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This paper presents a new numerical method to solve the non-linear constitutive equations of viscoelastic string with variable fractional order model. Firstly, the non-linear constitutive equation of the string is established based on the variable fractional viscoelastic model. Secondly, non-linear constitutive equation of the string is transformed into a matrix equation by using Bernstein polynomials, which can greatly simplify the solving process. Then the matrix equation is discretized and solved to obtain the numerical solutions. Finally, numerical results of viscoelastic string show the effect of the axial tension and axial velocity on its displacement, velocity and acceleration. It also indicates that how dynamic behaviours are affected by external force excitation. In addition, the proposed algorithm shows enormous potentials for solving the problem of high precision dynamics with the variable fractional order model. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive generalised function projective synchronisation scheme of fractional-order chaotic systems with different dimensions and orders and fully unknown parameters is presented. On the basis of the Lyapunov method of fractional-order systems, a stability theorem of the fractional-order system with non-identical orders is proven. Using the fractional-order controller and adaptive control theory, sufficient conditions for synchronisation and unknown parameters update rules are obtained. Theoretical analysis and numerical simulations are provided to verify the validity of the proposed scheme. Moreover, synchronisation results are applied to secure communication via modified chaotic masking (MCM) method. The unpredictability of the scaling function matrix and the use of fractional-order systems with different orders can increase the security of the cryptosystem. The security analysis shows that the introduced algorithm has large key space, high sensitivity to encryption keys, higher security and the acceptable encryption speed. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization, which decomposes a target matrix into the product of two matrices with nonnegative elements, has been widely used in various fields of science, engineering and technology. In this paper, we consider the more general Q-weighted nonnegative matrix factorization (QWNMF) problem. By using the additive representation of the Q-weighted norm, the QWNMF problem is transformed into an unconstraint optimization problem, and then a new iterative algorithm is designed to solve it. The numerical analysis of this algorithm is also given. Numerical examples show that the new method is feasible and effective. [ABSTRACT FROM AUTHOR]

In this paper, we study, from the numerical point of view, a dynamic problem involving a mixture of two viscoelastic solids. The mechanical problem is written as a system of two coupled partial differential equations. Its variational formulation is derived and an existence and uniqueness result, and an energy decay property, are recalled. Then, fully discrete approximations are introduced by using the classical finite element method and the implicit Euler scheme. A discrete stability property and a priori error estimates are shown, from which we deduce the linear convergence of the algorithm. Finally, some numerical simulations, including examples in one and two dimensions, are presented to show the accuracy of the approximation and the behaviour of the solution. [ABSTRACT FROM AUTHOR]

The finite-difference (FD) scheme is extensively applied in seismic modelling, imaging and inversion due to its advantages of large-scale parallel computing and programming. However, numerical dispersion caused by using a difference operator in substitution for the differential operator is non-negligible, which reduces the accuracy of the modelling and can lead to some misinterpretations. In addition, the computing resources required by the FD scheme is highly demanding when dealing with large models, which limits its applicability. In this paper, a new optimised FD scheme is proposed, which is based on an improved particle swarm optimisation (PSO) algorithm. We improve the conventional PSO algorithm by introducing strategies related to local learning and global learning, which contribute to accelerating the convergence rate and effectively avoiding getting trapped in local extrema. Then, the improved PSO algorithm is used to improve the conventional FD scheme. Dispersion analysis and numerical modelling demonstrate that the low-order optimised FD scheme can achieve higher accuracy than a high-order conventional operator. Compared with the conventional FD scheme and a FD scheme based on the Remez exchange algorithm, the optimised FD scheme based on the improved PSO algorithm can more efficiently suppress numerical dispersion and increase computational efficiency. [ABSTRACT FROM AUTHOR]

