Logic and Bit Operations
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Laced Boolean Functions and Subset Sum Problems in Finite Fields
Laced Boolean functions and subset sum problems in finite fields David Canright1, Sugata Gangopadhyay2 Subhamoy Maitra3, Pantelimon Stanic˘ a˘1 1 Department of Applied Mathematics, Naval Postgraduate School Monterey, CA 93943{5216, USA; fdcanright,[email protected] 2 Department of Mathematics, Indian Institute of Technology Roorkee 247667 INDIA; [email protected] 3 Applied Statistics Unit, Indian Statistical Institute 203 B. T. Road, Calcutta 700 108, INDIA; [email protected] March 13, 2011 Abstract In this paper, we investigate some algebraic and combinatorial properties of a special Boolean function on n variables, defined us- ing weighted sums in the residue ring modulo the least prime p ≥ n. We also give further evidence to a question raised by Shparlinski re- garding this function, by computing accurately the Boolean sensitivity, thus settling the question for prime number values p = n. Finally, we propose a generalization of these functions, which we call laced func- tions, and compute the weight of one such, for every value of n. Mathematics Subject Classification: 06E30,11B65,11D45,11D72 Key Words: Boolean functions; Hamming weight; Subset sum problems; residues modulo primes. 1 1 Introduction Being interested in read-once branching programs, Savicky and Zak [7] were led to the definition and investigation, from a complexity point of view, of a special Boolean function based on weighted sums in the residue ring modulo a prime p. Later on, a modification of the same function was used by Sauerhoff [6] to show that quantum read-once branching programs are exponentially more powerful than classical read-once branching programs. Shparlinski [8] used exponential sums methods to find bounds on the Fourier coefficients, and he posed several open questions, which are the motivation of this work. -
Analysis of Boolean Functions and Its Applications to Topics Such As Property Testing, Voting, Pseudorandomness, Gaussian Geometry and the Hardness of Approximation
Analysis of Boolean Functions Notes from a series of lectures by Ryan O’Donnell Guest lecture by Per Austrin Barbados Workshop on Computational Complexity February 26th – March 4th, 2012 Organized by Denis Th´erien Scribe notes by Li-Yang Tan arXiv:1205.0314v1 [cs.CC] 2 May 2012 Contents 1 Linearity testing and Arrow’s theorem 3 1.1 TheFourierexpansion ............................. 3 1.2 Blum-Luby-Rubinfeld. .. .. .. .. .. .. .. .. 7 1.3 Votingandinfluence .............................. 9 1.4 Noise stability and Arrow’s theorem . ..... 12 2 Noise stability and small set expansion 15 2.1 Sheppard’s formula and Stabρ(MAJ)...................... 15 2.2 Thenoisyhypercubegraph. 16 2.3 Bonami’slemma................................. 18 3 KKL and quasirandomness 20 3.1 Smallsetexpansion ............................... 20 3.2 Kahn-Kalai-Linial ............................... 21 3.3 Dictator versus Quasirandom tests . ..... 22 4 CSPs and hardness of approximation 26 4.1 Constraint satisfaction problems . ...... 26 4.2 Berry-Ess´een................................... 27 5 Majority Is Stablest 30 5.1 Borell’s isoperimetric inequality . ....... 30 5.2 ProofoutlineofMIST ............................. 32 5.3 Theinvarianceprinciple . 33 6 Testing dictators and UGC-hardness 37 1 Linearity testing and Arrow’s theorem Monday, 27th February 2012 Rn Open Problem [Guy86, HK92]: Let a with a 2 = 1. Prove Prx 1,1 n [ a, x • ∈ k k ∈{− } |h i| ≤ 1] 1 . ≥ 2 Open Problem (S. Srinivasan): Suppose g : 1, 1 n 2 , 1 where g(x) 2 , 1 if • {− } →± 3 ∈ 3 n x n and g(x) 1, 2 if n x n . Prove deg( f)=Ω(n). i=1 i ≥ 2 ∈ − − 3 i=1 i ≤− 2 P P In this workshop we will study the analysis of boolean functions and its applications to topics such as property testing, voting, pseudorandomness, Gaussian geometry and the hardness of approximation. -
Probabilistic Boolean Logic, Arithmetic and Architectures
