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The simulation of rubber-like materials is usually based on hyperelasticity. If strain-rate dependency has to be considered viscous dampers also have to be taken into account in the rheological model. A disadvantage of such a description is time-consuming parameter identification associated with the damping constants. In this paper, a tabulated formulation is presented which allows the fast generation of input data based on uniaxial static and dynamic tensile tests at different strain rates. Unloading, i.e. forming a hysteresis loop, can also be modeled easily based on a damage formulation. We show the theoretical background and algorithmic setup of our model that has been implemented in the explicit finite element program LS-DYNA [1-3]. Apart from purely numerical examples, the validation of a soft and a hard rubber under loading and subsequent unloading at different strain rates is shown. [ABSTRACT FROM AUTHOR]

This contribution discusses the influence of different barrier update strategies on the performance and robustness of an interior point algorithm for single-crystal plasticity at small strains. To this end, single-crystal plasticity is first briefly presented in the framework of a primal-dual interior point algorithm to outline the general algorithmic structure. The manner in which the barrier parameter is modified within the interior point method, steering the penalization of constraints, plays a crucial role for the robustness and efficiency of the overall algorithm. In this paper, we compare and analyze different strategies in the framework of crystal plasticity. In a thorough analysis of a numerical example covering a broad range of settings in monocrystals, we investigate robust hyperparameter ranges and identify the most efficient and robust barrier parameter update strategies. [ABSTRACT FROM AUTHOR]

Two-dimensional tomographic forward modeling based on first arrivals requires the calculation of the minimum travel times of multiple emission points in a single iteration. The conventional method, which calculates each emission point one by one, produces many unuseful calculations and has low computational efficiency. To solve this problem, given the characteristic that the velocity distribution of a model does not change during a single iteration of forward modeling, an improved algorithm was proposed based on the reciprocity principle and Fermat's principle. The ray tracing results of a small number of emission points were used to constrain the calculation area of other emission points, which reduced the number of unuseful calculations and improved the calculation efficiency of a single-iteration tomographic forward modeling simulation. Theoretical analysis and numerical examples showed that for a homogeneous model, when the transmitting and receiving points were located on two long sides, three adjacent sides (i.e., two long sides and one short side), and four sides, the computational efficiency of the improved algorithm was about 2 times, 2 times, and 1.5 times, respectively, that of the conventional method. For heterogeneous models, the computational efficiency of the improved algorithm was usually more significant. [ABSTRACT FROM AUTHOR]

Selecting financial products is one of the most fundamental investment activities to both individuals and companies, and therefore it is very important to establish an efficient and practical method for financial products selection. To address the challenge of the complicated decision-making environment and decision makers' expression habits for the selection of financial products, this paper develops the incomplete additive probabilistic linguistic preference relation to depict decision makers' preferences. Considering that, when decision makers express their opinions using probabilistic linguistic preference relation, it is possible that the sum of the value of the probability information is more than 1, this paper also extends the concepts of probabilistic linguistic term set, additive probabilistic linguistic preference relation and incomplete additive probabilistic linguistic preference relation to improve and ensure their practicability. Moreover, an "inverse prospect theory-based" algorithm is proposed to choose proper financial products. The algorithm processes the original incomplete additive probabilistic linguistic preference relation by using the inverse functions of the prospect theory at first. Then, a priority weights deriving model is established based on the extended concepts. Finally, the numerical case and analysis is presented as the evidences to the conclusion that the proposed algorithm is practical and robust. [ABSTRACT FROM AUTHOR]

This paper which depended on the foundation of back strip method has established mathematic model to simulate stratum burial history, which considered the computation of stratum skeleton depth based on max depth and the equation-depth point on the procedure to recover stratum by stripping each layer, and put forward the enhanced restoration method taking layers group as studying object. Finally, the method has been applied to build the computer model and program automatic back strip algorithm successfully. Experiments have proved that the method is accurate, applicable, and extensible under the conditions of various complex strata. [ABSTRACT FROM AUTHOR]

