Unification of the Nature's Complexities Via a Matrix Permanent—Critical Phenomena, Fractals, Quantum Computing, P-Complex
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Complexity Theory Lecture 9 Co-NP Co-NP-Complete
Complexity Theory 1 Complexity Theory 2 co-NP Complexity Theory Lecture 9 As co-NP is the collection of complements of languages in NP, and P is closed under complementation, co-NP can also be characterised as the collection of languages of the form: ′ L = x y y <p( x ) R (x, y) { |∀ | | | | → } Anuj Dawar University of Cambridge Computer Laboratory NP – the collection of languages with succinct certificates of Easter Term 2010 membership. co-NP – the collection of languages with succinct certificates of http://www.cl.cam.ac.uk/teaching/0910/Complexity/ disqualification. Anuj Dawar May 14, 2010 Anuj Dawar May 14, 2010 Complexity Theory 3 Complexity Theory 4 NP co-NP co-NP-complete P VAL – the collection of Boolean expressions that are valid is co-NP-complete. Any language L that is the complement of an NP-complete language is co-NP-complete. Any of the situations is consistent with our present state of ¯ knowledge: Any reduction of a language L1 to L2 is also a reduction of L1–the complement of L1–to L¯2–the complement of L2. P = NP = co-NP • There is an easy reduction from the complement of SAT to VAL, P = NP co-NP = NP = co-NP • ∩ namely the map that takes an expression to its negation. P = NP co-NP = NP = co-NP • ∩ VAL P P = NP = co-NP ∈ ⇒ P = NP co-NP = NP = co-NP • ∩ VAL NP NP = co-NP ∈ ⇒ Anuj Dawar May 14, 2010 Anuj Dawar May 14, 2010 Complexity Theory 5 Complexity Theory 6 Prime Numbers Primality Consider the decision problem PRIME: Another way of putting this is that Composite is in NP. -
Computational Complexity Theory Introduction to Computational
31/03/2012 Computational complexity theory Introduction to computational complexity theory Complexity (computability) theory theory deals with two aspects: Algorithm’s complexity. Problem’s complexity. References S. Cook, « The complexity of Theorem Proving Procedures », 1971. Garey and Johnson, « Computers and Intractability, A guide to the theory of NP-completeness », 1979. J. Carlier et Ph. Chrétienne « Problèmes d’ordonnancements : algorithmes et complexité », 1988. 1 31/03/2012 Basic Notions • Some problem is a “question” characterized by parameters and needs an answer. – Parameters description; – Properties that a solutions must satisfy; – An instance is obtained when the parameters are fixed to some values. • An algorithm: a set of instructions describing how some task can be achieved or a problem can be solved. • A program : the computational implementation of an algorithm. Algorithm’s complexity (I) • There may exists several algorithms for the same problem • Raised questions: – Which one to choose ? – How they are compared ? – How measuring the efficiency ? – What are the most appropriate measures, running time, memory space ? 2 31/03/2012 Algorithm’s complexity (II) • Running time depends on: – The data of the problem, – Quality of program..., – Computer type, – Algorithm’s efficiency, – etc. • Proceed by analyzing the algorithm: – Search for some n characterizing the data. – Compute the running time in terms of n. – Evaluating the number of elementary operations, (elementary operation = simple instruction of a programming language). Algorithm’s evaluation (I) • Any algorithm is composed of two main stages: initialization and computing one • The complexity parameter is the size data n (binary coding). Definition: Let be n>0 andT(n) the running time of an algorithm expressed in terms of the size data n, T(n) is of O(f(n)) iff n0 and some constant c such that: n n0, we have T(n) c f(n). -
Submitted Thesis
