On the complexity of finding paths in a two‐dimensional domain I: Shortest paths

The computational complexity of finding a shortest path in a two‐dimensional domain is studied in the Turing machine‐based computational model and in the discrete complexity theory. This problem is studied with respect to two formulations of polynomial‐time computable two‐dimensional domains: (A) domains with polynomialtime computable boundaries, and (B) polynomial‐time recognizable domains with polynomial‐time computable distance functions. It is proved that the shortest path problem has the polynomial‐space upper bound for domains of both type (A) and type (B); and it has a polynomial‐space lower bound for the domains of type (B), and has a #P lower bound for the domains of type (A). (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A hierarchy of Turing degrees of divergence bounded computable real numbers

A real number x is f-bounded computable (f-bc, for short) for a function f if there is a computable sequence (x_s) of rational numbers which converges to x f-bounded effectively in the sense that, for any natural number n, the sequence (x_s) has at most f(n) non-overlapping jumps of size larger than 2^-n. f-bc reals are called divergence bounded computable if f is computable. In this paper we give a hierarchy theorem for Turing degrees of different classes of f-bc reals. More precisely, we will show that, for any computable functions f and g, if there exists a constant γ>1 such that, for any constant c, f(nγ)+n+cg(n) holds for almost all n, then the classes of Turing degrees given by f-bc and g-bc reals are different. As a corollary this implies immediately the result of [R. Rettinger, X. Zheng, On the Turing degrees of the divergence bounded computable reals, in: CiE 2005, June 8–15, Amsterdam, The Netherlands, Lecture Notes in Computer Science, vol. 3526, 2005, Springer, Berlin, pp. 418–428.] that the classes of Turing degrees of d-c.e. reals and divergence bounded computable reals are different.     

Infinite Time Turing Machines With Only One Tape

Infinite time Turing machines with only one tape are in many respects fully as powerful as their multi‐tape cousins. In particular, the two models of machine give rise to the same class of decidable sets, the same degree structure and, at least for partial functions f : ℝ → ℕ, the same class of computable functions. Nevertheless, there are infinite time computable functions f : ℝ → ℝ that are not one‐tape computable, and so the two models of infinitary computation are not equivalent. Surprisingly, the class of one‐tape computable functions is not closed under composition; but closing it under composition yields the full class of all infinite time computable functions. Finally, every ordinal that is clockable by an infinite time Turing machine is clockable by a one‐tape machine, except certain isolated ordinals that end gaps in the clockable ordinals.     

On the L convergence of Lagrange interpolating entire functions of exponential type

Let f: be a continuous, 2π-periodic function and for each n ε let t_n(f; ·) denote the trigonometric polynomial of degree n interpolating f in the points 2kπ/(2n + 1) (k = 0, ±1, …, ±n). It was shown by J. Marcinkiewicz that lim_{n → ∞} ∝₀^2π¦f(θ) − t_n(f θ)¦^p dθ = 0 for every p > 0. We consider Lagrange interpolation of non-periodic functions by entire functions of exponential type τ > 0 in the points kπ/τ (k = 0, ± 1, ± 2, …) and obtain a result analogous to that of Marcinkiewicz.     

Time complexity of multidimensional Turing machines

It is proved that the work of an indeterminate m-dimensional Turing machine with time complexity t can be simulated on an indeterminate k-dimensional (km) Turing machine with time complexity t^{1–(1/m)+(1/k)+} (for any >0). Moreover, the following generalization to the multidimensional case of the familiar theorem of Hopcroft, Paul, and Valiant is proved: the work of an m-dimensional Turing machine with time complexity t log^1/mt [t(n)n] can be simulated on an address machine working with time complexity t.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 88, pp. 47–55, 1979.The author expresses thanks to A. O. Slisenko for interest in the work, S.V. Pakhamov for helpful discussions, and A. P. Bel'tyukov for valuable comments.     

