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Program extraction from proofs can be used to obtain verified algorithms in exact real arithmetic for e.g., the signed-digit code representation. In Minlog this has been done in the past with the use of certain coinductive predicates. In a next step we want to analyze the lookahead of these extracted programs. Doing it by hand is quite cumbersome, so instead we change our definition. Instead of a coinductive predicate we use an inductive predicate for the representation of reals that already incorporates the lookahead. In this way the lookahead becomes part of the specification which is carried through all the proofs. In the end we extract programs for computations on a signed-digit representation and we can just read off the lookahead from the specification. • We use the theory TCF for extracting verified algorithms for exact real number arithmetic based on stream representations. • The lookahead of the stream-transformers is explicitly part of the specification. • We use an explicit representation of real numbers rather than an axiomatization of abstract real numbers. [ABSTRACT FROM AUTHOR]

Emerging wireless and mobile applications, such as border intrusion detection with station-based drones, brought a new barrier coverage problem of using sink-based mobile sensors to cover a given line barrier with minimum energy consumption. In this paper, we focus on the uniform sink-based line barrier coverage (SLBC) problem, in which we are given a line barrier and k sink stations distributed on the plane which can emit an infinite number of sensors with an identical sensing radius. The problem aims to find their final positions on the barrier for the sensors emitted by the stations, such that the total moving distance of the sensors is minimized and each point of the barrier is within the sensing area of at least one sensor. We first observe the geometric structure of an optimal solution that any optimal solution can be considered as a set of intersecting tangent (disk) segments, where a tangent (disk) segment is a sequence of tangent disks. Then, we devise an algorithm to calculate all possible tangent (disk) segments and another algorithm to calculate the near-optimal positions for each of such segments. After computing all tangent (disk) segments and their near-optimal positions, an algorithm is proposed to transform uniform SLBC into an instance of the shortest path problem. It is shown the whole algorithm deserves a runtime O (k 2 log ⁡ k r ε) and consumes at most ε more movement than an optimal solution, where ε is any given positive real number, and r and k are the sensor radius and the number of sink stations, respectively. • The algorithm consumes a runtime O (k 2 log ⁡ k r ε) and produces a solution with at most ε more movement than that of an optimum solution. • With the runtime and the performance guarantee, the proposed algorithm behaves in a manner similar to a polynomial-time exact algorithm. • We proved several geometric properties of an optimum solution, which might have possible applications in other variants of the problem. [ABSTRACT FROM AUTHOR]

This paper enriches a pre-existing decision algorithm, which in turn augmented a fragment of Tarski's elementary algebra with one-argument real functions endowed with a continuous first derivative. In its present (still quantifier-free) version, our decidable language embodies the addition of functions and multiplication of functions by scalars; the issue we address is the one of satisfiability. As regards real numbers, individual variables and constructs designating the basic arithmetic operations are available, along with comparison relators. As regards functions, we have variables of another sort, out of which compound terms are formed by means of constructs designating addition and differentiation. An array of predicates designates various relationships between functions, as well as function properties, that may hold over intervals of the real line; those are: function comparisons, strict and non-strict monotonicity / convexity / concavity, comparisons between the derivative of a function and a real-valued term. Our decision method consists in preprocessing the given formula into an equi-satisfiable quantifier-free formula of the elementary algebra of real numbers, whose satisfiability can then be checked by means of Tarski's decision method. No direct reference to functions will appear in the target formula, each function variable having been superseded by a collection of stub real variables; hence, in order to prove that the proposed translation is satisfiability-preserving, we must figure out a flexible-enough family of interpolating C 1 functions that can accommodate a model for the source formula whenever the target formula turns out to be satisfiable. With respect to the results announced in earlier papers of the same stream, a significant effort went into designing the family of interpolating functions so that it could meet the new constraints stemming from the presence of function addition (along with differentiation) among the constructs of our fragment of mathematical analysis. • A formal language RDF* concerned with differentiable real functions is proposed. • The class of differentiable functions treated is closed under addition. • The expressive power of RDF* is illustrated through a gallery of examples. • A satisfiability-preserving algorithm reducing RDF* formulas into an existential sentence of Tarskian algebra is presented. • The correctness of the reduction algorithm is reported. [ABSTRACT FROM AUTHOR]

