced topology .For more detailed, see [1-2], [7-10] and others, in references .If X is a closed bounded subset of the real line ℝ, then any family of open sets in ℝ whose union contains X has a finite sub family whose union also contains X . If X is a metric space or topological space in its own right , then the above proposition can be thought as saying that any class of open sets in X whose union is equal to X has a finite subclass whose union is also equal to X.