The " fast iterative shrinkage-thresholding algorithm " (FISTA) is one of the most famous first order optimization schemes, and the stepsize, which plays an important role in theoretical analysis and numerical experiment, is always determined by a constant relating to the Lipschitz constant or by a backtracking strategy. In this paper, we design a new adaptive non-monotone stepsize strategy (NMS), which allows the stepsize to increase monotonically after finite iterations. It is remarkable that NMS can be successfully implemented without knowing the Lipschitz constant or without backtracking. And the additional cost of NMS is less than the cost of some existing backtracking strategies. For using NMS to the original FISTA (FISTA_NMS) and the modified FISTA (MFISTA_NMS), we show that the convergence results stay the same. Moreover, under the error bound condition, we show that FISTA_NMS achieves the rate of convergence to o 1 k 6 and MFISTA_NMS enjoys the convergence rate related to the value of parameter of t k , that is o 1 k 2 a + 1 ; and the iterates generated by the above two algorithms are convergent. In addition, by taking advantage of the restart technique to accelerate the above two methods, we establish the linear convergences of the function values and iterates under the error bound condition. We conduct some numerical experiments to examine the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The method of regularization factor selection determines stability and accuracy of the regularization method. A formula of regularization factor was proposed by analyzing the relationship between the improved SVD and regularization method. The improved SVD algorithm and regularization method could adapt to low SNR. The regularization method is better than the improved SVD in the case that SNR is below 30 and the improved SVD is better than the regularization method when SNR is higher than 30. The regularization method with the regularization factor proposed in this paper can be better applied into low SNR (5

In this paper, a novel zig-zag scan-based feedback convolution algorithm for image encryption against differential attacks is proposed. The two measures Number of Pixel Change Rate (NPCR) and Unified Average Changed Intensity (UACI) are commonly utilized for analyzing the differential attacks. From the study of the existing papers, even though high Number of Pixel Change Rate and Unified Average Changed Intensity values are obtained, a few values lie in the critical range of α-level significance which in turn increase the possibility of differential attacks. To overcome differential attacks, two aspects of scanning with different test cases are analyzed and from these analyses, it is concluded that zig-zag scan based feedback convolution in forward and reverse direction achieves good Number of Pixel Change Rate and Unified Average Changed Intensity without critical values. Zig-zag scan based feedback convolution in forward and reverse direction is enforced for key sequence generation and applied in diffusion process to achieve high level of security. Moreover, plain image related initial seed is also generated to overcome the chosen/known plain text attacks. Both numerical and theoretical analyses are performed to prove that the proposed encryption method is resistant to differential attacks. General security measures are carried out for the proposed method to validate its security level. From the simulations, it is shown that the proposed methodology has good keyspace, high key sensitivity, good randomness, and uniform distribution of cipher image pixels. [ABSTRACT FROM AUTHOR]

This paper considers the linear weighted complementarity problem (denoted by LWCP) introduced by Potra (SIAM J Optim 22:1634-1654, 2012). Based on two weighted smoothing functions, we propose a new nonmonotone smoothing algorithm for solving the LWCP and establish its global and local quadratic convergence without the strict complementarity assumption. Compared to existing nonmonotone smoothing algorithms, the proposed algorithm solves the linear system only approximately which can save the computation work when one solves large-scale LWCPs. Moreover, the nonmonotone line search technique adopted in this paper includes the usual monotone line search and some existing nonmonotone line searches as special cases. Numerical results show that our algorithm is considerably efficient for solving large-scale LWCPs. [ABSTRACT FROM AUTHOR]

Hessenberg matrices frequently arise in many application areas and have been attracted much attention in recent years. In the current paper, we present two numerical algorithms of $$O(n^2)$$ for computing the determinant of an n-by- n general Hessenberg matrix. The algorithms are all suited for implementation using Computer Algebra Systems such as MATLAB and MAPLE. Some numerical examples are provided in order to demonstrate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Image encryption has been an attractive research filed in recent years. In this paper, we propose a novel hyperchaotic encryption algorithm for color image utilizing DNA dynamic encoding and self-adapting permutation. Firstly, A new 4-dimensional hyperchaotic system is designed, and the detailed dynamic analysis shows that the system has strong pseudo-randomness and a large range of chaotic parameters. Secondly, based on the new 4-D hyperchaotic system, we devise the methods of DNA dynamic encoding, DNA dynamic calculation and DNA dynamic decoding in the image encryption algorithm, and the sequences generated by the hyperchaotic control these coding rules dynamically to make the results of operation more unpredictable. Moreover, the initial keystream is designed dependent upon the plaintext image, and the method of plaintext-related self-adapting permutation is proposed at the bit level and DNA level of the image respectively, which enhances the sensitivity of the algorithm to plaintext image and key. The theoretical analysis and numerical simulation show that the image algorithm has good security and can resist various attacks. [ABSTRACT FROM AUTHOR]