Saarland University Faculty of Mathematics and Computer Science Bachelor’s Thesis Synthetic One-One, Many-One, and Truth-Table Reducibility in Coq Author Advisor Felix Jahn Yannick Forster Reviewers Prof. Dr. Gert Smolka Prof. Dr. Markus Bläser Submitted: September 15th, 2020 ii Eidesstattliche Erklärung Ich erkläre hiermit an Eides statt, dass ich die vorliegende Arbeit selbstständig ver- fasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Statement in Lieu of an Oath I hereby confirm that I have written this thesis on my own and that I have not used any other media or materials than the ones referred to in this thesis. Einverständniserklärung Ich bin damit einverstanden, dass meine (bestandene) Arbeit in beiden Versionen in die Bibliothek der Informatik aufgenommen und damit veröffentlicht wird. Declaration of Consent I agree to make both versions of my thesis (with a passing grade) accessible to the public by having them added to the library of the Computer Science Department. Saarbrücken, September 15th, 2020 Abstract Reducibility is an essential concept for undecidability proofs in computability the- ory. The idea behind reductions was conceived by Turing, who introduced the later so-called Turing reduction based on oracle machines. In 1944, Post furthermore in- troduced with one-one, many-one, and truth-table reductions in comparison to Tur- ing reductions more specific reducibility notions. Post then also started to analyze the structure of the different reducibility notions and their computability degrees. Most undecidable problems were reducible from the halting problem, since this was exactly the method to show them undecidable. However, Post was able to con- struct also semidecidable but undecidable sets that do not one-one, many-one, or truth-table reduce from the halting problem. -
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. -
Lecture 4 1 the Permanent of a Matrix
Grafy a poˇcty - NDMI078 April 2009 Lecture 4 M. Loebl J.-S. Sereni 1 The permanent of a matrix 1.1 Minc's conjecture The set of permutations of f1; : : : ; ng is Sn. Let A = (ai;j)1≤i;j≤n be a square matrix with real non-negative entries. The permanent of the matrix A is n X Y perm(A) := ai,σ(i) : σ2Sn i=1 In 1973, Br`egman[4] proved M´ınc’sconjecture [18]. n×n Pn Theorem 1 (Br`egman,1973). Let A = (ai;j)1≤i;j≤n 2 f0; 1g . Set ri := j=1 ai;j. Then, n Y 1=ri perm(A) ≤ (ri!) : i=1 Further, if ri > 0 for every i 2 f1; 2; : : : ; ng, then there is equality if and only if up to permutations of rows and columns, A is a block-diagonal matrix, each block being a square matrix with all entries equal to 1. Several proofs of this result are known, the original being combinatorial. In 1978, Schrijver [22] found a neat and short proof. A probabilistic description of this proof is presented in the book of Alon and Spencer [3, Chapter 2]. The one we will see in Lecture 5 uses the concept of entropy, and was found by Radhakrishnan [20] in the late nineties. It is a nice illustration of the use of entropy to count combinatorial objects. 1.2 The van der Waerden conjecture A square matrix M = (mij)1≤i;j≤n of non-negative real numbers is doubly stochastic if the sum of the entries of every line is equal to 1, and the same holds for the sum of the entries of each column. -
Nitin Saurabh the Institute of Mathematical Sciences, Chennai
ALGEBRAIC MODELS OF COMPUTATION By Nitin Saurabh The Institute of Mathematical Sciences, Chennai. A thesis submitted to the Board of Studies in Mathematical Sciences In partial fulllment of the requirements For the Degree of Master of Science of HOMI BHABHA NATIONAL INSTITUTE April 2012 CERTIFICATE Certied that the work contained in the thesis entitled Algebraic models of Computation, by Nitin Saurabh, has been carried out under my supervision and that this work has not been submitted elsewhere for a degree. Meena Mahajan Theoretical Computer Science Group The Institute of Mathematical Sciences, Chennai ACKNOWLEDGEMENTS I would like to thank my advisor Prof. Meena Mahajan for her invaluable guidance and continuous support since my undergraduate days. Her expertise and ideas helped me comprehend new techniques. Her guidance during the preparation of this thesis has been invaluable. I also thank her for always being there to discuss and clarify any matter. I am extremely grateful to all the faculty members of theory group at IMSc and CMI for their continuous encouragement and giving me an opportunity to learn from them. I would like to thank all my friends, at IMSc and CMI, for making my stay in Chennai a memorable one. Most of all, I take this opportunity to thank my parents, my uncle and my brother. Abstract Valiant [Val79, Val82] had proposed an analogue of the theory of NP-completeness in an entirely algebraic framework to study the complexity of polynomial families. Artihmetic circuits form the most standard model for studying the complexity of polynomial computations. In a note [Val92], Valiant argued that in order to prove lower bounds for boolean circuits, obtaining lower bounds for arithmetic circuits should be a rst step. -
