Solving Convection-Diffusion Problems on Curved Domains by Extensions from Subdomains

We present a technique for numerically solving convection-diffusion problems in domains $\varOmega $ with curved boundary. The technique consists in approximating the domain $\varOmega $ by polyhedral subdomains $\mathsf{{D}}_h$ where a finite element method is used to solve for the approximate solution. The approximation is then suitably extended to the remaining part of the domain $\varOmega $ . This approach allows for the use of only polyhedral elements; there is no need of fitting the boundary in order to obtain an accurate approximation of the solution. To achieve this, the boundary condition on the border of $\varOmega $ is transferred to the border of $\mathsf{D }_h$ by using simple line integrals. We apply this technique to the hybridizable discontinuous Galerkin method and provide extensive numerical experiments showing that, whenever the distance of $\mathsf{{D}}_h$ to $\partial \varOmega $ is of order of the meshsize $h$ , the convergence properties of the resulting method are the same as those for the case in which $\varOmega =\mathsf{{D}}_h$ . We also show numerical evidence indicating that the ratio of the $L^2(\varOmega )$ norm of the error in the scalar variable computed with $d>0$ to that of that computed with $d=0$ remains constant (and fairly close to one), whenever the distance $d$ is proportional to $\min \{h,Pe^{-1}\}/(k+1)^2$ , where $Pe$ is the so-called Péclet number.     

Distinguishing and relating higher-order and first-order processes by expressiveness

This is a paper on distinguishing and relating two important kinds of calculi through expressiveness, settling some critical but long unanswered questions. The delimitation of higher-order and first-order process calculi is a basic and pivotal topic in the study of process theory. Particularly, expressiveness studies mutual encodability, which helps decide whether process-passing or name-passing is more fundamental, and the way they ought to be used in both theory and practice. In this paper, we contribute to such demarcation with three major results. Firstly $\pi $ (first-order pi-calculus) can faithfully express $\varPi $ (basic higher-order pi-calculus). The calculus $\varPi $ has the elementary operators (input, output, composition and restriction). This actually is a corollary of a more general result, that $\pi $ can encode $\varPi ^r$ ( $\varPi $ enriched with the relabelling operator). Secondly $\varPi $ cannot interpret $\pi $ reasonably. This is of more significance since it separates $\varPi $ and $\pi $ by drawing a well-defined boundary. Thirdly an encoding from $\pi $ to $\varPi ^r$ is revisited and discussed, which not only implies how to make $\varPi $ more useful but also stresses the importance of name-passing in $\pi $ .     

Gaussian,Lobatto and Radau positive quadrature rules with a prescribed abscissa

For a given $\theta \in (a,b)$ , we investigate the question whether there exists a positive quadrature formula with maximal degree of precision which has the prescribed abscissa $\theta $ plus possibly $a$ and/or $b$ , the endpoints of the interval of integration. This study relies on recent results on the location of roots of quasi-orthogonal polynomials. The above positive quadrature formulae are useful in studying problems in one-sided polynomial $L_1$ approximation.     

Simple characterization of strict residuated lattices with an involutive negation

We consider property of strict residuated lattices (SRL-algebras) with a new involutive negation $\lnot, $ called here by SRL $_{\lnot }$ -algebras, and give a simple characterization of SRL $_{\lnot }$ -algebras. We also prove a prime filter theorem of SRL $_{\lnot }$ -algebras, from which we conclude that every linearly ordered SRL $_{\lnot }$ -algebra is simple. As a corollary to this fact, we have a well-known result that every SML $_{\lnot }$ -algebra (SBL $_{\lnot }$ -algebra) is a subdirect product of linearly ordered SML $_{\lnot }$ -algebras (SBL $_{\lnot }$ -algebras).     

Optimal quadrature formulas in the sense of Sard in W_2^{(m,m-1)} space

This paper studies the problem of construction of optimal quadrature formulas in the sense of Sard in the $W_2^{(m,m-1)}(0,1)$ space. Using the Sobolev’s method we obtain new optimal quadrature formulas of such type for $N+1\ge m$ , where $N+1$ is the number of the nodes. Moreover, explicit formulas of the optimal coefficients are obtained. We investigate the order of convergence of the optimal formula for $m=1$ and prove an asymptotic optimality of such a formula in the Sobolev space $L_2^{(1)}(0,1)$ . It turns out that the error of the optimal quadrature formula in $W_2^{(1,0)}(0,1)$ is less than the error of the optimal quadrature formula given in the $L_2^{(1)}(0,1)$ space. The obtained optimal quadrature formula in the $W_2^{(m,m-1)}(0,1)$ space is exact for $\exp (-x)$ and $P_{m-2}(x)$ , where $P_{m-2}(x)$ is a polynomial of degree $m-2$ . Furthermore, some numerical results, which confirm the obtained theoretical results of this work, are given.     

