Search

This paper considers the iterative parameter estimation for a dual‐rate sampled‐data bilinear system with autoregressive moving average noise. Through combining the auxiliary model identification idea with the data filtering technique, this paper derives two filtering auxiliary model gradient‐based iterative algorithms by using two different filters. The key is to construct an auxiliary model for predicting the unavailable outputs, and to transform the dual‐rate bilinear system identification model into two sub‐identification models. Finally, an auxiliary model gradient‐based iterative (AM‐GI) algorithm is presented for comparison. The simulation results indicate that the proposed algorithms are effective for identifying the dual‐rate sampled‐data bilinear systems, and can generate more accurate parameter estimates and have a higher computational efficiency than the AM‐GI algorithm. [ABSTRACT FROM AUTHOR]

Traditional content-based recommendation algorithm has lower accuracy, while data sparseness and cold start problems are common in collaborative filtering recommendation algorithms. To solve this problem, this paper proposed a hybrid recommendation algorithm based on content and collaborative matrix factorization technique. The algorithm realized the decomp osition of content and collaborative matrix in a common low-dimensional space while preserving the local data structure. This paper used an iterative method based on multiplication update rules in parameter optimization, improved learning ability. The experimental results show that the proposed algorithm is superior to other representative projects cold start recommendation algorithm, which effectively alleviates the data sparseness and improves the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a new viscosity iterative method minimization algorithm for image restoration algorithm is proposed. Based on a new viscosity iteration, the algorithm for finding the common zeros of two accretive operators in the framework of uniformly smooth Banach spaces. Moreover, the strong convergence theorems for the iterative algorithms and an example is proposed which shows the validity of main theorem are proved. The results of this paper are improved and extended of the corresponding ones announced by many others and we also applied our result to solve a convex minimization problem and Gâteaux differentiable E P s and Variational Inequality. Experiment results show that the proposed algorithms outperform some other methods. Finally, we give the numerical experiments to show the efficiency and advantage of the proposed methods and we also used our proposed algorithm for applying to solve the image deblurring and image recovery problems. [ABSTRACT FROM AUTHOR]

Abstract A variationally consistent approximation of contact conditions by means of biorthogonal mortars was introduced by Wohlmuth as a powerful theoretically supported tool for the discretization of contact problems. This approach is especially useful when a potential contact interface is large and curved or when nonmatching grids are applied, but its effective implementation into FETI based algorithms is not straightforward due to the ill conditioning of related inequality constraints. In this paper we review the mortar discretization and theoretical results on scalability of the FETI based algorithm and show that the recently proposed adaptive augmentation can overcome the difficulties caused by the ill-conditioning of constraints. We demonstrate the (weak) numerical scalability by numerical experiments and present the results for a difficult real world problem discretized by mortars that show the power of the new algorithm – the number of iterations required to the solution of this problem discretized by mortars is just one third of that required by the original algorithm for the same problem discretized by node-to-node constraints. Highlights • The paper reviews the bounds on the conditioning of matrices arising from mortar discretization of non-penetration conditions. • The theoretical results support the scalability of FETI methods for the solution of multibody frictionless contact problems are given. • The (weak) scalability is demonstrated by the solution of a variant of the Hertz problem. • Moreover, the solution of a difficult real world problem shows that the new algorithm autperforms its predecessors. [ABSTRACT FROM AUTHOR]

In this paper, we continue to study a non-local free boundary problem arising in financial bubbles. We focus on the parabolic counterpart of the bubble problem and suggest an iterative algorithm which consists of a sequence of parabolic obstacle problems at each step to be solved, that in turn gives the next obstacle function in the iteration. The convergence of the proposed algorithm is proved. Moreover, we consider the finite difference scheme for this algorithm and obtain its convergence. At the end of the paper, we present and discuss computational results. [ABSTRACT FROM AUTHOR]