Lecture 12 – the Permanent and the Determinant
Lecture 12 { The permanent and the determinant Uriel Feige Department of Computer Science and Applied Mathematics The Weizman Institute Rehovot 76100, Israel [email protected] June 23, 2014 1 Introduction Given an order n matrix A, its permanent is X Yn per(A) = aiσ(i) σ i=1 where σ ranges over all permutations on n elements. Its determinant is X Yn σ det(A) = (−1) aiσ(i) σ i=1 where (−1)σ is +1 for even permutations and −1 for odd permutations. A permutation is even if it can be obtained from the identity permutation using an even number of transpo- sitions (where a transposition is a swap of two elements), and odd otherwise. For those more familiar with the inductive definition of the determinant, obtained by developing the determinant by the first row of the matrix, observe that the inductive defini- tion if spelled out leads exactly to the formula above. The same inductive definition applies to the permanent, but without the alternating sign rule. The determinant can be computed in polynomial time by Gaussian elimination, and in time n! by fast matrix multiplication. On the other hand, there is no polynomial time algorithm known for computing the permanent. In fact, Valiant showed that the permanent is complete for the complexity class #P , which makes computing it as difficult as computing the number of solutions of NP-complete problems (such as SAT, Valiant's reduction was from Hamiltonicity). For 0/1 matrices, the matrix A can be thought of as the adjacency matrix of a bipartite graph (we refer to it as a bipartite adjacency matrix { technically, A is an off-diagonal block of the usual adjacency matrix), and then the permanent counts the number of perfect matchings. -
Statistical Problems Involving Permutations with Restricted Positions
STATISTICAL PROBLEMS INVOLVING PERMUTATIONS WITH RESTRICTED POSITIONS PERSI DIACONIS, RONALD GRAHAM AND SUSAN P. HOLMES Stanford University, University of California and ATT, Stanford University and INRA-Biornetrie The rich world of permutation tests can be supplemented by a variety of applications where only some permutations are permitted. We consider two examples: testing in- dependence with truncated data and testing extra-sensory perception with feedback. We review relevant literature on permanents, rook polynomials and complexity. The statistical applications call for new limit theorems. We prove a few of these and offer an approach to the rest via Stein's method. Tools from the proof of van der Waerden's permanent conjecture are applied to prove a natural monotonicity conjecture. AMS subject classiήcations: 62G09, 62G10. Keywords and phrases: Permanents, rook polynomials, complexity, statistical test, Stein's method. 1 Introduction Definitive work on permutation testing by Willem van Zwet, his students and collaborators, has given us a rich collection of tools for probability and statistics. We have come upon a series of variations where randomization naturally takes place over a subset of all permutations. The present paper gives two examples of sets of permutations defined by restricting positions. Throughout, a permutation π is represented in two-line notation 1 2 3 ... n π(l) π(2) π(3) ••• τr(n) with π(i) referred to as the label at position i. The restrictions are specified by a zero-one matrix Aij of dimension n with Aij equal to one if and only if label j is permitted in position i. Let SA be the set of all permitted permutations. -
Module 34: Reductions and NP-Complete Proofs