Two-Grid Methods for Hermitian positive definite linear systems connected with an order relation

Given a multigrid procedure for linear systems with coefficient matrices $A_n,$ we discuss the optimality of a related multigrid procedure with the same smoother and the same projector, when applied to properly related algebraic problems with coefficient matrices $B_n$ : we assume that both $A_n$ and $B_n$ are Hermitian positive definite with $A_n\le \vartheta B_n,$ for some positive $\vartheta $ independent of $n.$ In this context we prove the Two-Grid Method optimality. We apply this elementary strategy for designing a multigrid solution for modifications of multilevel structured linear systems, in which the Hermitian positive definite coefficient matrix is banded in a multilevel sense. As structured matrices, Toeplitz, circulants, Hartley, sine ( $\tau $ class) and cosine algebras are considered. In such a way, several linear systems arising from the approximation of integro–differential equations with various boundary conditions can be efficiently solved in linear time (with respect to the size of the algebraic problem). Some numerical experiments are presented and discussed, both with respect to Two-Grid and multigrid procedures.     

Quantum discord in spin systems with dipole–dipole interaction

The behavior of total quantum correlations (discord) in dimers consisting of dipolar-coupled spins 1/2 are studied. We found that the discord $Q=0$ at absolute zero temperature. As the temperature $T$ increases, the quantum correlations in the system increase at first from zero to its maximum and then decrease to zero according to the asymptotic law $T^{-2}$ . It is also shown that in absence of external magnetic field $B$ , the classical correlations $C$ at $T\rightarrow 0$ are, vice versa, maximal. Our calculations predict that in crystalline gypsum $\hbox {CaSO}_{4}\cdot \hbox {2H}_{2}{\hbox {O}}$ the value of natural $(B=0)$ quantum discord between nuclear spins of hydrogen atoms is maximal at the temperature of 0.644  $\upmu $ K, and for 1,2-dichloroethane $\hbox {H}_{2}$ ClC– $\hbox {CH}_{2}{\hbox {Cl}}$ the discord achieves the largest value at $T=0.517~\upmu $ K. In both cases, the discord equals $Q\approx 0.083$  bit/dimer what is $8.3\,\%$ of its upper limit in two-qubit systems. We estimate also that for gypsum at room temperature $Q\sim 10^{-18}$  bit/dimer, and for 1,2-dichloroethane at $T=90$  K the discord is $Q\sim 10^{-17}$  bit per a dimer.     

Reduced ordered binary decision diagram with implied literals: a new knowledge compilation approach

Reduced ordered binary decision diagram (ROBDD) is one of the most influential knowledge compilation languages. We generalize it by associating some implied literals with each node to propose a new language called ROBDD with implied literals (ROBDD- $L$ ) and show that ROBDD- $L$ can meet most of the querying requirements involved in the knowledge compilation map. Then, we discuss a kind of subsets of ROBDD- $L$ called ROBDD- $L_i$ with precisely $i$ implied literals $(0\le i\le \infty )$ , where ROBDD- $L_0$ is isomorphic to ROBDD. ROBDD- $L_i$ has uniqueness over any given linear order of variables. We mainly focus on ROBDD- $L_\infty $ and demonstrate that: (a) it is a canonical representation on any given variable order; (b) it is the most succinct subset in ROBDD- $L$ and thus also meets most of the querying requirements; (c) given any logical operation ROBDD supports in polytime, ROBDD- $L_\infty $ can also support it in time polynomial in the sizes of the equivalent ROBDDs. Moreover, we propose an ROBDD- $L_i$ compilation algorithm for any $i$ and an ROBDD- $L_\infty $ compilation algorithm, and then we implement an ROBDD- $L$ package called BDDjLu. Our preliminary experimental results indicate that: (a) the compilation results of ROBDD- $L_\infty $ are significantly smaller than those of ROBDD; (b) the standard d-DNNF compiler c2d and our ROBDD- $L_\infty $ compiler do not dominate the other, yet ROBDD- $L_\infty $ has canonicity and supports more querying requirements and relatively efficient logical operations; and (c) the method that first compiles knowledge base into ROBDD- $L_\infty $ and then converts ROBDD- $L_\infty $ into ROBDD provides an efficient ROBDD compiler.     