The root-fnding problem of a univariate nonlinear equation is a fundamental and long-studied problem, and has wide applications in mathematics and engineering computation. In this paper, two modified Newton-type methods and two variants of Super-Halley's method to solve nonlinear equations are brought forward. Analytical discussions are reported and the theoretical efficiency of the methods is studied. Both of Algorithms 1 and 2 require three evaluations of the functions and two evaluations of derivatives at each iteration. Therefore the efficiency indices of Algorithms 1 and 2 are 1.431 and 1.476, respectively. The presented variants of Super-Halley's methods require two evaluations of the functions and two evaluations of derivatives at each iteration. Therefore the efficiency indices of Algorithms 3 and 4 are 1.565. Hence, all efficiency indices of the proposed algorithms are better than that of classical Newton's method 1.414. All of the proposed algorithms in this paper are free from second derivatives. Some numerical results are finally provided to support the theoretical discussions of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper revisits the problem of finding the best rank-1 approximation to a symmetric tensor and makes three contributions. First, in contrast to the many long and lingering arguments in the literature, it offers a straightforward justification that generically the best rank-1 approximation to a symmetric tensor is symmetric. Second, in contrast to the typical workhorse in the practice for the low-rank tensor approximation, namely, the alternating least squares (ALS) technique which improves one factor a time, this paper proposes three alternative algorithms based on the singular value decomposition (SVD) that modifies two factors a time. One step of SVD-based iteration is superior to two steps of ALS iterations. Third, it is not only proved that the generalized Rayleigh quotients generated from the three SVD-based algorithms enjoy monotone convergence, but also that the iterates themselves converge. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel classification model for the multiple instance data, which aims to maximize the number of positive instances ranked before the top-ranked negative instances. This method belongs to a recently emerged performance, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose an algorithm to learn the convolutional filters and the full connection weights to maximize the Pos@Top measure over the training set. Also, we try to minimize the rank of the filter matrix to explore the low-dimensional space of the instances in conjunction with the classification results. The rank minimization is conducted by the nuclear norm minimization of the filter matrix. In addition, we develop an iterative algorithm to solve the corresponding problem. We test our method on several benchmark datasets. The experimental results show the superiority of our method compared with other state-of-the-art Pos@Top maximization methods. [ABSTRACT FROM AUTHOR]

The Cauchy problem for the Helmholtz equation is considered. It was demonstrated in a previous paper by the authors that the alternating algorithm suggested by V.A. Kozlov and V.G. Maz’ya does not converge for large wavenumbers k in the Helmholtz equation. Here, we present some simple modifications of the algorithm which may restore the convergence. They consist of the replacement of the Neumann-Dirichlet iterations by the Robin-Dirichlet ones which repairs the convergence for less than the first Dirichlet-Laplacian eigenvalue. In order to treat large wavenumbers, we present an algorithm based on iterative solution of Robin-Dirichlet boundary value problems in a sufficiently narrow border strip. Numerical implementations obtained using the finite difference method are presented. The numerical results illustrate that the algorithms suggested in this paper, produce convergent iterative sequences. [ABSTRACT FROM AUTHOR]

The number of failed components in a failed or operating system is a very useful quantity in terms of replacement and maintenance strategies. These quantities have been studied in several papers for a system consisting of identical components. In this paper, the number of failed components at the time when the system fails and the number of failed components when the system is working are considered for a well-known and widely applicable k -out-of- n structure. The system is assumed to have multiple types of components. That is, the system consists of components having nonidentical failure time distributions. Optimization problems are also formulated to find optimal values of the number of components of each type, and the optimal replacement time. [ABSTRACT FROM AUTHOR]

Highlights • This paper presents a novel holistic calibration method for stereo deflectometry system to improve the system measurement accuracy. • The initial value of the system parameters are calculated from the fringe patterns displayed on the systemic LCD screen through a reflection of a markless flat mirror. • An iterative algorithm is proposed to compensate system distortion and optimize camera imaging parameters and system geometrical relation parameters based on a cost function. • The PV (peak value) of measurement error of a flat mirror can be reduced to 69.7 nm by applying the proposed method from 282 nm obtained with the conventional calibration approach. Abstract This paper presents a novel holistic calibration method for stereo deflectometry system to improve the system measurement accuracy. The reconstruction result of stereo deflectometry is integrated with the calculated normal data of the measured surface. The calculation accuracy of the normal data is seriously influenced by the calibration accuracy of the geometrical relationship of the stereo deflectometry system. Conventional calibration approaches introduce form error to the system due to inaccurate imaging model and distortion elimination. The proposed calibration method compensates system distortion based on an iterative algorithm instead of the conventional distortion mathematical model. The initial value of the system parameters are calculated from the fringe patterns displayed on the systemic LCD screen through a reflection of a markless flat mirror. An iterative algorithm is proposed to compensate system distortion and optimize camera imaging parameters and system geometrical relation parameters based on a cost function. Both simulation work and experimental results show the proposed calibration method can significantly improve the calibration and measurement accuracy of a stereo deflectometry. The PV (peak value) of measurement error of a flat mirror can be reduced to 69.7 nm by applying the proposed method from 282 nm obtained with the conventional calibration approach. [ABSTRACT FROM AUTHOR]