Frequency offset estimation is an important module in the synchronisation of underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems. Different ways are used for carrier frequency offset (CFO) estimation such as maximum likelihood based on a cyclic prefix. In this paper, the Zadoff-Chu (ZC) sequences are used for OFDM synchronisation in underwater communications. The normalised carrier frequency offset can be estimated using the ZC cross-correlation between the primary synchronisation signal and the corresponding local signal, which causes large complexity. The ZC sequences have a time centre symmetric property, and thus the autocorrelation of the received primary synchronisation signal can be used for frequency offset estimation. In this paper, we propose a modified algorithm with low complexity for frequency offset estimation. Simulation results demonstrate that the proposed algorithm can estimate the frequency offset accurately with simple implementation in comparison with the traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for predicting the distributions of people's trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people's spatial and temporal mobility patterns, the complexity of a city level's road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method. [ABSTRACT FROM AUTHOR]

The distributed modulated wideband converter (DMWC) is a kind of sub-Nyquist sampling system which randomly moves the sparse wideband signal spectrum to the baseband to realise low-speed sampling. DMWC regards each sensor node as a sampling channel and combines compressed sensing technology with a broadband cooperative spectrum sensing network. However, DMWC contains phase and transmission attenuation, which results in joint sparsity destroyed. Therefore, the original signal cannot be reliably recovered. In this paper, we combine measurement matrix with a phase shift to develop a phase shift calibration algorithm (PSCA) based on DMWC, a calibration matrix that is constructed to eliminate the influence of phase shift on recovery rate. Firstly, random waveforms of different channels in the DMWC system correspond to the row vectors of the measurement matrix. We apply the frequency-domain method to construct a calibration matrix. Secondly, we estimate the phase shift in each channel and write it as a complex diagonal matrix. Finally, we consider balancing the phase shift in each channel, the phase shift of each channel multiplied by the corresponding row of the measurement matrix. Theoretical analysis and numerical simulations show that the algorithm improves the recovery rate to 98% or higher in lower SNR. [ABSTRACT FROM AUTHOR]

The paper proposes a new extragradient algorithm for solving strongly pseudomonotone equilibrium problems which satisfy a Lipschitz-type condition recently introduced by Mastroeni in auxiliary problem principle. The main novelty of the paper is that the algorithm generates the strongly convergent sequences in Hilbert spaces without the prior knowledge of Lipschitz-type constants and any hybrid method. Several numerical experiments on a test problem are also presented to illustrate the convergence of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel estimation of coefficients in single-index regression models. Unlike the traditional average derivative estimation [Powell JL, Stock JH, Stoker TM. Semiparametric estimation of index coefficients. Econometrica. 1989;57(6):1403-1430; Hardle W, Thomas M. Investigating smooth multiple regression by the method of average derivatives. J Amer Statist Assoc. 1989;84(408):986-995] and semiparametric least squares estimation [Ichimura H. Semiparametric least squares (sls) and weighted sls estimation of singleindex models. J Econometrics. 1993;58(1):71-120; Hardle W, Hall P, Ichimura H. Optimal smoothing in single-index models. Ann Statist. 1993;21(1):157-178], the procedure developed in this paper is to estimate the coefficients directly by minimizing the mean variation function and does not involve estimating the link function nonparametrically. As a result, it avoids the selection of the bandwidth or the number of knots, and its implementation is more robust and easier. The resultant estimator is shown to be consistent. Numerical results and real data analysis also show that the proposed procedure is more applicable against model free assumptions. [ABSTRACT FROM AUTHOR]

H-matrices play an important role in applied sciences such as numerical analysis and optimization theory. An attractive question is to identify whether a given matrix is an H-matrix. In this paper, we propose a new iterative algorithm for identifying H-matrices. We show that the proposed algorithm has linear convergence and can determine the H-matrix characterization for any given matrix. Its performance is illustrated in a set of numerical tests. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for semantic segmentation of pedestrian trajectories based on pedestrian behavior models, or agents. The agents model the dynamics of pedestrian movements in two-dimensional space using a linear dynamics model and common start and goal locations of trajectories. First, agent models are estimated from the trajectories obtained from image sequences. Our method is built on top of the Mixture model of Dynamic pedestrian Agents (MDA); however, the MDA's trajectory modeling and estimation are improved. Then, the trajectories are divided into semantically meaningful segments. The subsegments of a trajectory are modeled by applying a hidden Markov model using the estimated agent models. Experimental results with a real trajectory dataset show the effectiveness of the proposed method as compared to the well-known classical Ramer-Douglas-Peucker algorithm and also to the original MDA model. [ABSTRACT FROM AUTHOR]