The Nonconvergence of h-Refinement in Prolate Elements

Prolate elements are a “plug-compatible” modification of spectral elements in which Legendre polynomials are replaced by prolate spheroidal wave functions of order zero. Prolate functions contain a“bandwidth parameter” $c \ge 0 $ c ≥ 0 whose value is crucial to numerical performance; the prolate functions reduce to Legendre polynomials for $c\,=\,0$ c = 0 . We show that the optimal bandwidth parameter $c$ c not only depends on the number of prolate modes per element $N$ N , but also on the element widths $h$ h . We prove that prolate elements lack $h$ h -convergence for fixed $c$ c in the sense that the error does not go to zero as the element size $h$ h is made smaller and smaller. Furthermore, the theoretical predictions that Chebyshev and Legendre polynomials require $\pi $ π degrees of freedom per wavelength to resolve sinusoidal functions while prolate series need only 2 degrees of freedom per wavelength are asymptotic limits as $N \rightarrow \infty $ N → ∞ ; we investigate the rather different behavior when $N \sim O(4-10)$ N ～ O ( 4 ? 10 ) as appropriate for spectral elements and prolate elements. On the other hand, our investigations show that there are certain combinations of $N,\,h$ N , h and $c>0$ c > 0 where a prolate basis clearly outperforms the Legendre polynomial approximation.     

Completions of cut systems in Q-sets

Let $ Q$ be a complete residuated lattice. Let $\text {SetR}(Q)$ be the category of sets with similarity relations with values in $ Q$ (called $ Q$ -sets), which is an analogy of the category of classical sets with relations as morphisms. A cut in an $ Q$ -set $(A,\delta )$ is a system $(C_{\alpha })_{\alpha \in Q}$ , where $C_{\alpha }$ are subsets of $A\times Q$ . It is well known that in the category $\text {SetR}(Q)$ , there is a close relation between special cuts (called f-cuts) in an $ Q$ -set on one hand and fuzzy sets in the same $ Q$ -set, on the other hand. Moreover, there exists a completion procedure according to which any cut $(C_{\alpha })_{\alpha }$ can be extended onto an f-cut $(\overline{C_{\alpha }})_{\alpha }$ . In the paper, we prove that the completion procedure is, in some sense, the best possible. This will be expressed by the theorem which states that the category of f-cuts is a full reflective subcategory in the category of cuts.     

General DG-Methods for Highly Indefinite Helmholtz Problems

We develop a stability and convergence theory for a Discontinuous Galerkin formulation (DG) of a highly indefinite Helmholtz problem in $\mathbb R ^{d}$ , $d\in \{1,2,3\}$ . The theory covers conforming as well as non-conforming generalized finite element methods. In contrast to conventional Galerkin methods where a minimal resolution condition is necessary to guarantee the unique solvability, it is proved that the DG-method admits a unique solution under much weaker conditions. As an application we present the error analysis for the $hp$ -version of the finite element method explicitly in terms of the mesh width $h$ , polynomial degree $p$ and wavenumber $k$ . It is shown that the optimal convergence order estimate is obtained under the conditions that $kh/\sqrt{p}$ is sufficiently small and the polynomial degree $p$ is at least $O(\log k)$ . On regular meshes, the first condition is improved to the requirement that $kh/p$ be sufficiently small.     

Optimized Parameter Search for Large Datasets of the Regularization Parameter and Feature Selection for Ridge Regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order $O(R K N^{2} M)$ with $R$ the number of applied regularization parameters, $K$ the number of folds in the validation set, $N$ the number of input features and $M$ the number of data samples in the training set. Our implementation has a computational complexity of the order $O(KN^3)$ . This computational cost is smaller than that of regression without regularization $O(N^2M)$ for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity $O(RKNN_s^3)$ and $O(RKN^3N_r)$ for forward and backward feature selection respectively, with $N_s$ the number of selected features and $N_r$ the number of removed features. This is an order $M$ faster than $O(RKNN_s^3M)$ and $O(RKN^3N_rM)$ for the naive implementation, with $N \ll M$ for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines.     

On matrices, automata, and double counting in constraint programming

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables $\mathcal{M}$ , with the same constraint C defined by a finite-state automaton $\mathcal{A}$ on each row of $\mathcal{M}$ and a global cardinality constraint $\mathit{gcc}$ on each column of $\mathcal{M}$ . We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the $\mathit{gcc}$ constraints from the automaton $\mathcal{A}$ . The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We also provide a domain consistency filtering algorithm for the conjunction of lexicographic ordering constraints between adjacent rows of $\mathcal{M}$ and (possibly different) automaton constraints on the rows. We evaluate the impact of our methods in terms of runtime and search effort on a large set of nurse rostering problem instances.     