A problem that is easier to solve on the unit-cost algebraic RAM

Problem 5.1 in (L. Blum, M. Shub, and S. Smale, 1989, Bull. Amer. Math. Soc. 21(1), 1–46) asks if there is a decision problem that cannot be solved in polynomial time by a Turing machine, but can be solved in polynomial time on a unit-cost algebraic RAM with operations {+,-,*,/,<}, and without the integer division operation. We present a problem that is not known to be solvable in polynomial time on a Turing machine, but can be solved in polynomial time on a unit-cost algebraic RAM. This is strong evidence for an affirmative answer to Problem 5.1.     

图灵机计算实数函数的稳定性

定义在全体实数上的可计算函数是一个很重要的概念．在这以前定义可计算的实数函数有两个途径．第一个途径是首先要定义可计算实数的指标．想要确定实数函数y=f（x）是不是可以计算就要看是否存在一个自然数的（部分）递归函数将可计算实数x的指标对应到可计算实数y的指标．这样一来对实数函数的研究依赖于对自然数函数的研究．第二个定义可计算的实数函数的途径是以逼近为基础的．一个实数函数是可以计算的如果它既是序列可计算的同时也是一致连续的．用这个途径来定义可计算实数函数使用的条件过强以至于很多有用的实数函数成为不可计算的实数函数．例如“〈”和“=”的命题函数就是不可以计算的因为它们是不连续的命题函数．本文讨论了图灵机的稳定性并且给出了一个基于稳定图灵机的可计算实数函数的定义．我们的定义不需要用到自然数的（部分）递归函数．根据我们的定义很多常用实数函数特别是一些不连续的常用实数函数都是可以计算的．用我们的定义来讨论可计算实数函数的性质比原来的定义要方便得多．     

An Efficient Algorithm for a Sparse k-Edge-Connectivity Certificate

We present an efficient algorithm for finding a sparse k-edge-connectivity certificate of a multigraph G. Our algorithm runs in O((log kn)(log k)²(log n)²) time using O(k(n + m′)) processors on an ARBITRARY CRCW PRAM, where n and m′ stand for the numbers of vertices in G and edges in the simplified graph of G, respectively.     

Computing Schrödinger propagators on Type-2 Turing machines

We study Turing computability of the solution operators of the initial-value problems for the linear Schrödinger equation u_t=iΔu+φ and the nonlinear Schrödinger equation of the form iu_t=-Δu+mu+|u|²u. We prove that the solution operators are computable if the initial data are Sobolev functions but noncomputable in the linear case if the initial data are L^p-functions and p≠2. The computations are performed on Type-2 Turing machines.     

δ-Uniform BSS Machines

Aδ-uniform BSS machine is a standard BSS machine which does not rely on exact equality tests. We prove that, for any real closed archimedean fieldR, a set isδ-uniformly semi-decidable iff it is open and semi-decidable by a BSS machine which is locally time bounded; we also prove that the local time bound condition is nontrivial. This entails a number of results about BSS machines, in particular the existence of decidable sets whose interior (closure) is not even semi-decidable without adding constants. Finally, we show that the sets semi-decidable by Turing machines are the sets semi-decidable byδ-uniform machines with coefficients inQorT, the field of Turing computable numbers.     

Is wave propagation computable or can wave computers beat the turing machine?

According to the Church-Turing Thesis a number function is computableby the mathematically defined Turing machine if and only ifit is computable by a physical machine. In 1983 Pour-El andRichards defined a three-dimensional wave u(t,x) such that theamplitude u(0,x) at time 0 is computable and the amplitude u(1,x)at time 1 is continuous but not computable. Therefore, theremight be some kind of wave computer beating the Turing machine.By applying the framework of Type 2 Theory of Effectivity (TTE),in this paper we analyze computability of wave propagation.In particular, we prove that the wave propagator is computableon continuously differentiable waves, where one derivative islost, and on waves from Sobolev spaces. Finally, we explainwhy the Pour-El-Richards result probably does not help to designa wave computer which beats the Turing machine. 2000 Mathematical Subject Classification: 03D80, 03F60, 35L05,68Q05.     