Concave knapsack problems with integer variables have many applications in real life, and they are NP-hard. In this paper, an exact and efficient algorithm is presented for concave knapsack problems. The algorithm combines the contour cut with a special cut to improve the lower bound and reduce the duality gap gradually in the iterative process. The lower bound of the problem is obtained by solving a linear underestimation problem. A special cut is performed by exploiting the structures of the objective function and the feasible region of the primal problem. The optimal solution can be found in a finite number of iterations, and numerical experiments are also reported for two different types of concave objective functions. The computational results show the algorithm is efficient. [ABSTRACT FROM AUTHOR]

In this paper, a new pattern search is proposed to solve the systems of nonlinear equations. We introduce a new non-monotone strategy which includes a convex combination of the maximum function of some preceding successful iterates and the current function. First, we produce a stronger non-monotone strategy in relation to the generated strategy by Gasparo et al. [Nonmonotone algorithms for pattern search methods, Numer. Algorithms 28 (2001), pp. 171-186] whenever iterates are far away from the optimizer. Second, when iterates are near the optimizer, we produce a weaker non-monotone strategy with respect to the generated strategy by Ahookhosh and Amini [An efficient nonmonotone trust-region method for unconstrained optimization, Numer. Algorithms 59 (2012), pp. 523-540]. Third, whenever iterates are neither near the optimizer nor far away from it, we produce a medium non-monotone strategy which will be laid between the generated strategy by Gasparo et al. [Nonmonotone algorithms for pattern search methods, Numer. Algorithms 28 (2001), pp. 171-186] and Ahookhosh and Amini [An efficient nonmonotone trust-region method for unconstrained optimization, Numer. Algorithms 59 (2012), pp. 523-540]. Reported are numerical results of the proposed algorithm for which the global convergence is established. [ABSTRACT FROM AUTHOR]

In this paper, we present an interior point method for nonlinear programming that avoids the use of penalty function or filter. We use an adaptively perturbed primal dual interior point framework to computer trial steps and a central path technique is used to keep the iterate bounded away from 0 and not to deviate too much from the central path. A trust-funnel-like strategy is adopted to drive convergence. We also use second-order correction (SOC) steps to achieve fast local convergence by avoiding Maratos effect. Furthermore, the presented algorithm can avoid the blocking effect. It also does not suffer the blocking of productive steps that other trust-funnel-like algorithm may suffer. We show that, under second-order sufficient conditions and strict complementarity, the full Newton step (combined with an SOC step) will be accepted by the algorithm near the solution, and hence the algorithm is superlinearly local convergent. Numerical experiments results, which are encouraging, are reported. [ABSTRACT FROM AUTHOR]

In this paper we study the stability of the minimal solutions of set optimization problems. We provide sufficient conditions for the upper and lower semicontinuity and compactness of the minimal solutions of parametric set optimization problems whose objective values are not necessarily compact. [ABSTRACT FROM AUTHOR]

In 1981 Dixon introduced a clever idea for factoring large numbers. This idea has become the basis for many current factoring techniques. In this paper, we show how to implement the idea on the computer in the classroom. Additionally, pseudocode is given for finding examples suitable for demonstrating Dixon factorization. [ABSTRACT FROM AUTHOR]

Conventional algorithms for polygon rasterization are typically designed to maintain non-topological characteristics. Consequently, topological relationships, such as the adjacency between polygons, may also be lost or altered, creating topological errors. This paper proposes a topology-preserving polygon rasterization algorithm to avoid topological errors. Four types of topological error may occur during polygon rasterization. The algorithm starts from an initial polygon rasterization and uses a set of preserving strategies to increase topological accuracy. The count of the four types of error measures the topological errors of the conversion. Topological accuracy is summarized as 1 minus the ratio of actual topological errors to the total number of possible error cases. When applied to a land-use dataset with a data volume of 128 MB, 127,836 polygons, and extending 1352 km2, the algorithm achieves a topological accuracy of more than 99% when raster cell size is 30 m or smaller (100% for 5 and 10 m). The effects of cell size, polygon shape, and number of iterations on topological accuracy are also examined. [ABSTRACT FROM AUTHOR]