Module 34: Reductions and NP-Complete Proofs This module 34 focuses on reductions and proof of NP-Complete problems. The module illustrates the ways of reducing one problem to another. Then, the module illustrates the outlines of proof of NP- Complete problems with some examples. The objectives of this module are To understand the concept of Reductions among problems To Understand proof of NP-Complete problems To overview proof of some Important NP-Complete problems. Computational Complexity There are two types of complexity theories. One is related to algorithms, known as algorithmic complexity theory and another related to complexity of problems called computational complexity theory [2,3]. Algorithmic complexity theory [3,4] aims to analyze the algorithms in terms of the size of the problem. In modules 3 and 4, we had discussed about these methods. The size is the length of the input. It can be recollected from module 3 that the size of a number n is defined to be the number of binary bits needed to write n. For example, Example: b(5) = b(1012) = 3. In other words, the complexity of the algorithm is stated in terms of the size of the algorithm. Asymptotic Analysis [1,2] refers to the study of an algorithm as the input size reaches a limit and analyzing the behaviour of the algorithms. The asymptotic analysis is also science of approximation where the behaviour of the algorithm is expressed in terms of notations such as big-oh, Big-Omega and Big- Theta. Computational complexity theory is different. Computational complexity aims to determine complexity of the problems itself. -
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). -
Computational Complexity: a Modern Approach
i Computational Complexity: A Modern Approach Draft of a book: Dated January 2007 Comments welcome! Sanjeev Arora and Boaz Barak Princeton University [email protected] Not to be reproduced or distributed without the authors’ permission This is an Internet draft. Some chapters are more finished than others. References and attributions are very preliminary and we apologize in advance for any omissions (but hope you will nevertheless point them out to us). Please send us bugs, typos, missing references or general comments to [email protected] — Thank You!! DRAFT ii DRAFT Chapter 9 Complexity of counting “It is an empirical fact that for many combinatorial problems the detection of the existence of a solution is easy, yet no computationally efficient method is known for counting their number.... for a variety of problems this phenomenon can be explained.” L. Valiant 1979 The class NP captures the difficulty of finding certificates. However, in many contexts, one is interested not just in a single certificate, but actually counting the number of certificates. This chapter studies #P, (pronounced “sharp p”), a complexity class that captures this notion. Counting problems arise in diverse fields, often in situations having to do with estimations of probability. Examples include statistical estimation, statistical physics, network design, and more. Counting problems are also studied in a field of mathematics called enumerative combinatorics, which tries to obtain closed-form mathematical expressions for counting problems. To give an example, in the 19th century Kirchoff showed how to count the number of spanning trees in a graph using a simple determinant computation. Results in this chapter will show that for many natural counting problems, such efficiently computable expressions are unlikely to exist. -
Chapter 24 Conp, Self-Reductions
Chapter 24 coNP, Self-Reductions CS 473: Fundamental Algorithms, Spring 2013 April 24, 2013 24.1 Complementation and Self-Reduction 24.2 Complementation 24.2.1 Recap 24.2.1.1 The class P (A) A language L (equivalently decision problem) is in the class P if there is a polynomial time algorithm A for deciding L; that is given a string x, A correctly decides if x 2 L and running time of A on x is polynomial in jxj, the length of x. 24.2.1.2 The class NP Two equivalent definitions: (A) Language L is in NP if there is a non-deterministic polynomial time algorithm A (Turing Machine) that decides L. (A) For x 2 L, A has some non-deterministic choice of moves that will make A accept x (B) For x 62 L, no choice of moves will make A accept x (B) L has an efficient certifier C(·; ·). (A) C is a polynomial time deterministic algorithm (B) For x 2 L there is a string y (proof) of length polynomial in jxj such that C(x; y) accepts (C) For x 62 L, no string y will make C(x; y) accept 1 24.2.1.3 Complementation Definition 24.2.1. Given a decision problem X, its complement X is the collection of all instances s such that s 62 L(X) Equivalently, in terms of languages: Definition 24.2.2. Given a language L over alphabet Σ, its complement L is the language Σ∗ n L. 24.2.1.4 Examples (A) PRIME = nfn j n is an integer and n is primeg o PRIME = n n is an integer and n is not a prime n o PRIME = COMPOSITE .