PROBABILISTIC BOOLEAN LOGIC, ARITHMETIC AND ARCHITECTURES A Thesis Presented to The Academic Faculty by Lakshmi Narasimhan Barath Chakrapani In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Computer Science, College of Computing Georgia Institute of Technology December 2008 PROBABILISTIC BOOLEAN LOGIC, ARITHMETIC AND ARCHITECTURES Approved by: Professor Krishna V. Palem, Advisor Professor Trevor Mudge School of Computer Science, College Department of Electrical Engineering of Computing and Computer Science Georgia Institute of Technology University of Michigan, Ann Arbor Professor Sung Kyu Lim Professor Sudhakar Yalamanchili School of Electrical and Computer School of Electrical and Computer Engineering Engineering Georgia Institute of Technology Georgia Institute of Technology Professor Gabriel H. Loh Date Approved: 24 March 2008 College of Computing Georgia Institute of Technology To my parents The source of my existence, inspiration and strength. iii ACKNOWLEDGEMENTS आचायातर् ्पादमादे पादं िशंयः ःवमेधया। पादं सॄचारयः पादं कालबमेणच॥ “One fourth (of knowledge) from the teacher, one fourth from self study, one fourth from fellow students and one fourth in due time” 1 Many people have played a profound role in the successful completion of this disser- tation and I first apologize to those whose help I might have failed to acknowledge. I express my sincere gratitude for everything you have done for me. I express my gratitude to Professor Krisha V. Palem, for his energy, support and guidance throughout the course of my graduate studies. Several key results per- taining to the semantic model and the properties of probabilistic Boolean logic were due to his brilliant insights. -
Application: Digital Logic Circuits
SECTION 2.4 Application: Digital Logic Circuits Copyright © Cengage Learning. All rights reserved. Application: Digital Logic Circuits Switches “in series” Switches “in parallel” Change closed and on are replaced by T, open and off are replaced by F? Application: Digital Logic Circuits • More complicated circuits correspond to more complicated logical expressions. • This correspondence has been used extensively in design and study of circuits. • Electrical engineers use language of logic when refer to values of signals produced by an electronic switch as being “true” or “false.” • Only that symbols 1 and 0 are used • symbols 0 and 1 are called bits, short for binary digits. • This terminology was introduced in 1946 by the statistician John Tukey. Black Boxes and Gates Black Boxes and Gates • Circuits: transform combinations of signal bits (1’s and 0’s) into other combinations of signal bits (1’s and 0’s). • Computer engineers and digital system designers treat basic circuits as black boxes. • Ignore inside of a black box (detailed implementation of circuit) • focused on the relation between the input and the output signals. • Operation of a black box is completely specified by constructing an input/output table that lists all its possible input signals together with their corresponding output signals. Black Boxes and Gates One possible correspondence of input to output signals is as follows: An Input/Output Table Black Boxes and Gates An efficient method for designing more complicated circuits is to build them by connecting less complicated black box circuits. Gates can be combined into circuits in a variety of ways. If the rules shown on the next page are obeyed, the result is a combinational circuit, one whose output at any time is determined entirely by its input at that time without regard to previous inputs. -
Shalack V.I. Semiotic Foundations of Logic
Semiotic foundations of logic Vladimir I. Shalack abstract. The article offers a look at the combinatorial logic as the logic of signs operating in the most general sense. For this it is proposed slightly reformulate it in terms of introducing and replacement of the definitions. Keywords: combinatory logic, semiotics, definition, logic founda- tions 1 Language selection Let’s imagine for a moment what would be like the classical logic, if we had not studied it in the language of negation, conjunction, dis- junction and implication, but in the language of the Sheffer stroke. I recall that it can be defined with help