This paper is devoted to the analysis of the sixth-order symplectic and symmetric explicit extended Runge-Kutta-Nyström (ERKN) schemes for solving multi-frequency oscillatory nonlinear Hamiltonian equations. Fourteen practical sixth-order symplectic and symmetric explicit ERKN schemes are constructed, and their phase properties are investigated. The paper is accompanied by five numerical experiments, including a nonlinear two-dimensional wave equation. The numerical results in comparison with the sixth-order symplectic and symmetric Runge-Kutta-Nyström methods and a Gautschi-type method demonstrate the efficiency and robustness of the new explicit schemes for solving multi-frequency oscillatory nonlinear Hamiltonian equations. [ABSTRACT FROM AUTHOR]

Due to the fact that many classical numerical methods have not yet mature quantum counterparts, quantum circuit design is very important in quantum image processing. In this paper, using the novel enhanced quantum representation (NEQR) model, an image encryption algorithm based on Feistel structure is carried out in quantum computer by giving the encryption quantum circuits. First, the modified Feistel structure for image encryption is proposed. It is a 128-bit block cipher and requires 16-bit subkeys to encrypt the image, and it is a mixture of Feistel and substitution–permutation network; then, the detailed quantum circuits design of the encryption algorithm are given. Through numerical simulation and analysis, it is verified that the proposed quantum image encryption algorithm is effective and can resist statistical attacks effectively. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates a near admissible heuristic search strategy and its application to a kind of flexible manufacturing system (FMS) scheduling in a Petri net framework. Petri nets can concisely model the strict precedence constraint, multiple kinds of resources, and concurrent activities. To cope with the complexities for scheduling of FMS with alternative routings, this paper proposes an admissible heuristic function based on the execution of P-timed Petri nets and presents an improved dynamic weighting A* strategy using the proposed heuristic function. The search scheme does not need to predict the depth of solution in advance and the quality of the search result is also controllable. Some numerical experiments are carried out to demonstrate usefulness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper discusses characteristics unique in the two-phase high-frequency-testing (HFT) environment in semiconductor manufacturing. We believe this paper is the first to define, formalize, and analyze the decision making problem associated with the two-phase HFT. Specifically, this paper defines the problem of minimizing the total HFT capacity usage by systematically finding the optimal number of preliminary HFT bin-1 chips subject to the main HFT, with the existence of the target service rate. We also propose a heuristic algorithm that exploits the special structure of the problem for efficiently obtaining a near-optimal solution. Finally, a numerical analysis and a case study have been conducted to gain more insights on the problem structure and the proposed algorithm. [ABSTRACT FROM AUTHOR]

Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in attribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented. [ABSTRACT FROM AUTHOR]

Sunken relief is an art form whereby the depicted shapes are sunk into a given flat plane with a shallow overall depth. In this paper, we propose an efficient sunken relief generation algorithm based on a single image by the technique of feature line enhancement. Our method starts from a single image. First, we smoothen the image with morphological operations such as opening and closing operations and extract the feature lines by comparing the values of adjacent pixels. Then we apply unsharp masking to sharpen the feature lines. After that, we enhance and smoothen the local information to obtain an image with less burrs and jaggies. Differential operations are applied to produce the perceptive relief-like images. Finally, we construct the sunken relief surface by triangularization which transforms two-dimensional information into a three-dimensional model. The experimental results demonstrate that our method is simple and efficient. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most powerful and versatile evolutionary algorithms for efficiently solving complex real-world optimization problems in recent years. Since its introduction in 1995, the research focus in DE has mostly been on the variant side with so many new algorithms proposed based on the original DE algorithm. However, each new algorithm is only suitable for certain fitness landscapes, and, therefore, some types of optimization problems cannot be solved efficiently. To tackle this issue, this paper presents a new self-feedback DE algorithm, named the SFDE; its optimal variation strategy is selected by extracting the local fitness landscape characteristics in each generation population and combing the probability distributions of unimodality and multimodality in each local fitness landscape. The proposed algorithm is tested on a suite of 17 benchmark functions, and the experimental results demonstrated its advantages in a high search dimension in that it can ensure that the population moves to a better fitness landscape, then speeds up convergence to the global optimum, and avoids falling into local optima. [ABSTRACT FROM AUTHOR]

