Real or natural number interpretation and their effect on complexity

Suppose {X(t), t ∈ G d } is a random field in d dimensions, with d ∈ ℤ+; that is, {X(t), t ∈ G d } is a collection of random variables X(t) taking values in a state space S, defined on a probability space (Ω, A, P), and indexed by the variable t ∈ G d . Throughout this chapter, G will stand for either the set of real numbers ℝ, or the set of integers ℤ; thus, the random field {X(t)} is allowed to “run” in either continuous or discrete “time.” Similarly, G+ will denote ℝ+ or ℤ+ (the sets of positive real numbers and integers, respectively) according to whether G = ℝ or G = ℤ.

The article is devoted to the problem of the real numbers' decomposition to a linear combination of natural numbers' squares. First of all the theorem giving indirectly the conditions of existing natural numbers' two equal squares is formulated and proved. Basing on this theorem two algorithms designed for finding couples of integer numbers generating eąual sums of sąuares and determining the multiplicity of that couples are presented. Finally the calculation of the algorithms' classes and estimation of the calculation errors is made. Also an example output of presented algorithms was shown.

We prove that the art gallery problem is equivalent under polynomial time reductions to deciding whether a system of polynomial equations over the real numbers has a solution. The art gallery problem is a classic problem in computational geometry, introduced in 1973 by Victor Klee. Given a simple polygon P and an integer k, the goal is to decide if there exists a set G of k guards within P such that every point p ∈ P is seen by at least one guard g∈ G. Each guard corresponds to a point in the polygon P, and we say that a guard g sees a point p if the line segment pg is contained in P. The art gallery problem has stimulated extensive research in geometry and in algorithms. However, the complexity status of the art gallery problem has not been resolved. It has long been known that the problem is NP-hard, but no one has been able to show that it lies in NP. Recently, the computational geometry community became more aware of the complexity class ∃ ℝ, which has been studied earlier by other communities. The class ∃ ℝ consists of problems that can be reduced in polynomial time to the problem of deciding whether a system of polynomial equations with integer coefficients and any number of real variables has a solution. It can be easily seen that NP ⊆ ∃ ℝ. We prove that the art gallery problem is ∃ ℝ-complete, implying that (1) any system of polynomial equations over the real numbers can be encoded as an instance of the art gallery problem, and (2) the art gallery problem is not in the complexity class NP unless NP=∃ ℝ. As an illustration of our techniques, we can show that for every compact semi-algebraic set S⊂ [0,1]2 there exists a polygon with rational coordinates that enforces one of the guards to be at any position p∈ S, in any optimal guarding. As a corollary of our construction, we prove that for any real algebraic number α there is an instance of the art gallery problem where one of the coordinates of the guards equals α in any guard set of minimum cardinality. That rules out many natural geometric approaches to the problem, as it shows that any approach based on constructing a finite set of candidate points for placing guards has to include points with coordinates being roots of polynomials with arbitrary degree. In the ∃ ℝ-hardness proof for the art gallery problem we introduce a new ∃ ℝ-complete problem ETR-INV. We believe that this problem is of independent interest, as it can be used to obtain ∃ ℝ-hardness proofs for other problems. In particular, ETR-INV has been very recently used to prove ∃ ℝ-hardness of other geometric problems.

n= Zx,+ i=1 The principal tool in the proof is the Hardy-Littlewood circle method, incorporating results and techniques of a powerful new iterative method developed by Vaughan and Wooley ([Va3], [Va4], [VW1], [Wol], [VW2]). In section 2, an algorithm developed in [Fo] for optimizing the parameters in mixed power mean value theorems is generalized and analyzed. Section 3 details a more sophisticated method of generating mixed power mean value theorems, by a limited adaptation of the iterative method itself. The form of these estimates offers many advantages over those of section 2, and provides the key to the elimination of the 16th power from (1.1). These mean value theorems are then applied to the proof of Theorem 1 in section 4. The tools developed here are applicable to a wide range of mixed power representation problems, and we briefly illustrate in section 5 the application to the problem of determining the number of terms required to represent all large n, when the lowest power used is a kth power instead of a square. Throughout, n is a large natural number whose representation as a sum of mixed powers is at issue, and E is an arbitrarily small positive real number. Constants implied by the Landau Oand Vinogradov <<-symbols may depend on E or k. For a real number x, write e(x) for e27iX, [x] for the greatest integer not exceeding x and llxll for the distance from x to the nearest integer. Unless otherwise specified, lowercase Latin letters denote natural numbers and Greek letters denote real numbers. Let A(P, R) denote the set of natural numbers not exceeding P with no