of negation and conjunction as follows A j B =Df :(A ^ B): In turn, all connectives can be defined with help of the Sheffer stroke in following manner :A =Df (A j A) A ^ B =Df (A j B) j (A j B) A _ B =Df (A j A) j (B j B) A ⊃ B =Df A j (B j B): Modus ponens rule takes the following form A; A j (B j B) . B 226 Vladimir I. Shalack We can go further and following the ideas of M. Sch¨onfinkel to define two-argument infix quantifier ‘jx’ x A j B =Df 8x(A j B): Now we can use it to define Sheffer stroke and quantifiers. x A j B =Df A j B where the variable x is not free in the formulas A and B; y x y 8xA =Df (A j A) j (A j A) where the variable y is not free in the formula A; x y x 9xA =Df (A j A) j (A j A) where the variable y is not free in the formula A. -
CS 173: Discrete Structures, Spring 2012 Homework 1 Solutions
CS 173: Discrete Structures, Spring 2012 Homework 1 Solutions This homework contains 3 problems worth a total of 25 points. 1. [1 point] Finding information on Piazza How many pet rats does Margaret’s family have? (Hint: see Piazza.) Solution: Margaret’s family has six pet rats. 2. [4 points] Demographic Survey 3. [20 points] Logic operators The late 19th century philosopher Charles Peirce (rhymes with ‘hearse,’ not ‘fierce’) wrote about a set of logically dual operators and, in his writings, coined the term ‘Ampheck’ to describe them. The two most common Ampheck operators, the Peirce arrow (written ↓ or ⊥ or ∨ by different people) and the Sheffer stroke (written ↑ or | or ∧ by different people), are defined by the following truth table: p q p ↑ q p ↓ q T T F F T F T F F T T F F F T T (a) (4 points) The set of operators {∧, ∨, ¬} is functionally complete, which means that every logical statement can be expressed using only these three operators. Is the smaller set of operators {∨, ¬} also functionally complete? Explain why or why not. Solution: By De Morgan’s laws, ¬(¬p∨¬q) ≡ ¬¬p∧¬¬q ≡ p∧q So every logical statement using the ∧ operator can be rewritten in terms of the ∨ and ¬ operators. Since every logical statement can be expressed in terms of the ∧, ∨, and ¬ operators, this implies that every logical statement can be expressed in terms of the ∨ and ¬ operators, and so {∨, ¬} is functionally complete. (b) (4 points) Express ¬p using only the Sheffer stroke operation ↑. Solution: Note that ¬p is true if and only if p is false. -
Predicate Logic
Predicate Logic Laura Kovács Recap: Boolean Algebra and Propositional Logic 0, 1 True, False (other notation: t, f) boolean variables a ∈{0,1} atomic formulas (atoms) p∈{True,False} boolean operators ¬, ∧, ∨, fl, ñ logical connectives ¬, ∧, ∨, fl, ñ boolean functions: propositional formulas: • 0 and 1 are boolean functions; • True and False are propositional formulas; • boolean variables are boolean functions; • atomic formulas are propositional formulas; • if a is a boolean function, • if a is a propositional formula, then ¬a is a boolean function; then ¬a is a propositional formula; • if a and b are boolean functions, • if a and b are propositional formulas, then a∧b, a∨b, aflb, añb are boolean functions. then a∧b, a∨b, aflb, añb are propositional formulas. truth value of a boolean function truth value of a propositional formula (truth tables) (truth tables) Recap: Boolean Algebra and Propositional Logic 0, 1 True, False (other notation: t, f) boolean variables a ∈{0,1} atomic formulas (atoms) p∈{True,False} boolean operators ¬, ∧, ∨, fl, ñ logical connectives ¬, ∧, ∨, fl, ñ boolean functions: propositional formulas (propositions, Aussagen ): • 0 and 1 are boolean functions; • True and False are propositional formulas; • boolean variables are boolean functions; • atomic formulas are propositional formulas; • if a is a boolean function, • if a is a propositional formula, then ¬a is a boolean function; then ¬a is a propositional formula; • if a and b are boolean functions, • if a and b are propositional formulas, then a∧b, a∨b, aflb, añb are -
Satisfiability 6 the Decision Problem 7