In this work, we study a range of constrained versions of the k-supplier and k-center problems. In the classical (unconstrained) k -supplier problem, we are given a set of clients C in a metric space X , with distance function d (. ,.). We are also given a set of feasible facility locations L ⊆ X. The goal is to open a set F of k facilities in L to minimize the maximum distance of any client to the closest open facility, i.e., minimize, cost (F , C) ≡ max j ∈ C ⁡ { d (F , j) } , where d (F , j) is the distance of client j to the closest facility in F. The k -center problem is a special case of the k -supplier problem where L = C. We study various constrained versions of the k -supplier problem such as: capacitated , fault-tolerant , ℓ -diversity, etc. These problems fall under a broad framework of constrained clustering. A unified framework for constrained clustering was proposed by Ding and Xu [Algorithmica 2020] in the context of the k -median and k -means objectives. We extend this framework to the k -supplier and k -center objectives in this work. This unified framework allows us to obtain results simultaneously for the following constrained versions of the k -supplier problem: r-gather, r-capacity, balanced, chromatic, fault-tolerant, strongly private, ℓ-diversity, and fair k-supplier problems, with and without outliers. We design Fixed-Parameter Tractable (FPT) algorithms for these problems. FPT algorithms have polynomial running time if the parameter under consideration is a constant. This may be relevant even to a practitioner since the parameter k is a small number in many real clustering scenarios. We obtain the following results: • We give 3 and 2 approximation algorithms for the constrained k -supplier and k -center problems, respectively, with FPT running time k O (k) ⋅ n O (1) , where n = | C ∪ L |. Moreover, these approximation guarantees are tight; that is, for any constant ε > 0 , no algorithm can achieve (3 − ε) and (2 − ε) approximation guarantees for the constrained k -supplier and k -center problems in FPT time, assuming FPT ≠ W [ 2 ]. • We study the constrained clustering problem with outliers. Our algorithm gives 3 and 2 approximation guarantees for the constrained outlier k -supplier and k -center problems, respectively, with FPT running time (k + m) O (k) ⋅ n O (1) , where n = | C ∪ L | and m is the number of outliers. • Our techniques generalise for distance function d (. ,.) z. That is, for any positive real number z , if the cost of a client is defined by d (. ,.) z instead of d (. ,.) , then our algorithm gives 3 z and 2 z approximation guarantees for the constrained k -supplier and k -center problems, respectively. • We design 3 approximation algorithm for a wide range of constrained k -supplier problems with FPT running time f (k). p o l y (n). • We design 2 approximation algorithm for a range of constrained k -center problems with FPT running time f (k). p o l y (n). • We extend the algorithm to outlier setting with FPT running time f (k , m). p o l y (n) , where m = number of outliers. • We show matching lower bounds for the constrained k -supplier and k -center problems in the outlier and non-outlier settings. [ABSTRACT FROM AUTHOR]

According to the Jordan Decomposition Theorem, for any function f : [ 0 , 1 ] → R of bounded variation there exists a pair of nondecreasing functions f 1 , f 2 : [ 0 , 1 ] → R with f = f 1 − f 2. Such a pair is called a Jordan decomposition of f. Ko (1991) and Zheng and Rettinger (2005) have analyzed computability-theoretic and complexity-theoretic aspects of this theorem. We complement their observations by three new results. One of our results says that there exists a polynomial time computable, absolutely continuous function f : [ 0 , 1 ] → R with polynomial modulus of absolute continuity such that, on the one hand, the so-called standard Jordan decomposition of f is computable but, on the other hand, neither the standard Jordan decomposition of f nor any other Jordan decomposition of f can be computed easily, say, in polynomial time, or in exponential time. • A function with a non-computable standard Jordan decomposition can have a polytime computable Jordan decomposition. • A computable but not polytime computable standard Jordan decomposition allows a polytime computable Jordan decomposition. • There is a PAC function without polytime computable Jordan decomposition but with computable standard Jordan decomposition. [ABSTRACT FROM AUTHOR]

The independent set polynomial is important in many areas of combinatorics, computer science, and statistical physics. For every integer Δ ≥ 2 , the Shearer threshold is the value λ ⁎ (Δ) = (Δ − 1) Δ − 1 / Δ Δ. It is known that for λ < − λ ⁎ (Δ) , there are graphs G with maximum degree Δ whose independent set polynomial, evaluated at λ , is at most 0. Also, there are no such graphs for any λ > − λ ⁎ (Δ). This paper is motivated by the computational problem of approximating the independent set polynomial when λ < − λ ⁎ (Δ). The key issue in complexity bounds for this problem is "implementation". Informally, an implementation of a real number λ ′ is a graph whose hard-core partition function, evaluated at λ , simulates a vertex-weight of λ ′ in the sense that λ ′ is the ratio between the contribution to the partition function from independent sets containing a certain vertex and the contribution from independent sets that do not contain that vertex. Implementations are the cornerstone of intractability results for the problem of approximately evaluating the independent set polynomial. Our main result is that, for any λ < − λ ⁎ (Δ) , it is possible to implement a set of values that is dense over the reals. The result is tight in the sense that it is not possible to implement a set of values that is dense over the reals for any λ > λ ⁎ (Δ). Our result has already been used in a paper with Bezáková (STOC 2018) to show that it is #P-hard to approximate the evaluation of the independent set polynomial on graphs of degree at most Δ at any value λ < − λ ⁎ (Δ). In the appendix, we give an additional incomparable inapproximability result (strengthening the inapproximability bound to an exponential factor, but weakening the hardness to NP-hardness). [ABSTRACT FROM AUTHOR]