Computation Models for Parameterized Complexity

A parameterized computational problem is a set of pairs (x, k), where k is a distinguished item called “parameter”. FPT is the class of fixed-parameter tractable problems: for any fixed value of k, they are solvable in time bounded by a polynomial of degree α, where α is a constant not dependent on the parameter. In order to deal with parameterized intractability, Downey and Fellows have introduced a hierarchy of classes W[l] ? W[2] ? ? containing likely intractable parameterized problems, and they have shown that such classes have many natural, complete languages. In this paper we analyze several variations of the halting problem for nondeterministic Turing machines with parameterized time, and we show that its parameterized complexity strongly depends on some resources like the number of tapes, head and internal states, and on the size of the alphabet. Notice that classical polynomial-time complexity fails in distinguishing such features. As byproducts, we show that parameterized complexity is a useful tool for the study of the intrinsic power of some computational models, and we underline the different “computational powers” of some levels of the parameterized hierarchy.     

The Algebra of Flows in Graphs

We define a contravariant functorKfrom the category of finite graphs and graph morphisms to the category of finitely generated graded abelian groups and homomorphisms. For a graphX, an abelian groupB, and a nonnegative integerj, an element of Hom(K^j(X), B) is a coherent family ofB-valued flows on the set of all graphs obtained by contracting some (j − 1)-set of edges ofX; in particular, Hom(K¹(X), ) is the familiar (real) “cycle-space” ofX. We show thatK ^· (X) is torsion-free and that its Poincaré polynomial is the specializationt^{n − k}T_X(1/t, 1 + t) of the Tutte polynomial ofX(hereXhasnvertices andkcomponents). Functoriality ofK ^· induces a functorial coalgebra structure onK ^· (X); dualizing, for any ringBwe obtain a functorialB-algebra structure on Hom(K ^· (X), B). WhenBis commutative we present this algebra as a quotient of a divided power algebra, leading to some interesting inequalities on the coefficients of the above Poincaré polynomial. We also provide a formula for the theta function of the lattice of integer-valued flows inX, and conclude with 10 open problems.     

Polynomial Hierarchy, Betti Numbers, and a Real Analogue of Toda’s Theorem

Toda (in SIAM J. Comput. 20(5):865–877, 1991) proved in 1989 that the (discrete) polynomial time hierarchy, PH, is contained in the class P ^#P , namely the class of languages that can be decided by a Turing machine in polynomial time given access to an oracle with the power to compute a function in the counting complexity class #P. This result, which illustrates the power of counting, is considered to be a seminal result in computational complexity theory. An analogous result in the complexity theory over the reals (in the sense of Blum–Shub–Smale real machines in Bull. Am. Math. Soc. (NS) 21(1): 1–46, 1989) has been missing so far. In this paper we formulate and prove a real analogue of Toda’s theorem. Unlike Toda’s proof in the discrete case, which relied on sophisticated combinatorial arguments, our proof is topological in nature. As a consequence of our techniques, we are also able to relate the computational hardness of two extremely well-studied problems in algorithmic semi-algebraic geometry: the problem of deciding sentences in the first-order theory of the reals with a constant number of quantifier alternations, and that of computing Betti numbers of semi-algebraic sets. We obtain a polynomial time reduction of the compact version of the first problem to the second. This latter result may be of independent interest to researchers in algorithmic semi-algebraic geometry.     

Kolmogorov complexity and characteristic constants of formal theories of arithmetic

We investigate two constants c _T and r _T , introduced by Chaitin and Raatikainen respectively, defined for each recursively axiomatizable consistent theory T and universal Turing machine used to determine Kolmogorov complexity. Raatikainen argued that c _T does not represent the complexity of T and found that for two theories S and T , one can always find a universal Turing machine such that $c_\mathbf {S}= c_\mathbf {T}$. We prove the following are equivalent: $c_\mathbf {S}\ne c_\mathbf {T}$ for some universal Turing machine, $r_\mathbf {S}\ne r_\mathbf {T}$ for some universal Turing machine, and T proves some Π₁‐sentence which S cannnot prove. © 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim     

Compactly supported wavelet bases for Sobolev spaces

In this paper we investigate compactly supported wavelet bases for Sobolev spaces. Starting with a pair of compactly supported refinable functions φ and in satisfying a very mild condition, we provide a general principle for constructing a wavelet ψ such that the wavelets ψ_jk:=2^j/2ψ(2^j·−k) ( ) form a Riesz basis for . If, in addition, φ lies in the Sobolev space , then the derivatives 2^j/2ψ^(m)(2^j·−k) ( ) also form a Riesz basis for . Consequently, is a stable wavelet basis for the Sobolev space . The pair of φ and are not required to be biorthogonal or semi-orthogonal. In particular, φ and can be a pair of B-splines. The added flexibility on φ and allows us to construct wavelets with relatively small supports.     