In this paper, based on a new smoothing function, the general box constrained variational inequalities are solved by a smoothing Newton algorithm with a nonmonotone line search. The proposed algorithm is proved to be globally and locally superlinearly convergent under suitable assumptions. The preliminary numerical results are reported. [ABSTRACT FROM AUTHOR]

In the paper, we consider the bioprocess system optimal control problem. Generally speaking, it is very difficult to solve this problem analytically. To obtain the numerical solution, the problem is transformed into a parameter optimization problem with some variable bounds, which can be efficiently solved using any conventional optimization algorithms, e.g. the improved Broyden-Fletcher-Goldfarb-Shanno algorithm. However, in spite of the improved Broyden-Fletcher-Goldfarb-Shanno algorithm is very efficient for local search, the solution obtained is usually a local extremum for non-convex optimal control problems. In order to escape from the local extremum, we develop a novel stochastic search method. By performing a large amount of numerical experiments, we find that the novel stochastic search method is excellent in exploration, while bad in exploitation. In order to improve the exploitation, we propose a hybrid numerical optimization algorithm to solve the problem based on the novel stochastic search method and the improved Broyden-Fletcher-Goldfarb-Shanno algorithm. Convergence results indicate that any global optimal solution of the approximate problem is also a global optimal solution of the original problem. Finally, two bioprocess system optimal control problems illustrate that the hybrid numerical optimization algorithm proposed by us is low time-consuming and obtains a better cost function value than the existing approaches. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a new hybrid algorithm for pricing financial derivatives in the arithmetic Asian options. In this paper, two variance reduction techniques are combined, the multiple control variates (MCV) and the antithetic variates (AV). We propose an efficient algorithm for pricing arithmetic Asian options based on the AV and the MCV procedures. A detailed numerical study illustrates the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose an arc-search infeasible-interior-point method based on the wide neighbourhoodfor linear complementarity problems over symmetric cones with the Cartesian-property (-SCLCP). The algorithm searches the optimizers along the ellipses that approximate the central path. Moreover, we derive the complexity bound of the algorithm which coincides with the currently best known theoretical complexity bounds for the short step path-following algorithm. Some numerical results are provided to demonstrate the computational performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents a parallel varied density-based clustering algorithm with optimized data partition (PVDB). First, we improve the partition with reduced boundary points algorithm using shared nearest neighbour (SNN) methods and propose the reachable partition with reduced boundary points algorithm. Second, we introduce a layered grouping grid structure and propose an efficient k nearest neighbour (kNN) search method. This method enhances the efficiency of kNN searches and determines whether kNNs are in their own partitions. Third, we propose a new merging strategy for connecting clusters in different partitions, based on the reachable point concept. Meanwhile, the strategy avoids connecting clusters with varying densities by SNN as occurs with SNN-based clustering methods. Our algorithm is implemented and compared with DBSCANMR and GriDBSCAN using the MapReduce paradigm and shows better varied density clustering capability and scalability. In addition, varied applications show our algorithm's capability of discerning spatial patterns and extending to many fields. [ABSTRACT FROM AUTHOR]

The performance of commonly used asymptotic inference procedures for the random-effects model used in meta analysis relies on the number of studies. When the number of studies is moderate or small, the exact inference procedure is more reliable than the asymptotic counterparts. However, the related numerical computation may be demanding and an obstacle of routine use of the exact method. In this paper, we proposed a novel numerical algorithm for constructing the exact 95% confidence interval of the location parameter in the random-effects model. The algorithm is much faster than the naive method and may greatly facilitate the use of the more appropriate exact inference procedure in meta analysis. Numerical studies and real data examples are used to illustrate the advantage of the proposed method. [ABSTRACT FROM AUTHOR]