This paper is concerned with mathematical and numerical analysis of the system of radiative integral transfer equations. The existence and uniqueness of solution to the integral system is proved by establishing the boundedness of the radiative integral operators and proving the invertibility of the operator matrix associated with the system. A collocation-boundary element method is developed to discretize the differential-integral system. For the non-convex geometries, an element-subdivision algorithm is developed to handle the computation of the integrals containing the visibility factor. An efficient iterative algorithm is proposed to solve the nonlinear discrete system and its convergence is also established. Numerical experiment results are also presented to verify the effectiveness and accuracy of the proposed method and algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to introduce an algorithm which considers not only time factor but also cost, risk, and quality criteria to determine the critical path under fuzzy environment. In this algorithm first, decision makers allocate time, cost, risk, and quality to each activity. We are lacking in data and information, so in the proposed algorithm, the ratings of each activity and the weight of each criterion are described by fuzzy numbers and linguistic variables, which can be expressed in triangular fuzzy number. Linguistic variables are applied to represent the intensity of preferences of one criterion over another. Then, we add up triangular fuzzy numbers to determine the final evaluation value of each criterion for paths. Next, we use fuzzy TOPSIS, a technique for order preferences by similarity to an ideal solution, a method proposed by the authors in another paper, to choose the best alternative. Finally, numerical example is solved to illustrate the procedure of proposed method at the end of this paper. [ABSTRACT FROM AUTHOR]

The classical method for program analysis by abstract interpretation consists in computing first an increasing sequence using an extrapolation operation, called widening, to correctly approximate the limit of the sequence. Then, this approximation is improved by computing a decreasing sequence without widening, the terms of which are all correct, more and more precise approximations. It is generally admitted that, when the decreasing sequence reaches a fixpoint, it cannot be improved further. As a consequence, most efforts for improving the precision of an analysis have been devoted to improving the limit of the increasing sequence. In a previous paper, we proposed a method to improve a fixpoint after its computation. This method consists in computing from the obtained solution a new starting value from which increasing and decreasing sequences are computed again. The new starting value is obtained by projecting the solution onto well-chosen components. The present paper extends and improves the previous paper: the method is discussed in view of some example programs for which it fails. A new method is proposed to choose the restarting value: the restarting value is no longer a simple projection, but is built by gathering and combining information backward the widening nodes in the basic solution. Experiments show that the new method properly solves all our examples, and improves significantly the results obtained on a classical benchmark. [ABSTRACT FROM AUTHOR]

A novel algorithm for the control synthesis for nonlinear switched systems is presented in this paper. Based on an existing procedure of state-space bisection and made available for nonlinear systems with the help of guaranteed integration, the algorithm has been improved to be able to consider longer patterns of modes with a better pruning approach. Moreover, the use of guaranteed integration also permits to take bounded perturbations and varying parameters into account. It is particularly interesting for safety critical applications, such as in aeronautical, military or medical fields. The whole approach is entirely guaranteed and the induced controllers are correct-by-design. Some experimentations are performed to show the important gain of the new algorithm. [ABSTRACT FROM AUTHOR]

This paper concerns the model updating problems of the ⋆-palindromic quadratic system P(λ)=λ2A⋆+λQ+A, where A,Q∈Cn×n, Q⋆=Q and the symbol ⋆ is used as an abbreviation for transpose or conjugate transpose of complex matrix. Model updating with no spill-over has been a very challenging task in applications. This paper provides some necessary and sufficient solvability conditions on when such an updating for ⋆-palindromic quadratic system is possible. Numerical experiments show that our proposed algorithm works well. [ABSTRACT FROM AUTHOR]

Tempered fractional Laplacian is the generator of the tempered isotropic Lévy process. This paper provides the finite difference discretization for the two-dimensional tempered fractional Laplacian by using the weighted trapezoidal rule and the bilinear interpolation. Then it is used to solve the tempered fractional Poisson equation with homogeneous Dirichlet boundary condition and the error estimate is also derived. Numerical experiments verify the predicted convergence rates and effectiveness of the schemes. [ABSTRACT FROM AUTHOR]

This paper investigates an anti-interference energy-efficient power allocation scheme in the multi-carrier millimeter-wave (mmWave) ultra-dense networks. To suppress the severe intercell-interference, this work proposes a novel interference minimization scheme based on the non-cooperative game theory for energy-efficiency maximization. In each best response, the non-convex problem is transformed into some convex subproblems, and each is solved by a low-complexity stair water-filling (SWF) algorithm over some subintervals. The interference minimization scheme, together with the SWF algorithm, has been proven to converge to a unique Nash equilibrium point. Simulation results and numerical analysis show that the scheme displays significant energy-efficiency performance advantages over other iterative water-filling methods. [ABSTRACT FROM AUTHOR]