Satisfiability Difficult Problems Dealing with SAT Implementation Klaus Sutner Carnegie Mellon University 2020/02/04 Finding Hard Problems 2 Entscheidungsproblem to the Rescue 3 The Entscheidungsproblem is solved when one knows a pro- cedure by which one can decide in a finite number of operations So where would be look for hard problems, something that is eminently whether a given logical expression is generally valid or is satis- decidable but appears to be outside of P? And, we’d like the problem to fiable. The solution of the Entscheidungsproblem is of funda- be practical, not some monster from CRT. mental importance for the theory of all fields, the theorems of which are at all capable of logical development from finitely many The Circuit Value Problem is a good indicator for the right direction: axioms. evaluating Boolean expressions is polynomial time, but relatively difficult D. Hilbert within P. So can we push CVP a little bit to force it outside of P, just a little bit? In a sense, [the Entscheidungsproblem] is the most general Say, up into EXP1? problem of mathematics. J. Herbrand Exploiting Difficulty 4 Scaling Back 5 Herbrand is right, of course. As a research project this sounds like a Taking a clue from CVP, how about asking questions about Boolean fiasco. formulae, rather than first-order? But: we can turn adversity into an asset, and use (some version of) the Probably the most natural question that comes to mind here is Entscheidungsproblem as the epitome of a hard problem. Is ϕ(x1, . , xn) a tautology? The original Entscheidungsproblem would presumable have included arbitrary first-order questions about number theory. -
Boolean Logic
Boolean logic Lecture 12 Contents . Propositions . Logical connectives and truth tables . Compound propositions . Disjunctive normal form (DNF) . Logical equivalence . Laws of logic . Predicate logic . Post's Functional Completeness Theorem Propositions . A proposition is a statement that is either true or false. Whichever of these (true or false) is the case is called the truth value of the proposition. ‘Canberra is the capital of Australia’ ‘There are 8 day in a week.’ . The first and third of these propositions are true, and the second and fourth are false. The following sentences are not propositions: ‘Where are you going?’ ‘Come here.’ ‘This sentence is false.’ Propositions . Propositions are conventionally symbolized using the letters Any of these may be used to symbolize specific propositions, e.g. :, Manchester, , … . is in Scotland, : Mammoths are extinct. The previous propositions are simple propositions since they make only a single statement. Logical connectives and truth tables . Simple propositions can be combined to form more complicated propositions called compound propositions. .The devices which are used to link pairs of propositions are called logical connectives and the truth value of any compound proposition is completely determined by the truth values of its component simple propositions, and the particular connective, or connectives, used to link them. ‘If Brian and Angela are not both happy, then either Brian is not happy or Angela is not happy.’ .The sentence about Brian and Angela is an example of a compound proposition. It is built up from the atomic propositions ‘Brian is happy’ and ‘Angela is happy’ using the words and, or, not and if-then. -
Problems and Comments on Boolean Algebras Rosen, Fifth Edition: Chapter 10; Sixth Edition: Chapter 11 Boolean Functions
Problems and Comments on Boolean Algebras Rosen, Fifth Edition: Chapter 10; Sixth Edition: Chapter 11 Boolean Functions Section 10. 1, Problems: 1, 2, 3, 4, 10, 11, 29, 36, 37 (fifth edition); Section 11.1, Problems: 1, 2, 5, 6, 12, 13, 31, 40, 41 (sixth edition) The notation ""forOR is bad and misleading. Just think that in the context of boolean functions, the author uses instead of ∨.The integers modulo 2, that is ℤ2 0,1, have an addition where 1 1 0 while 1 ∨ 1 1. AsetA is partially ordered by a binary relation ≤, if this relation is reflexive, that is a ≤ a holds for every element a ∈ S,it is transitive, that is if a ≤ b and b ≤ c hold for elements a,b,c ∈ S, then one also has that a ≤ c, and ≤ is anti-symmetric, that is a ≤ b and b ≤ a can hold for elements a,b ∈ S only if a b. The subsets of any set S are partially ordered by set inclusion. that is the power set PS,⊆ is a partially ordered set. A partial ordering on S is a total ordering if for any two elements a,b of S one has that a ≤ b or b ≤ a. The natural numbers ℕ,≤ with their ordinary ordering are totally ordered. A bounded lattice L is a partially ordered set where every finite subset has a least upper bound and a greatest lower bound.The least upper bound of the empty subset is defined as 0, it is the smallest element of L. -