Sharma (Appl. Math. Comput. 259:741–752) introduced the mixed summation integral-type two-dimensionalq–Lupaş–Phillips–Bernstein operators, wherein he established the rate of approximation by applying Korovkin theorem and studied the weighted approximation properties. The goal of this paper is to establish a Voronovskaja-type theorem and introduce the associated generalized Boolean Sum (GBS) caseof these operators and study the degree of approximation by the Lipschitz class of Bögel continuous functions and the mixed modulus of smoothness. Furthermore, we show the rate of convergence of the bivariate operatorsand the corresponding GBS operatorsby illustrative graphics and numerical examples using Maple algorithms. [ABSTRACT FROM PUBLISHER]

This paper introduces an alternating direction method of multipliers (ADMM) for finding solutions to a class of Sylvester matrix equationAXB = Esubject to a linear matrix inequality constraintCXD≥G. Preliminary convergence properties of ADMM are presented. Numerical experiments are performed to illustrate the feasibility and effectiveness of ADMM. In addition, some numerical comparisons with a recent algorithm are also given. [ABSTRACT FROM PUBLISHER]

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data. [ABSTRACT FROM PUBLISHER]

This paper inductively investigates an inverse problem for difference boundary value problems with boundary conditions that depend quadratically on the eigenparameter. In particular, given the eigenvalues and the weights, we provide an algorithm to uniquely reconstruct the potential. [ABSTRACT FROM AUTHOR]

In this paper, we propose an iteratively reweightedminimization algorithm (IRL1 algorithm) for solving the elasticminimization problem. We prove that any sequence generated by the IRL1 algorithm is bounded and asymptotically regular. We also prove that the sequence is convergent for any rationaland the limit is a stationary point of the elasticminimization problem. Moreover, under certain conditions, we present an error bound between the limit point of convergent sequence and the sparse solution of underdetermined linear system. Numerical experiments on sparse vector recovery are presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper,we propose a new predictor–corrector interior-point method for symmetric cone programming. This algorithm is based on a wide neighbourhood and the Nesterov–Todd direction. We prove that, besides the predictor steps, each corrector step also reduces the duality gap by a rate of, whereris the rank of the associated Euclidean Jordan algebras. In particular, the complexity bound is, whereis a given tolerance. To our knowledge, this is the best complexity result obtained so far for interior-point methods with a wide neighbourhood over symmetric cones. The numerical results show that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

In this paper, we consider the perturbation analysis for ⋆-Sylvester equations. Based on the small-sample condition estimation (SCE) technique, we devise algorithms to estimate normwise, mixed and componentwise condition numbers for ⋆-Sylvester equations. We also define a componentwise backward error with a sharp and easily computable bound. Numerical examples illustrate that our algorithm for componentwise perturbations produces reliable estimates, and the new derived computable bound for the componentwise backward error is sharp and reliable for well-conditioned and moderate ill-conditioned ⋆-Sylvester equations under large or small perturbations. [ABSTRACT FROM AUTHOR]

In this paper, we propose two proximal-gradient algorithms for fractional programming problems in real Hilbert spaces, where the numerator is a proper, convex and lower semicontinuous function and the denominator is a smooth function, either concave or convex. In the iterative schemes, we perform a proximal step with respect to the nonsmooth numerator and a gradient step with respect to the smooth denominator. The algorithm in case of a concave denominator has the particularity that it generates sequences which approach both the (global) optimal solutions set and the optimal objective value of the underlying fractional programming problem. In case of a convex denominator the numerical scheme approaches the set of critical points of the objective function, provided the latter satisfies the Kurdyka-ᴌojasiewicz property. [ABSTRACT FROM AUTHOR]