This paper focuses on solving a class of multi-criteria optimization with the difference of convex objective functions. Proximal point algorithms, extensively studied for scalar optimization, are extended to our setting. We show that the proposed algorithms are well posed and globally convergent to a critical point. For an application, the new methods are used to a multi-criteria model arising in portfolio optimization. The numerical results show the efficiency of our methods. [ABSTRACT FROM AUTHOR]

In this paper, we mainly study one class of convex mixed-integer nonlinear programming problems (MINLPs) with non-differentiable data. By dropping the differentiability assumption, we substitute gradients with subgradients obtained from KKT conditions, and use the outer approximation method to reformulate convex MINLP as one equivalent MILP master program. By solving a finite sequence of subproblems and relaxed MILP problems, we establish an outer approximation algorithm to find the optimal solution of this convex MINLP. The convergence of this algorithm is also presented. The work of this paper generalizes and extends the outer approximation method in the sense of dealing with convex MINLPs from differentiable case to non-differentiable one. [ABSTRACT FROM AUTHOR]

It has been pointed out by Jones D. R. that the DIRECT global optimization algorithm can quickly get close to the basin of the optimum but takes longer to achieve a high degree of accuracy. In this paper, we introduce a bilevel strategy into a modifed DIRECT algorithm to overcome this shortcoming. The proposed algorithm is proved to be convergent globally. Numerical results show that the proposed algorithm is very promising. [ABSTRACT FROM AUTHOR]

The multiple-sets split equality problem (MSSEP) requires finding a point x ∈∩Ni=1 Ci, y ∈∩Mj=1 Qj, such that Ax = By, where N and M are positive integers, {C1, C2,..., CN} and {Q1,Q2,...,QM} are closed convex subsets of Hilbert spaces H1, H2, respectively, and A : H1→H3, B : H2→H3 are two bounded linear operators. When N = M = 1, the MSSEP is called the split equality problem (SEP). If let B = I, then the MSSEP and SEP reduce to the well-known multiple-sets split feasibility problem (MSSFP) and split feasibility problem (SFP), respectively. Recently, some authors proposed many algorithms to solve the SEP and MSSEP. However, to implement these algorithms, one has to find the projection on the closed convex sets, which is not possible except in simple cases. One of the purposes of this paper is to study the SEP and MSSEP for a family of quasi-nonexpansive multi-valued mappings in the framework of infinite-dimensional Hilbert spaces, and propose an algorithm to solve the SEP and MSSEP without the need to compute the projection on the closed convex sets. [ABSTRACT FROM AUTHOR]

We consider the problem of tensor (i.e., multidimensional array) inpainting in this paper. By using higher-order singular value decomposition, we propose an iterative algorithm that performs soft thresholding on entries of the core tensor and then reconstructs via the directional orthogonal matrices. An inpainted tensor is obtained at the end of the iteration. Simulations conducted over color images, video frames, and MR images validate that the proposed algorithm is competitive with state-of-the-art completion algorithms. The evaluation is made in terms of quality metrics and visual comparison. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for solving structured optimization problems, which covers both the backward-backward and the Douglas-Rachford algorithms as special cases, and analyzes its convergence. The set of fixed points of the corresponding operator is characterized in several cases. Convergence criteria of the algorithm in terms of general fixed point iterations are established. When applied to nonconvex feasibility including potentially inconsistent problems, we prove local linear convergence results under mild assumptions on regularity of individual sets and of the collection of sets. In this special case, we refine known linear convergence criteria for the Douglas-Rachford (DR) algorithm. As a consequence, for feasibility problem with one of the sets being affine, we establish criteria for linear and sublinear convergence of convex combinations of the alternating projection and the DR methods. These results seem to be new. We also demonstrate the seemingly improved numerical performance of this algorithm compared to the RAAR algorithm for both consistent and inconsistent sparse feasibility problems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an algorithm for presentation of an inner estimation of the solution set of a complex interval linear system, where the coefficient matrix is a crisp complex-valued matrix and the right-hand-side vector is an interval complex-valued vector. Also, we show that under some certain conditions, the obtained inner estimation is, in fact, an algebraic solution. [ABSTRACT FROM AUTHOR]