The complexity of detecting fixed-density clusters

We study the complexity of finding a subgraph of a certain size and a certain density, where density is measured by the average degree. Let γ:N→Q₊ be any density function, i.e., γ is computable in polynomial time and satisfies γ(k)?k-1 for all k∈N. Then γ-CLUSTER is the problem of deciding, given an undirected graph G and a natural number k, whether there is a subgraph of G on k vertices that has average degree at least γ(k). For γ(k)=k-1, this problem is the same as the well-known CLIQUE problem, and thus NP-complete. In contrast to this, the problem is known to be solvable in polynomial time for γ(k)=2. We ask for the possible functions γ such that γ-CLUSTER remains NP-complete or becomes solvable in polynomial time. We show a rather sharp boundary: γ CLUSTER is NP-complete if γ=2+Ω(1/k^1-ε) for some ε>0 and has a polynomial-time algorithm for γ=2+O(1/k). The algorithm also shows that for γ=2+O(1/k^1-o(1)), γ-CLUSTER is solvable in subexponential time 2^no(1).     

Monotonically Computable Real Numbers

Area number x is called k‐monotonically computable (k‐mc), for constant k > 0, if there is a computable sequence (x_n)_{n ∈ ℕ} of rational numbers which converges to x such that the convergence is k‐monotonic in the sense that k · |x — x_n| ≥ |x — x_m| for any m > n and x is monotonically computable (mc) if it is k‐mc for some k > 0. x is weakly computable if there is a computable sequence (x_s)_{s ∈ ℕ} of rational numbers converging to x such that the sum $\sum _{s \in \mathbb{N}}$|x_s — x_{s + 1}| is finite. In this paper we show that a mc real numbers are weakly computable but the converse fails. Furthermore, we show also an infinite hierarchy of mc real numbers.     

Turing oracle machines, online computing, and three displacements in computability theory

We begin with the history of the discovery of computability in the 1930’s, the roles of Gödel, Church, and Turing, and the formalisms of recursive functions and Turing automatic machines (a-machines). To whom did Gödel credit the definition of a computable function? We present Turing’s notion [1939, §4] of an oracle machine (o-machine) and Post’s development of it in [1944, §11], [1948], and finally Kleene-Post [1954] into its present form.A number of topics arose from Turing functionals including continuous functionals on Cantor space and online computations. Almost all the results in theoretical computability use relative reducibility and o-machines rather than a-machines and most computing processes in the real world are potentially online or interactive. Therefore, we argue that Turing o-machines, relative computability, and online computing are the most important concepts in the subject, more so than Turing a-machines and standard computable functions since they are special cases of the former and are presented first only for pedagogical clarity to beginning students. At the end in §10–§13 we consider three displacements in computability theory, and the historical reasons they occurred. Several brief conclusions are drawn in §14.     

Triply-Logarithmic Parallel Upper and Lower Bounds for Minimum and Range Minima over Small Domains

We consider the problem of computing the minimum ofnvalues, and several well-known generalizations [prefix minima, range minima, and all nearest smaller values (ANSV)] for input elements drawn from the integer domain [1···s], wheres ≥ n. In this article we give simple and efficient algorithms for all of the preceding problems. These algorithms all takeO(log log log s) time using an optimal number of processors andO(ns^ε) space (for constant ε < 1) on the COMMON CRCW PRAM. The best known upper bounds for the range minima and ANSV problems were previouslyO(log log n) (using algorithms for unbounded domains). For the prefix minima and for the minimum problems, the improvement is with regard to the model of computation. We also prove a lower bound of Ω(log log n) for domain sizes = 2^{Ω(log n log log n)}. Since, forsat the lower end of this range, log log n = Ω(log log log s), this demonstrates that any algorithm running ino(log log log s) time must restrict the range ofson which it works.