This paper presents a time domain technique for estimating dynamic loads acting on a structure from acceleration time response measured experimentally at a finite number of optimally placed accelerometers on the structure. The technique utilizes model reduction to obtain precise load estimates. The structure essentially acts as its own load transducer. The approach is based on the standard equilibrium equations of motion in modal coordinates. The modal parameters of a system – natural frequencies, mode shapes and damping factors – can be estimated experimentally from measured data, analytically for simple problems, or using the finite element method. For measurement of the acceleration response, there can be a large number of locations on the structure where the accelerometers can be mounted, and the precision with which the applied loads are estimated from measured acceleration response may be strongly influenced by the locations selected for accelerometer placements. A solution approach, based on the construction of D-optimal designs, is presented to determine the number and optimum locations of accelerometers that will provide the most precise load estimates. An improvement in the algorithm, based on reduced modal matrix, is further proposed to reconstruct the input forces accurately. Numerical examples that help understand the main characteristics of the proposed approach are also presented. The numerical results illustrate the effectiveness of the proposed technique in accurately recovering the loads imposed on discrete as well as continuous systems. [ABSTRACT FROM AUTHOR]

This paper describes iterative methods for solving the general linear matrix equation including the well-known Lyapunov matrix equation, Sylvester matrix equation and some related matrix equations encountered in control system theory, as special cases. We develop the methods from the optimization point of view in the sense that the iterative algorithms are constructed to solve some optimization problems whose solutions are closely related to the unique solution to the linear matrix equation. Actually, two optimization problems are considered and, therefore, two iterative algorithms are proposed to solve the linear matrix equation. To solve the two optimization problems, the steepest descent method is adopted. By means of the so-called weighted inner product that is defined and studied in this paper, the convergence properties of the algorithms are analysed. It is shown that the algorithms converge at least linearly for arbitrary initial conditions. The proposed approaches are expected to be numerically reliable as only matrix manipulation is required. Numerical examples show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In the paper [S.P. Rui and C.X. Xu, A smoothing inexact Newton method for nonlinear complementarity problems, J. Comput. Appl. Math. 233 (2010), pp. 2332–2338], the authors proposed an inexact smoothing Newton method for nonlinear complementarity problems (NCP) with the assumption that F is a uniform P function. In this paper, we present a non-monotone inexact regularized smoothing Newton method for solving the NCP which is based on Fischer–Burmeister smoothing function. We show that the proposed algorithm is globally convergent and has a locally superlinear convergence rate under the weaker condition that F is a P 0 function and the solution of NCP is non-empty and bounded. Numerical results are also reported for the test problems, which show the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, a universal simulator for cellular neural network (CNN) is presented. This simulator is capable of performing Raster simulation for any size of input image, and thus is a powerful tool for researchers investigating potential applications of CNN. This paper reports the latency properties of CNNs along with popular numerical integration algorithms; results and comparisons are also presented. [ABSTRACT FROM AUTHOR]

In this paper we discuss the convergence behaviour of the nonlinear Uzawa algorithm for solving saddle point problems presented in a recent paper of Cao [Z.H. Cao, Fast Uzawa algorithm for generalized saddle point problems, Appl. Numer. Math. 46 (2003), pp. 157-171]. For a general case, the results on the convergence of the algorithm are given. [ABSTRACT FROM AUTHOR]

This paper deals with the application of the global optimization methods (evolutionary algorithms) to the optimization and identification problems in laminates. The simple and hybrid laminates are considered. There are two aims of the paper: i) to find the optimum stacking sequence of the laminates and ii) to identify the material constants of the laminates. The modal analysis methods are used in both cases. The distributed version of the evolutionary algorithm is used to reduce the computation time. The finite element method software is employed to solve the direct boundary-value problem. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the augmented Lagrangian method for a class of nonsmooth convex optimization. We present the Lagrange optimality system of the augmented Lagrangian associated with the problems, and establish its connections with the standard optimality condition and the saddle point condition of the augmented Lagrangian, which provides a powerful tool for developing numerical algorithms: we derive a Lagrange–Newton algorithm for the nonsmooth convex optimization, and establish the nonsingularity of the Newton system and the local convergence of the algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we present a dynamic programming algorithm for pricing variable annuities with Guaranteed Minimum Withdrawal Benefits (GMWB) under a general Lévy processes framework. The GMWB gives the policyholder the right to make periodical withdrawals from her policy account even when the value of this account is exhausted. Typically, the total amount guaranteed for withdrawals coincides with her initial investment, providing then a protection against downside market risk. At each withdrawal date, the policyholder has to decide whether, and how much, to withdraw, or to surrender the contract. We show how different policyholder’s withdrawal behaviours can be modelled. We perform a sensitivity analysis comparing the numerical results obtained for different contractual and market parameters, policyholder behaviours and different types of Lévy processes. [ABSTRACT FROM AUTHOR]