Inherent part of evolutionary algorithms that are based on Darwin’s theory of evolution and Mendel’s theory of genetic heritage, are random processes since genetic algorithms and evolutionary strategies are used. In this paper, we present extended experiments (of our previous) of selected evolutionary algorithms and test functions showing whether random processes really are needed in evolutionary algorithms. In our experiments we used differential evolution and SOMA algorithms with functions 2ndDeJong, Ackley, Griewangk, Rastrigin, SineWave and StretchedSineWave. We use n periodical deterministic processes (based on deterministic chaos principles) instead of pseudo-random number generators (PRGNs) and compare performance of evolutionary algorithms powered by those processes and by PRGNs. Results presented here are numerical demonstrations rather than mathematical proofs. We propose the hypothesis that a certain class of deterministic processes can be used instead of PRGNs without lowering the performance of evolutionary algorithms. [ABSTRACT FROM AUTHOR]

The maximum or minimum spanning tree problem is a classical combinatorial optimization problem. In this paper, we consider the partial inverse maximum spanning tree problem in which the weight function can only be decreased. Given a graph, an acyclic edge set, and an edge weight function, the goal of this problem is to decrease weights as little as possible such that there exists with respect to function containing the given edge set. If the given edge set has at least two edges, we show that this problem is APX-Hard. If the given edge set contains only one edge, we present a polynomial time algorithm. [ABSTRACT FROM AUTHOR]

In a finite precision computing environment, the trajectories of all chaotic sequences enter a cycle leading to degradation of their dynamics. In this paper a new perturbation feedback hybrid control method to reduce the influence of finite precision. A traditional logistic map is introduced as a pseudo-random sequence generator for time-varying perturbation to perturb the coefficients of chaotic map and make them iteratively changed in the chaotic region. The nonlinear feedback mechanism has high complexity. Numerical analysis results show that the perturbation-feedback hybrid control method can effectively attenuate the dynamic degradation of digital chaotic systems. Further, we propose a simple encryption algorithm based on the perturbation-feedback hybrid control method and apply it to image encryption. The NPCR and UACI of our encryption method are 0.99609 and 0.33464, respectively and the information entropy is as high as 7.9976. All the numerical experiments results prove that the proposed algorithm is highly secure, resistant to multiple attacks, and is more competitive than other encryption algorithms. [ABSTRACT FROM AUTHOR]

The diffusion of Device-to-Device (D2D) communications opens the door to exploit the contributions of multiple Mobile Devices (MDs) to accomplish collaborative tasks. In this paper a speaker recognition algorithm for MDs based on a multiple-observations approach is presented. We propose various fusion and clustering algorithms aimed at efficiently exploiting data coming from MDs. Numerical results show that in many cases our multiple-observation approach is able to significantly improve the accuracy of the considered speaker recognition algorithm. [ABSTRACT FROM AUTHOR]

Most of the traditional top- k algorithms are based on a single-server setting. They may be highly inefficient and/or cause huge communication overhead when applied to a distributed system environment. Therefore, the problem of top- k monitoring in distributed environments has been intensively investigated recently. This paper studies how to monitor the top- k data objects with the largest aggregate numeric values from distributed data streams within a fixed-size monitoring window W, while minimizing communication cost across the network. We propose a novel algorithm, which adaptively reallocates numeric values of data objects among distributed nodes by assigning revision factors when local constraints are violated and keeps the local top- k result at distributed nodes in line with the global top- k result. We also develop a framework that combines a distributed data stream monitoring architecture with a sliding window model. Based on this framework, extensive experiments are conducted on top of Apache Storm to verify the efficiency and scalability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose an iterative algorithm for solving the generalized elastic net regularization problem with smoothed $$\ell _{q} (0

Spatial outlier detection is an important research problem that has received much attentions in recent years. Most existing approaches are designed for numerical attributes, but are not applicable to categorical ones (e.g., binary, ordinal, and nominal) that are popular in many applications. The main challenges are the modeling of spatial categorical dependency as well as the computational efficiency. This paper presents the first outlier detection framework for spatial categorical data. Specifically, a new metric, named as Pair Correlation Ratio (PCR), is measured for each pair of category sets based on their co-occurrence frequencies at specific spatial distance ranges. The relevances among spatial objects are then calculated using PCR values with regard to their spatial distances. The outlierness for each object is defined as the inverse of the average relevance between an object and its spatial neighbors. Those objects with the highest outlier scores are returned as spatial categorical outliers. A set of algorithms are further designed for single-attribute and multi-attribute spatial categorical datasets. Extensive experimental evaluations on both simulated and real datasets demonstrated the effectiveness and efficiency of our proposed approaches. [ABSTRACT FROM AUTHOR]