Interpolation splines minimizing a semi-norm

Using S.L. Sobolev’s method, we construct the interpolation splines minimizing the semi-norm in $K_2(P_2)$ , where $K_2(P_2)$ is the space of functions $\phi $ such that $\phi ^{\prime } $ is absolutely continuous, $\phi ^{\prime \prime } $ belongs to $L_2(0,1)$ and $\int _0^1(\varphi ^{\prime \prime }(x)+\varphi (x))^2dx<\infty $ . Explicit formulas for coefficients of the interpolation splines are obtained. The resulting interpolation spline is exact for the trigonometric functions $\sin x$ and $\cos x$ . Finally, in a few numerical examples the qualities of the defined splines and $D^2$ -splines are compared. Furthermore, the relationship of the defined splines with an optimal quadrature formula is shown.     

Arithmetizing Classes Around \textsf{NC} 1 and \textsf{L}

The parallel complexity class $\textsf{NC}$ ¹ has many equivalent models such as polynomial size formulae and bounded width branching programs. Caussinus et al. (J. Comput. Syst. Sci. 57:200–212, 1992) considered arithmetizations of two of these classes, $\textsf{\#NC}$ ¹ and $\textsf{\#BWBP}$ . We further this study to include arithmetization of other classes. In particular, we show that counting paths in branching programs over visibly pushdown automata is in $\textsf{FLogDCFL}$ , while counting proof-trees in logarithmic width formulae has the same power as $\textsf{\#NC}$ ¹. We also consider polynomial-degree restrictions of $\textsf{SC}$ ⁱ , denoted $\textsf{sSC}$ ⁱ , and show that the Boolean class $\textsf{sSC}$ ¹ is sandwiched between $\textsf{NC}$ ¹ and $\textsf{L}$ , whereas $\textsf{sSC}$ ⁰ equals $\textsf{NC}$ ¹. On the other hand, the arithmetic class $\textsf{\#sSC}$ ⁰ contains $\textsf{\#BWBP}$ and is contained in $\textsf{FL}$ , and $\textsf{\#sSC}$ ¹ contains $\textsf{\#NC}$ ¹ and is in $\textsf{SC}$ ². We also investigate some closure properties of the newly defined arithmetic classes.     

Computational complexity and approximability of social welfare optimization in multiagent resource allocation

A central task in multiagent resource allocation, which provides mechanisms to allocate (bundles of) resources to agents, is to maximize social welfare. We assume resources to be indivisible and nonshareable and agents to express their utilities over bundles of resources, where utilities can be represented in the bundle form, the $k$ -additive form, and as straight-line programs. We study the computational complexity of social welfare optimization in multiagent resource allocation, where we consider utilitarian and egalitarian social welfare and social welfare by the Nash product. Solving some of the open problems raised by Chevaleyre et al. (2006) and confirming their conjectures, we prove that egalitarian social welfare optimization is $\mathrm{NP}$ -complete for the bundle form, and both exact utilitarian and exact egalitarian social welfare optimization are $\mathrm{DP}$ -complete, each for both the bundle and the $2$ -additive form, where $\mathrm{DP}$ is the second level of the boolean hierarchy over  $\mathrm{NP}$ . In addition, we prove that social welfare optimization by the Nash product is $\mathrm{NP}$ -complete for both the bundle and the $1$ -additive form, and that the exact variants are $\mathrm{DP}$ -complete for the bundle and the $3$ -additive form. For utility functions represented as straight-line programs, we show $\mathrm{NP}$ -completeness for egalitarian social welfare optimization and social welfare optimization by the Nash product. Finally, we show that social welfare optimization by the Nash product in the $1$ -additive form is hard to approximate, yet we also give fully polynomial-time approximation schemes for egalitarian and Nash product social welfare optimization in the $1$ -additive form with a fixed number of agents.     