Propositional Logic
Mathematical Logic (Based on lecture slides by Stan Burris) George Voutsadakis1 1Mathematics and Computer Science Lake Superior State University LSSU Math 300 George Voutsadakis (LSSU) Logic January 2013 1 / 86 Outline 1 Propositional Logic Connectives, Formulas and Truth Tables Equivalences, Tautologies and Contradictions Substitution Replacement Adequate Sets of Connectives Disjunctive and Conjunctive Forms Valid Arguments, Tautologies and Satisfiability Compactness Epilogue: Other Propositional Logics George Voutsadakis (LSSU) Logic January 2013 2 / 86 Propositional Logic Connectives, Formulas and Truth Tables Subsection 1 Connectives, Formulas and Truth Tables George Voutsadakis (LSSU) Logic January 2013 3 / 86 Propositional Logic Connectives, Formulas and Truth Tables The Alphabet: Connectives and Variables The following are the basic logical connectives that we use to connect logical statements: Symbol Name Symbol Name 1 true ∧ and 0 false ∨ or ¬ not → implies ↔ iff In the same way that in algebra we use x, y, z,... to stand for unknown or varying numbers, in logic we use the propositional variables P, Q, R,... to stand for unknown or varying propositions or statements; Using the connectives and variables we can construct propositional formulas like ((P → (Q ∨ R)) ∧ ((¬Q) ↔ (1 ∨ P))). George Voutsadakis (LSSU) Logic January 2013 4 / 86 Propositional Logic Connectives, Formulas and Truth Tables Inductive (Recursive) Definition of Propositional Formulas Propositional formulas are formally built as follows: Every propositional variable P is a propositional formula; the constants 0 and 1 are propositional formulas; if F is a propositional formula, then (¬F ) is a propositional formula; if F and G are propositional formulas, then (F ∧ G), (F ∨ G), (F → G) and (F ↔ G) are propositional formulas. -
Combinational Circuits
Combinational Circuits Jason Filippou CMSC250 @ UMCP 06-02-2016 Jason Filippou (CMSC250 @ UMCP) Circuits 06-02-2016 1 / 1 Outline Jason Filippou (CMSC250 @ UMCP) Circuits 06-02-2016 2 / 1 Hardware design levels Hardware design levels Jason Filippou (CMSC250 @ UMCP) Circuits 06-02-2016 3 / 1 Hardware design levels Levels of abstraction for ICs Small Scale Integration: ≈ 10 boolean gates Medium Scale Integration: > 10; ≤ 100 Large Scale Integration: Anywhere between 100 and 30; 000. Very Large Scale Integration: Up to 150; 000. Very Very Large Scale Integration: > 150; 000. Jason Filippou (CMSC250 @ UMCP) Circuits 06-02-2016 4 / 1 Hardware design levels Example schematic Figure 1: VLSI schematic for a professional USB interface for Macintosh PCs. Jason Filippou (CMSC250 @ UMCP) Circuits 06-02-2016 5 / 1 Hardware design levels SSI Consists of circuits that contain about 10 gates. 16-bit adders. Encoders / Decoders. MUX/DEMUX. ::: All our examples will be at an SSI level. Logic Design is the branch of Computer Science that essentially analyzes the kinds of circuits and optimizations done at an SSI/MSI level. We do not have such a course in the curriculum, but you can expect to be exposed to it if you ever take 411. Jason Filippou (CMSC250 @ UMCP) Circuits 06-02-2016 6 / 1 Combinational Circuit Design Combinational Circuit Design Jason Filippou (CMSC250 @ UMCP) Circuits 06-02-2016 7 / 1 A sequential circuit is one where this constraint can be violated. Example: Flip-flops (yes, seriously). In this course, we will only touch upon combinational circuits. Combinational Circuit Design Combinational vs Sequential circuits A combinational circuit is one where the output of a gate is never used as an input into it.