In this paper, a finite filter is used in the structure of the Barzilai–Browein (BB) gradient method in order to propose a new modified BB algorithm for solving large-scale unconstrained optimization problems. Our algorithm is equipped with a relaxed nonmonotone line search technique which allows the algorithm to enjoy the nonmonotonicity properties from scratch. Under some suitable conditions, the global convergence property of the new proposed algorithm is established. Numerical results on some test problems in CUTEr library show the efficiency and effectiveness of the new algorithm in practice too. [ABSTRACT FROM PUBLISHER]

In this paper we study numerical stability of path-based algorithms for the traffic assignment problem. These algorithms are based on decomposition of the original problem into smaller sub-problems which are optimized sequentially. Previously, path-based algorithms were numerically tested only in the setting of moderate requirements to the level of solution precision. In this study we analyse convergence of these methods when the convergence measure approaches machine epsilon of IEEE double precision format. In particular, we demonstrate that the straightforward implementation of one of the algorithms of this group (projected gradient) suffers from loss of precision and is not able to converge to highly precise solution. We propose a way to solve this problem and test the proposed adjusted version of the algorithm on various benchmark instances. [ABSTRACT FROM AUTHOR]

The eigenvalue complementarity problem (EiCP) differs from the traditional eigenvalue problem in that the primal and dual variables belong to a closed and convex coneKand its dual, respectively, and satisfy a complementarity condition. In this paper we investigate the solution of the second-order cone EiCP (SOCEiCP) whereKis the Lorentz cone. We first show that the SOCEiCP reduces to a special Variational Inequality Problem on a compact set defined byKand a normalization constraint. This guarantees that SOCEiCP has at least one solution, and a new enumerative algorithm is introduced for finding a solution to this problem. The method is based on finding a global minimum of an appropriate nonlinear programming (NLP) formulation of the SOCEiCP using a special branching scheme along with a local nonlinear optimizer that computes stationary points on subsets of the feasible region of NLP associated with the nodes generated by the algorithm. A semi-smooth Newton's method is combined with this enumerative algorithm to enhance its numerical performance. Our computational experience illustrates the efficacy of the proposed techniques in practice. [ABSTRACT FROM AUTHOR]

In this paper, existence of critical point and weak efficient point of vector optimization problem is studied. A sequence of points in n-dimension is generated using positive definite matrices like Quasi-Newton method. It is proved that accumulation points of this sequence are critical points or weak efficient points under different conditions. An algorithm is provided in this context. This method is free from any kind of priori chosen weighting factors or any other form of a priori ranking or ordering information for objective functions. Also, this method does not depend upon initial point. The algorithm is verified in numerical examples. [ABSTRACT FROM AUTHOR]

The Bregman function-based Proximal Point Algorithm (BPPA) is an efficient tool for solving equilibrium problems and fixed-point problems. Extending rather classical proximal regularization methods, the main additional feature consists in an application of zone coercive regularizations. The latter allows to treat the generated subproblems as unconstrained ones, albeit with a certain precaution in numerical experiments. However, compared to the (classical) Proximal Point Algorithm for equilibrium problems, convergence results require additional assumptions which may be seen as the price to pay for unconstrained subproblems. Unfortunately, they are quite demanding – for instance, as they imply a sort of unique solvability of the given problem. The main purpose of this paper is to develop a modification of the BPPA, involving an additional extragradient step with adaptive (and explicitly given) stepsize. We prove that this extragradient step allows to leave out any of the additional assumptions mentioned above. Hence, though still of interior proximal type, the suggested method is applicable to an essentially larger class of equilibrium problems, especially including non-uniquely solvable ones. [ABSTRACT FROM AUTHOR]