Over the last decade, first-order constraints have been efficiently used in the artificial intelligence world to model many kinds of complex problems such as: scheduling, resource allocation, computer graphics and bio-informatics. Recently, a new property called decomposability has been introduced and many first-order theories have been proved to be decomposable: finite or infinite trees, rational and real numbers, linear dense order,...etc. A decision procedure in the form of five rewriting rules has also been developed. This latter can decide if a first-order formula without free variables is true or not in any decomposable theory. Unfortunately, this decision procedure is not enough when we want to express the solutions of a first-order constraint having free variables. These kind of problems are generally known as first-order constraint satisfaction problems. We present in this paper, not only a decision procedure but a full first-order constraint solver for decomposable theories. Our solver is given in the form of nine rewriting rules which transform any first-order constraint ? (which can possibly contain free variables) into an equivalent formula ? which is either the formula true, or the formula false or a simple solved formula having at least one free variable and being equivalent neither to true nor to false. We show the efficiency of our solver by solving complex first-order constraints over finite or infinite trees containing a huge number of imbricated quantifiers and negations and compare the performances with those obtained using the most recent and efficient dedicated solver for finite or infinite trees. This is the first full first-order constraint solver for any decomposable theory.

Let ξ be a real number and let n be a positive integer. We define four exponents of Diophantine approximation, which complement the exponents w n (ξ) and w n * (ξ) defined by Mahler and Koksma. We calculate their six values when n=2 and ξ is a real number whose continued fraction expansion coincides with some Sturmian sequence of positive integers, up to the initial terms. In particular, we obtain the exact exponent of approximation to such a continued fraction ξ by quadratic surds.

It is intuitively obvious that if z is a complex number such that ∣1–zv∣ ≤ b ≺ 1 for all positive integers p and some real number b, then z = 1. The purpose of this note is to exhibit a proof of the following generalisation of this observation: THEOREM. Let A be continuous linear operator on a reflexive Banachspace B. If there exists a continuous linear operator T on B, a real number b, and a positive integer p' such that, p an integer and , then A = I. Moreover, in this case ∥I—T∥.

We discuss the procedure of different partitions in the finite set of N integer numbers and construct generic formulas for a bijective map of real numbers sy, where y = 1, 2,…, N, N = \( \underset{k=1}{\overset{n}{\varPi}}{X}_k, \) and Xk are positive integers, onto the set of numbers s(y(x1, x2,…, xn)). We give the functions used to present the bijective map, namely, y(x1, x2, …, xn) and xk(y) in an explicit form and call them the functions detecting the hidden correlations in the system. The idea to introduce and employ the notion of “hidden gates” for a single qudit is proposed. We obtain the entropic-information inequalities for an arbitrary finite set of real numbers and consider the inequalities for arbitrary Clebsch–Gordan coefficients as an example of the found relations for real numbers.

Improved algorithms for the continuous tree edge-partition problems and a note on ratio and sorted matrices searches

Let T be a tree in which every edge is associated with a real number. The sum of a path in T is the sum of the numbers associated with the edges of the path and its length is the number of the edges in it. For two positive integers L 1 ≤ L 2 and two real numbers S 1 ≤ S 2 , a path is feasible if its length is between L 1 and L 2 and its sum is between S 1 and S 2 . We address the problem: Given a tree T, and four numbers, L 1 , L 2 , S 1 and S 2 , find the longest feasible path of T. We provide an optimal O(n log n) time algorithm for the problem, where n = |T|.

1. The Natural Numbers. The Basics. The Fundamental Theorem of Arithmetic. Searching for Primes. Number Fascinations. 2. The Integers. Diophantine Equations. Congruence Arithmetic. Pell and Pythagoras. Factoring Large Numbers. 3. The Rational Numbers. Rational Numbers as Decimals. Decimals as Rational Numbers. Continued Fractions. Solving Equations on the Rational Plane 4. The Real Numbers. Algebraic Representations. Geometric Representations. Analytic Representations. Searching for Transcendental Numbers. 5. Mathematical Projects. Rings of Factors. Sums of Consecutive Numbers. Measuring Abundance. Inside the Fibonacci Numbers. Pictures at an Iteration. Eenie Meenie Miney Mo. Factoring with the Pollard ...r Method. Charting the Integral Universe. Triangles on the Integral Lattice. The Gaussian Integers. Writing Fractions the Egyptian Way. Building Polygons with Dots. The Decimal Universe of Fractions, I. The Decimal Universe of Fractions, II. The Making of a Star. Making Your Own Real Numbers. Building 1 the Egyptian Way. Continued Fraction Expansions of x1/2 N. A Special Kind of Triangle. Polygon Numbers. Continued Fraction Expansions.