Local Discontinuous Galerkin Methods for One-Dimensional Second Order Fully Nonlinear Elliptic and Parabolic Equations

This paper is concerned with developing accurate and efficient nonstandard discontinuous Galerkin methods for fully nonlinear second order elliptic and parabolic partial differential equations (PDEs) in the case of one spatial dimension. The primary goal of the paper to develop a general framework for constructing high order local discontinuous Galerkin (LDG) methods for approximating viscosity solutions of these fully nonlinear PDEs which are merely continuous functions by definition. In order to capture discontinuities of the first order derivative $u_x$ of the solution $u$ , two independent functions $q^-$ and $q^+$ are introduced to approximate one-sided derivatives of $u$ . Similarly, to capture the discontinuities of the second order derivative $u_{xx}$ , four independent functions $p^{- -}, p^{- +}, p^{+ -}$ , and $p^{+ +}$ are used to approximate one-sided derivatives of $q^-$ and $q^+$ . The proposed LDG framework, which is based on a nonstandard mixed formulation of the underlying PDE, embeds a given fully nonlinear problem into a mostly linear system of equations where the given nonlinear differential operator must be replaced by a numerical operator which allows multiple value inputs of the first and second order derivatives $u_x$ and $u_{xx}$ . An easy to verify set of criteria for constructing “good” numerical operators is also proposed. It consists of consistency and generalized monotonicity. To ensure such a generalized monotonicity property, the crux of the construction is to introduce the numerical moment in the numerical operator, which plays a critical role in the proposed LDG framework. The generalized monotonicity gives the LDG methods the ability to select the viscosity solution among all possible solutions. The proposed framework extends a companion finite difference framework developed by Feng and Lewis (J Comp Appl Math 254:81–98, 2013) and allows for the approximation of fully nonlinear PDEs using high order polynomials and non-uniform meshes. Numerical experiments are also presented to demonstrate the accuracy, efficiency and utility of the proposed LDG methods.     

\lambda -Statistical convergence of fuzzy numbers and fuzzy functions of order \theta

In this paper, we introduce the concept of $\lambda $ -statistical convergence of order $\theta $ and strong $\lambda $ -summability of order $\theta $ for the sequence of fuzzy numbers. Further the same concept is extended to the sequence of fuzzy functions and introduce the spaces like $S_\lambda ^\theta (\hat{f})$ and $\omega _{\lambda p} ^\theta (\hat{f})$ . Some inclusion relations in those spaces and also the underlying relation between these two spaces are also obtained.     

Uncertainty and certainty relations for complementary qubit observables in terms of Tsallis’ entropies

Uncertainty relations for more than two observables have found use in quantum information, though commonly known relations pertain to a pair of observables. We present novel uncertainty and certainty relations of state-independent form for the three Pauli observables with use of the Tsallis $\alpha $ -entropies. For all real $\alpha \in (0;1]$ and integer $\alpha \ge 2$ , lower bounds on the sum of three $\alpha $ -entropies are obtained. These bounds are tight in the sense that they are always reached with certain pure states. The necessary and sufficient condition for equality is that the qubit state is an eigenstate of one of the Pauli observables. Using concavity with respect to the parameter $\alpha $ , we derive approximate lower bounds for non-integer $\alpha \in (1;+\infty )$ . In the case of pure states, the developed method also allows to obtain upper bounds on the entropic sum for real $\alpha \in (0;1]$ and integer $\alpha \ge 2$ . For applied purposes, entropic bounds are often used with averaging over the individual entropies. Combining the obtained bounds leads to a band, in which the rescaled average $\alpha $ -entropy ranges in the pure-state case. A width of this band is essentially dependent on $\alpha $ . It can be interpreted as an evidence for sensitivity in quantifying the complementarity.     

The Local Discontinuous Galerkin Method for the Fourth-Order Euler–Bernoulli Partial Differential Equation in One Space Dimension. Part I: Superconvergence Error Analysis

In this paper we develop and analyze a new superconvergent local discontinuous Galerkin (LDG) method for approximating solutions to the fourth-order Euler–Bernoulli beam equation in one space dimension. We prove the $L^2$ stability of the scheme and several optimal $L^2$ error estimates for the solution and for the three auxiliary variables that approximate derivatives of different orders. Our numerical experiments demonstrate optimal rates of convergence. We also prove superconvergence results towards particular projections of the exact solutions. More precisely, we prove that the LDG solution and its spatial derivatives (up to third order) are $\mathcal O (h^{k+3/2})$ super close to particular projections of the exact solutions for $k$ th-degree polynomial spaces while computational results show higher $\mathcal O (h^{k+2})$ convergence rate. Our proofs are valid for arbitrary regular meshes and for $P^k$ polynomials with $k\ge 1$ , and for periodic, Dirichlet, and mixed boundary conditions. These superconvergence results will be used to construct asymptotically exact a posteriori error estimates by solving a local steady problem on each element. This will be reported in Part II of this work, where we will prove that the a posteriori LDG error estimates for the solution and its derivatives converge to the true errors in the $L^2$ -norm under mesh refinement.