In this paper, we propose a generalized Laguerre spectral method for Fisher's-type equation with inhomogeneous boundary conditions on a semi-infinite interval. By reformulating the equation with suitable functional transform, it is shown that the generalized Laguerre approximations are convergent on a semi-infinite interval with spectral accuracy. An efficient and accurate algorithm based on the generalized Laguerre approximations to the transformed equation is developed and implemented. Numerical results show the efficiency of this approach and coincide well with theoretical analysis. [ABSTRACT FROM PUBLISHER]

A rotation algorithm was developed that computes the slope of permanently deformed columns subjected to earthquake ground motion using micro-electro-mechanical system accelerometers. Previous studies by the authors have shown that the algorithm can provide estimates of the residual displacement in the column, and thus the story drift can be evaluated. The algorithm was validated through applications to columns that deflect in single and double curvature. Information from tests on such columns conducted at various laboratories was used to conduct the study. The optimal number of sensors and their placement on the test columns were determined through a series of numerical analyses. The accuracy of the rotation and displacement estimates is assessed under the assumptions of zero-noise sensors and perfect measurements. In this paper, the effect of measurement error and sensor noise on the predicted rotations and corresponding deformations is studied in order to provide bounds on the accuracy of the rotation algorithm and on the allowable sensor noise. The same test columns are used for the purposes of these analyses. [ABSTRACT FROM PUBLISHER]

The paper presents the iterative solutions of two coefficient inverse problems (CIPs) arising in frequency sounding and electrical prospecting. An iterative algorithm is constructed to obtain such solutions. Exploiting the Beilina–Klibanov approach to CIPs, this algorithm possesses the new iterative and refinement procedures. These features enhance significantly both the spatial and contrast resolutions of reconstructed coefficients. The computational effectiveness of the proposed numerical technique is demonstrated in computational experiments with two applied CIPs: electromagnetic or acoustic frequency sounding and electrical prospecting of layered media. The Slichter–Langer–Tikhonov formulation is exploited as a mathematical model of the latter. [ABSTRACT FROM PUBLISHER]

An instance of the operational fixed job scheduling problem arises when open work caused by unplanned events such as bus breakdowns, inclement weather, and driver (operator) absenteeism need to be covered by reserve (extraboard) drivers. Each work-piece, which is referred to as a job, requires one operator who must work continuously between specified start and end times to complete the job. Each extraboard operator may be assigned up towhours of work, which may not to be continuous so long as the total work time is within as-hour time window of that operator’s shift start time. Parameterswandsare called allowable work-time and spread-time, respectively. The objective is to choose operators’ shift start times and work assignments, while honoring work-time and spread-time constraints, such that the amount of work covered as part of regular duties is maximized. This paper argues that the extraboard operator scheduling problem is NP-hard and three heuristic approaches are presented for the solution of such problems. These include a decomposition-based algorithm whose worst-case performance ratio is proved to lie in [1 − 1/e, 19/27], wheree≈ 2:718 is the base of the natural logarithm. Numerical experiments are presented that use data from a large transit agency, which show that the average performance of the decomposition algorithm is good when applied to real-world data. [ABSTRACT FROM PUBLISHER]

In this paper, a numerical algorithm is proposed based on the method of fundamental solutions (MFS) for the Cauchy problem connected with the Laplace equation in ℝ2. The major advantage of the modified MFS is to keep a very basic natural property, i.e. the invariance under trivial coordinate changes in the problem description. The method combines Newton's method and classic Tikhonov regularization to solve an inverse problem. The numerical convergence, accuracy, and stability of the method with respect to increasing the number of source points, and decreasing the amount of noise added into the input data, respectively, are also analysed with some examples, even for high noise levels. [ABSTRACT FROM PUBLISHER]