Summary: This paper studies a low‐communication algorithm for solving elliptic PDEs on high‐performance machines, the nested iteration with range decomposition (NIRD) algorithm. Previous work has shown that NIRD converges to a high level of accuracy within a small, fixed number of iterations (usually one or two) when applied to simple elliptic problems. This paper makes some improvements to the NIRD algorithm (including the addition of adaptivity during preprocessing, wider choice of partitioning functions, and modified error measurement) that enhance the method's accuracy and scalability, especially on more difficult problems. In addition, an updated convergence proof is presented based on heuristic assumptions that are supported by numerical evidence. Furthermore, a new performance model is developed that shows increased performance benefits for NIRD when problems are more expensive to solve using traditional methods. Finally, extensive testing on a variety of elliptic problems provides additional insight into the behavior of NIRD and additional evidence that NIRD achieves excellent convergence on a wide class of elliptic PDEs and, as such, should be a very competitive method for solving PDEs on large parallel computers. [ABSTRACT FROM AUTHOR]

The paper introduces and analyzes the convergence of a new iterative algorithm for approximating solutions of equilibrium problems involving strongly pseudomonotone and Lipschitz-type bifunctions in Hilbert spaces. The algorithm uses a stepsize sequence which is non-increasing, diminishing, and non-summable. This leads to the main advantage of the algorithm, namely that the construction of solution approximations and the proof of its convergence are done without the prior knowledge of the modulus of strong pseudomonotonicity and Lipschitz-type constants of bifunctions. The strongly convergent theorem is established under suitable assumptions. The paper also discusses the assumptions used in the formulation of the convergent theorem. Several numerical results are reported to illustrate the behavior of the algorithm with different sequences of stepsizes and also to compare it with others. [ABSTRACT FROM AUTHOR]

We consider mixed parallel and cyclic iterative algorithms in this paper to solve the multiple-set split equality common fixed-point problem which is a generalization of the split equality problem and the split feasibility problem for the demicontractive mappings without prior knowledge of operator norms in real Hilbert spaces. Some weak and strong convergence results are established. The results obtained in this paper generalize and improve the recent ones announced by many others. [ABSTRACT FROM AUTHOR]

This paper is a continuation of our previous work [Front. Math. China, 2016, 11(6): 1379-1418] where an efficient algorithm for computing the maximal eigenpair was introduced first for tridiagonal matrices and then extended to the irreducible matrices with nonnegative off-diagonal elements. This paper introduces mainly two global algorithms for computing the maximal eigenpair in a rather general setup, including even a class of real (with some negative off-diagonal elements) or complex matrices. [ABSTRACT FROM AUTHOR]

Advanced driver assistance systems (ADAS) have been developed to automate and modify vehicles for safety and better driving experience. Among all computer vision modules in ADAS, 360-degree surround view generation of immediate surroundings of the vehicle is very important, due to application in on-road traffic assistance, parking assistance etc. This paper presents a novel algorithm for fast and computationally efficient transformation of input fisheye images into required top down view. This paper also presents a generalized framework for generating top down view of images captured by cameras with fish-eye lenses mounted on vehicles, irrespective of pitch or tilt angle. The proposed approach comprises of two major steps, viz. correcting the fish-eye lens images to rectilinear images, and generating top-view perspective of the corrected images. The images captured by the fish-eye lens possess barrel distortion, for which a nonlinear and non-iterative method is used. Thereafter, homography is used to obtain top-down view of corrected images. This paper also targets to develop surroundings of the vehicle for wider distortion less field of view and camera perspective independent top down view, with minimum computation cost which is essential due to limited computation power on vehicles. [ABSTRACT FROM AUTHOR]