In this paper we propose randomized first-order algorithms for solving bilinear saddle points problems. Our developments are motivated by the need for sublinear time algorithms to solve large-scale parametric bilinear saddle point problems where cheap online assessment of the solution quality is crucial. We present the theoretical efficiency estimates of our algorithms and discuss a number of applications, primarily to the problem of ℓ1 minimization arising in sparsity-oriented signal processing. We demonstrate, both theoretically and by numerical examples, that when seeking for medium-accuracy solutions of large-scale ℓ1 minimization problems, our randomized algorithms outperform significantly (and progressively as the sizes of the problem grow) the state-of-the art deterministic methods. [ABSTRACT FROM AUTHOR]

In practical engineering, the layout optimization technique driven by the thermal performance is faced with a severe computational burden when directly integrating the numerical analysis tool of temperature simulation into the optimization loop. To alleviate this difficulty, this paper presents a novel deep learning surrogate-assisted heat source layout optimization method. First, two sampling strategies, namely the random sampling strategy and the evolving sampling strategy, are proposed to produce diversified training data. Then, regarding mapping between the layout and the corresponding temperature field as an image-to-image regression task, the feature pyramid network (FPN), a kind of deep neural network, is trained to learn the inherent laws, which plays as a surrogate model to evaluate the thermal performance of the domain with respect to different input layouts accurately and efficiently. Finally, the neighborhood search-based layout optimization (NSLO) algorithm is proposed and combined with the FPN surrogate to solve discrete heat source layout optimization problems. A typical two-dimensional heat conduction optimization problem is investigated to demonstrate the feasibility and effectiveness of the proposed deep learning surrogate-assisted layout optimization framework. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a modernized and improved version of the Davison-Man method for the numerical resolution of Sylvester matrix equations. In the case of moderate size problems, we give some background facts about this iterative method, addressing the problem of stagnation and we propose an iterative refinement technique to improve its accuracy. Although not designed to solve large-scale Sylvester equations, we propose an approach combining the extended block Krylov algorithm and the Davison-Man method which gives interesting results in terms of accuracy and shows to be competitive with the classical Krylov subspaces methods. Numerical examples are given to illustrate the efficiency of this approach. [ABSTRACT FROM AUTHOR]

In the paper, we introduce a class of delay differential variational inequalities consisting of a system of delay differential equations and variational inequalities. The existence conclusion of Carathéodory's weak solution for delay differential variational equalities is obtained. Furthermore, an algorithm for solving the delay differential variational inequality is shown, and the convergence analysis for the algorithm is given. Finally, a numerical example is given to verify the validity of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose robust numerical methods for finding the maximum likelihood estimation of the generalized inverse Gaussian distribution. A comparative analysis of the existing algorithms and the results of numerical experiments are presented. Special attention is paid to reproducibility of the tests. [ABSTRACT FROM AUTHOR]

The paper considers the computation of the probability of feasible load constellations in a stationary gas network with uncertain demand. More precisely, a network with a single entry and several exits with uncertain loads is studied. Feasibility of a load constellation is understood in the sense of an existing flow meeting these loads along with given pressure bounds in the pipes. In a first step, feasibility of deterministic exit loads is characterized algebraically and these general conditions are specified to networks involving at most one cycle. This prerequisite is essential for determining probabilities in a stochastic setting when exit loads are assumed to follow some (joint) Gaussian distribution when modeling uncertain customer demand. The key of our approach is the application of the spheric-radial decomposition of Gaussian random vectors coupled with Quasi Monte-Carlo sampling. This approach requires an efficient algorithmic treatment of the mentioned algebraic relations moreover depending on a scalar parameter. Numerical results are illustrated for different network examples and demonstrate a clear superiority in terms of precision over simple generic Monte-Carlo sampling. They lead to fairly accurate probability values even for moderate sample size. [ABSTRACT FROM AUTHOR]