The purpose of the paper is to introduce and analyse the following conception: by an ontic shift $X \tilde \rightarrow X''$ we mean an (explicit or hidden) replacement of mathematical object $X'$ called $X$ by different object $X''$ which is also called $X$ and is to play the role of $X'$. If $X'$ and $X''$ are sets, then $X' \neq X''$ means that these sets are different in the usual sense. Otherwise $X'$ and $X''$ may stand for basic concepts of mathematics such as, e.g., point, straight line, natural number, addition of natural numbers, (standard) real number, set, ordered pair, and then $X' \neq X''$ is interpreted in terms of the Leibniz principle of indiscernibility. Several examples from secondary and college mathematics are discussed starting with the ontic shift $P \tilde \rightarrow (x, y)$, where $P$ of the plane is replaced by pair $(x, y)$ of real numbers, also called point; an angle $\varphi$ (thought of as geometric figure) is replaced by its measure which is also denoted by $\varphi$; number (natural, integer, etc.) is replaced by sophisticated set; real number is replaced by the complex number $(a, 0)$, and so on. We distinguish three types of ontic shifts: (α) object $X'$ is replaced by $X''$, and $X'$ is discarded; (β) object $X'$ is replaced by conglomerate $X' \coprod X''$ of two alternatively and exibly used objects $X'$ and $X''$; (γ) object $X'$ is replaced by new object which is mental synthesis $X' \& X''$ integrating features of $X'$ and $X''$. What is crucial in ontic shifts is the change of approach: $X'$ may be identified with $X''$ by declaring that: $X'$ is the same as $X''$. For example, a function may be identified with set of is replaced by a function is the same as its graph, i.e., function becomes set of pairs. In turn, set of pairs of real numbers may be identified with geometric figure. If such ontic shifts were composed, function such as, say, $x \mapsto \sin x$ would be the same as geometric figure. Such arguments show that ontic shifts are often local in the sense that they apply only to certain part of mathematics and composition of two identifications may be unacceptable. in $\mathbb{R}^3$ may be replaced by the corresponding vector and one may say: A is the same as vector. Also vector may be replaced by the corresponding and then we may say that A vector is the same as translation. This identification, however, is not transitive. To see that the statement point is the same as translation is unacceptable imagine student who is asked How do we define of 3D-space? and answers: A is an arbitrary of the space. The paper also deals with distinguishing between ontic shifts and metonymies and with the delicate border between two phenomena: (a) change of the linguistic form, and (b) replacing one mathematical object by another. Although labelling different objects with the same name does not fit the stereotypical image of mathematics, ontic shifts are its significant feature. They should be used in an elastic way, with proper understanding of the concepts involved. However, if such shifts are introduced prematurely, they cause serious didactical troubles.

This paper presents a decision procedure for a certain class of sentences of first order logic involving integral polynomials and the exponential function in which the variables range over the real numbers. The inputs to the decision procedure are prenex sentences in which only the outermost quantified variable can occur in the exponential function. The decision procedure has been implemented in the computer logic system REDLOG. Closely related work is reported in [2, 7, 16, 20, 24].

is. The study of partially ordered sets is a kind of abstract mathematics of the most general sort, compared with the theory of algebraic integers which gave rise to ideal theory. I should like to say a few words about the distinction which is often made between abstract mathematics and the other kind, which may be called classical mathematics. Classical mathematics is based on the real number system, or equivalently on the system of positive integers or on the complex number system. Abstract mathematics is based on set theory. It studies the consequences of quite arbitrary postulate systems. From the point of view of the abstract mathematician, the real number system is a particular ordered field, or a particular locally compact topological ring; in other words, one of a class of similar abstract systems, and not necessarily more interesting than the others. From this same point of view, classical mathematics is a special case of a more general abstract theory. Actually, all mathematics is equally abstract. The real number system is no more concrete than, say, the theory of groups. Neither deals with the physical universe. To be sure, classical mathematics has seemed to be the kind most useful in applications to the sciences. However, perhaps this is an illusion, due to the time lag between the development of a theory and its application. The classical mathematics of the last century is being applied now. The abstract mathematics now being developed may turn out to be just as useful in the next century. Perhaps it will be found that you can kill just as many people just as efficiently using the theory of quasi-groups or p-adic fields as you can with the real number system. Of course many mathematicians will disagree with this view. But it is true now, just as it has been found to be true in the past, that mathematical results arrived at with no thought of their possible use are being applied to situations not imagined by their originators.

We investigate the concept of semicomputability of relations on abstract structures. We consider three possible definitions of this concept, which all reduce to the classical notion of recursive enumerability over the natural numbers. By working in the algebra of the reals, with and without order, we find examples of sets which distinguish between these three notions. We also find interesting examples of sets of real and complex numbers which are semicomputable but not computable.