Finding the solutions of nonlinear operator equations has been a subject of research for decades but has recently attracted much attention. This paper studies the convergence of a newly introduced viscosity implicit iterative algorithm to a fixed point of a nonexpansive mapping in Banach spaces. Our technique is indispensable in terms of explicitly clarifying the associated concepts and analysis. The scheme is effective for obtaining the solutions of various nonlinear operator equations as it involves the generalized contraction. The results are applied to obtain a fixed point of λ -strictly pseudocontractive mappings, solution of α -inverse-strongly monotone mappings, and solution of integral equations of Fredholm type. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new iterative method in a real Hilbert space for approximating a point in the solution set of a pseudomonotone equilibrium problem which is a common fixed point of a finite family of demicontractive mappings. Our result does not require that we impose the condition that the sum of the control sequences used in the finite convex combination is equal to 1. Furthermore, we state and prove a strong convergence result and give some numerical experiments to demonstrate the efficiency and applicability of our iterative method. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of finding a solution to fixed point problem involving demicontractive mappings in the framework of Hilbert spaces. Inspired by general iterative algorithm, a new iterative method for solving the problem is introduced. Strong convergence theorem of the proposed method is established without any compactness assumption. Our theorems are significant improvements on several important recent results. [ABSTRACT FROM AUTHOR]

In this paper, we study a special instance of the split inverse problem (SIP), which is the split variational inclusion problem (SVIP). Three simple iterative methods for solving it are introduced and weak and strong convergence theorems are established under mild and standard assumptions. As an application, the problem of minimizing two proper, convex, and lower semi-continuous functions is considered. We compare and illustrate the efficiency and applicability of our schemes for several numerical experiments as well as an example in the field of compressed sensing. [ABSTRACT FROM AUTHOR]

In this paper, we suggest two new iterative methods for finding an element of the solution set of split variational inclusion problem in real Hilbert spaces. Under suitable conditions, we present weak and strong convergence theorems for these methods. We also apply the proposed algorithms to study the split feasibility problem. Finally, we give some numerical results which show that our proposed algorithms are efficient and implementable from the numerical point of view. [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 new framework for solving state estimation problems with an additional sparsity-promoting $L_1$ -regularizer term. We first formulate such problems as minimization of the sum of linear or nonlinear quadratic error terms and an extra regularizer, and then present novel algorithms which solve the linear and nonlinear cases. The methods are based on a combination of the iterated extended Kalman smoother and variable splitting techniques such as alternating direction method of multipliers (ADMM). We present a general algorithmic framework for variable splitting methods, where the iterative steps involving minimization of the nonlinear quadratic terms can be computed efficiently by iterated smoothing. Due to the use of state estimation algorithms, the proposed framework has a low per-iteration time complexity, which makes it suitable for solving a large-scale or high-dimensional state estimation problem. We also provide convergence results for the proposed algorithms. The experiments show the promising performance and speed-ups provided by the methods. [ABSTRACT FROM AUTHOR]

This paper focuses on developing new navigation algorithms for cooperative unmanned vehicles in costmap environments. The vehicles need to plan optimal paths subject to mutual diversity. We first propose three algorithms that can find optimal paths in costmap environments with improved computational efficiency. The proposed algorithms include 1) the scaled A*; 2) the A* plus-plus; and 3) the A* plus-plus with reconstruction. The heuristics of the three algorithms are proved admissible. Then, a diversity-based path planning algorithm is proposed based on the new A star variant algorithms for multiple vehicles to plan diverse paths for risk management. In particular, we propose an iterative segment planner, which generates a set of spatially diverse paths by iteratively planning segments of all paths in a sequential manner. To prevent the generation of spatially close paths, the iterative segment planner utilizes a penalty function to increase the cost of paths that lie near the route(s) assigned to other vehicles. By varying the penalty gains, different degrees of path diversity can be achieved. Some illustrative examples are provided to compare the performance of the proposed algorithms with the standard A* and Dijkstra's algorithms, and show the effectiveness of the proposed diversity-based path planning algorithm. [ABSTRACT FROM AUTHOR]