This paper focuses on numerical evaluation techniques related to fluctuation theory for Lévy processes; they can be applied in various domains, e.g., in finance in the pricing of so-called barrier options. More specifically, with $$\bar{X}_t:= \sup _{0\le s\le t} X_s$$ denoting the running maximum of the Lévy process $$X_t$$, the aim is to evaluate $$\mathbb{P }(\bar{X}_t \in \mathrm{d}x)$$ for $$t,x>0$$. The starting point is the Wiener-Hopf factorization, which yields an expression for the transform $$\mathbb E e^{-\alpha \bar{X}_{e(\vartheta )}}$$ of the running maximum at an exponential epoch (with $$\vartheta ^{-1}$$ the mean of this exponential random variable). This expression is first rewritten in a more convenient form, and then it is pointed out how to use Laplace inversion techniques to numerically evaluate $$\mathbb{P }(\bar{X}_t\in \mathrm{d}x).$$ In our experiments we rely on the efficient and accurate algorithm developed in den Iseger (Probab Eng Inf Sci 20:1-44, ). We illustrate the performance of the algorithm with various examples: Brownian motion (with drift), a compound Poisson process, and a jump diffusion process. In models with jumps, we are also able to compute the density of the first time a specific threshold is exceeded, jointly with the corresponding overshoot. The paper is concluded by pointing out how our algorithm can be used in order to analyze the Lévy process' concave majorant. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the performance of the DIRECT global optimization algorithm on problems with linear scaling. We show with computations that the performance of DIRECT can be affected by linear scaling of the objective function. We also provide a theoretical result which shows that DIRECT does not perform well when the absolute value of the objective function is large enough. Then we present DIRECT-a, a modification of DIRECT, to eliminate the sensitivity to linear scaling of the objective function. We prove theoretically that linear scaling of the objective function does not affect the performance of DIRECT-a. Similarly, we prove that some modifications of DIRECT are also unaffected by linear scaling of the objective function, while the original DIRECT algorithm is sensitive to linear scaling. Numerical results in this paper show that DIRECT-a is more robust than the original DIRECT algorithm, which support the theoretical results. Numerical results also show that careful choices of the parameter ε can help DIRECT perform well when the objective function is poorly linearly scaled. [ABSTRACT FROM AUTHOR]

This paper describes an algorithm to solve large-scale maximal entropy problems. The algorithm employs an inexact accelerated proximal gradient method to generate an initial iteration point which is important; then it applies the Newton-CG method to the dual problem. Numerical experiments illustrate that the algorithm can supply an acceptable and even highly accurate solution, while algorithms without generating a good initial point may probably fail. [ABSTRACT FROM AUTHOR]

Long accumulators for the exact summation of floating point numbers or products are well known tools in numerical analysis especially in algorithms verifying the result (C++ Toolbox for Verified Computing, Springer, New York, ). An exact dot product is one of the features of the upcoming interval standard (IEEE Interval Standard Working Group, P1788. ). Usually an accumulator is realized as a memory block with operations to add floating point numbers and products. Several variants have been proposed to avoid carry rippling: use separate accumulators for positive and negative numbers, initialize the accu with a pattern not equal to zero, or perform a kind of carry look-ahead-technique. All these approaches are described in detail in Kulisch (Computer arithmetic and validity-theory, implementation, and applications. Series: de Gruyter Studies in Mathematics 33, ) and Bohlender (Computer arithmetic and self-validating numerical methods, Academic Press, San Diego, ). In this paper we propose a long accumulator similar to a carry-save adder. The main idea is to augment the long accumulator with cache information. The cache is used to store the carries or borrows, instead of propagating them through the whole accumulator every time. Due to the cache, operations are kept local in our approach. The full information of the exact result is represented by the accu and the cache. When we want to deliver the result we have to add the contents of the cache into the accumulator. In this paper we present an implementation in software and compare it with other approaches. Furthermore we discuss the advantages of this algorithm for a hardware implementation. [ABSTRACT FROM AUTHOR]

This paper focuses on the facility location problem with two conflicting objectives. We observe that minimization of the total cost of a particular echelon may lead to the increase in the total cost of a supply chain as a whole. Thus, these conflicting objectives are required to be met together from a supply chain perspective. We have solved the problem formulated in mixed nonlinear programming by a multi-objective evolutionary algorithm (MOEA) known as non-dominated sorting algorithm, or NSGA II in short. Numerical example is provided to show the effect of the algorithm on the solution. [ABSTRACT FROM AUTHOR]