In the famous paper in which he introduced what is now known as the Turing machine, Alan Turing gave a definition of computable real numbers under which it turns out that multiplication by 3 is uncomputable. This shortcoming vanished in a Correction to his paper that Turing himself published shortly afterwards, but it clearly illustrates the subtlety of defining computability issues correctly. In this paper, we give the name "printable" to real numbers that Turing originally called "computable", we recall what is now the generally accepted definition of computable real numbers (which is not quite Turing's amended definition, but is equivalent to it), and we contrast the two notions. Despite the fact that the multiplication by 3 of printable numbers is uncomputable, as opposed to the same operation on computable numbers, a real number is computable if and only if it is printable. The resolution of this apparent paradox is that no machine can transform the "computable" description of a real number to its "printable" description, as Turing proved in his Correction. Finally, we address the subtle issue of allowing or not the printable description of a real number to end with an infinite sequence of 9s (or of 1s in binary), which was left open by Turing in his Correction. Several of these results were already known, as they appear in scattered places, some in non-refereed publications, but we give a unified treatment with some different proofs and a historical perspective. • Alan Turing's original definition of computable real numbers was inadequate. • Turing's use of the word Entscheidungsproblem led us to invent the Druckproblem. • The correct definition of computable real numbers is very subtle. • All computable real numbers are printable, but this is inherently non-constructive. • Discontinuous functions of the real numbers on closed intervals are uncomputable. [ABSTRACT FROM AUTHOR]

We study the decomposition of multivariate polynomials as sums of powers of linear forms. As one of our main results, we give a randomized algorithm for the following problem: given a homogeneous polynomial f (x 1 , ... , x n) of degree 3, decide whether it can be written as a sum of cubes of linearly independent linear forms with complex coefficients. Compared to previous algorithms for the same problem, the two main novel features of this algorithm are: (i) It is an algebraic algorithm, i.e., it performs only arithmetic operations and equality tests on the coefficients of the input polynomial f. In particular, it does not make any appeal to polynomial factorization. (ii) For f ∈ Q [ x 1 , ... , x n ] , the algorithm runs in polynomial time when implemented in the bit model of computation. The algorithm relies on methods from linear and multilinear algebra (symmetric tensor decomposition by simultaneous diagonalization). We also give a version of our algorithm for decomposition over the field of real numbers. In this case, the algorithm performs arithmetic operations and comparisons on the input coefficients. Finally we give several related derandomization results on black box polynomial identity testing, the minimization of the number of variables in a polynomial, the computation of Lie algebras and factorization into products of linear forms. [ABSTRACT FROM AUTHOR]

ksmt is a CDCL-style calculus for solving non-linear constraints over the real numbers involving polynomials and transcendental functions. In this article we investigate properties of the ksmt calculus and show that it is a δ -complete decision procedure for bounded problems. For that purpose we provide concrete algorithms computing linearisations based on either uniform or local moduli of continuity of non-linear functions. The latter method is called local linearisation and is shown to have desirable properties sufficient for termination and which also allow for more efficient treatment of non-linear constraints. Our methods for constructing linearisations are based on computable analysis, in particular we introduce the Cauchy-compatible compact representation of reals and prove its names to be locally compact, allowing for more efficient computation of local linearisations while maintaining δ -completeness. • ksmt is a CDCL-style calculus for solving nonlinear constraints over real numbers involving polynomials and transcendentals. • We show properties of the calculus itself and that it is a δ -complete decision procedure for bounded problems. • Concrete algorithms computing linearisations based on uniform and local moduli of continuity of the non-linear function. • Local linearisations have desirable properties for termination and they allow efficient treatment of nonlinear constraints. • Linearisations based on methods from computable analysis, as is the CCC representation of reals with locally compact names. [ABSTRACT FROM AUTHOR]

Spiking neural P systems (SN P systems) are bio-inspired neural-like computational devices that mimic the communication between two nearby neurons and the spike changes in the neuron. This study incorporates concepts of the weight of synapses and the delay on synapses to increase the bio-explainability (the model can better simulate the communication between neurons), where the weight is a natural feature of synapses, and the delay is that in the communication between neurons, through synapses. Thus, an innovative type of spiking neural-like P system is defined, called spiking neural P systems with weights and delays on synapses (W D S N P systems). In W D S N P systems, synapses are assigned weights and delays, where weights are real numbers and delays are natural numbers. Results proved in this paper show that W D S N P systems can reach Turing universality in the generating and accepting mode. The semi-uniform solution for the Subset Sum problem demonstrates that W D S N P systems can solve NP -complete problems efficiently. [ABSTRACT FROM AUTHOR]