Two iterative procedures have been proposed recently to calculate the chemical potentials corresponding to prescribed concentrations from grand canonical Monte Carlo (GCMC) simulations. Both are based on repeated GCMC simulations with updated excess chemical potentials until the desired concentrations are established. In this paper, we propose combining our robust and fast converging iteration algorithm [Malasics, Gillespie, and Boda, J. Chem. Phys. 128, 124102 (2008)] with the suggestion of Lamperski [Mol. Simul. 33, 1193 (2007)] to average the chemical potentials in the iterations (instead of just using the chemical potentials obtained in the last iteration). We apply the unified method for various electrolyte solutions and show that our algorithm is more efficient if we use the averaging procedure. We discuss the convergence problems arising from violation of charge neutrality when inserting/deleting individual ions instead of neutral groups of ions (salts). We suggest a correction term to the iteration procedure that makes the algorithm efficient to determine the chemical potentials of individual ions too. [ABSTRACT FROM AUTHOR]

In this paper, we study the set of trajectories satisfying both a given LTI system’s laws and also laws of the corresponding ‘adjoint’ system: in other words, trajectories in the intersection of the system’s behavior and that of the adjoint system. This intersection has important system theoretic significance: for example, it is known that the trajectories in this intersection are the ones with minimal ‘dissipation’. Underlying the notion of adjoint is that of a power supply: it is with respect to this supply rate that the trajectories in the intersection are known to be ‘stationary’. In this paper, we deal with half-line solutions to the differential equations governing both the system and its adjoint. Analysis of half-line solutions plays a central role for example in initial value problems and in well-posedness studies of an interconnection. We interpret the set of half-line trajectories allowed by a system and its adjoint as an interconnection of these two systems, and thus address issues about well-posedness/ill-posedness of the interconnection. We formulate necessary and sufficient conditions for this intersection to be autonomous. For the case of an ill-posed interconnection and resulting autonomous system, we derive conditions for existence of initial conditions that lead to impulsive solutions in the states of the system. We link our conditions with the strongly reachable and weakly unobservable subspaces of a state space system. We show that absence of impulsive initial conditions is equivalent to the well-known subspace iteration algorithms for these subspaces converging in one step. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce a new mapping for a finite family of accretive operators and introduce an iterative algorithm for finding a common zero of a finite family of accretive operators in a real reflexive strictly convex Banach space which has a uniformly Gâteaux differentiable norm and admits the duality mapping jφ, where φ is a gauge function invariant on [0, ∞). Furthermore, we prove the strong convergence under some certain conditions. The results obtained in this paper improve and extend the corresponding ones announced by many others. [ABSTRACT FROM AUTHOR]

Stochastic dominance, a pairwise comparison between random variables, is an effective tool for expressing risk aversion in stochastic optimization. In this paper, we develop a family of primal-dual algorithms for optimization problems with stochastic dominance-constraints. First, we develop an offline primal-dual algorithm and bound its optimality gap as a function of the number of iterations. Then, we extend this algorithm to the online setting where only one random sample is given in each decision epoch. We give probabilistic bounds o n the optimality gap in this setting. This technique also yields an online algorithm for the stochastic dominance-constrained multiarmed bandit with partial feedback. The paper concludes by discussing a dual approach for a batch learning problem with robust stochastic dominance constraints. [ABSTRACT FROM AUTHOR]

Our contribution in this paper is to propose an iterative algorithm which does not require prior knowledge of operator norm and prove strong convergence theorem for approximating a solution of split common fixed point problem of demicontractive mappings in a real Hilbert space. So many authors have used algorithms involving the operator norm for solving split common fixed point problem, but as widely known the computation of these algorithms may be difficult and for this reason, authors have recently started constructing iterative algorithms with a way of selecting the step-sizes such that the implementation of the algorithm does not require the calculation or estimation of the operator norm. We introduce a new algorithm for solving the split common fixed point problem for demicontractive mappings with a way of selecting the step-sizes such that the implementation of the algorithm does not require the calculation or estimation of the operator norm and then prove strong convergence of the sequence in real Hilbert spaces. Finally, we give some applications of our result and numerical example at the end of the paper. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study some iterative algorithms for finding a common element of the set of solutions of systems of variational inequalities for inverse-strongly accretive mappings and the set of fixed points of an asymptotically nonexpansive mapping in uniformly convex and 2-uniformly smooth Banach space or uniformly convex and q-uniformly smooth Banach space. Strong convergence theorems are obtained under suitable conditions. We also give some numerical examples to support our main results. The results obtained in this paper improve and extend the recent ones announced by many others in the literature. [ABSTRACT FROM AUTHOR]

The traditional iterative closest point (ICP) algorithm could register two point sets well, but it is easily affected by local dissimilarity. To deal with this problem, this paper proposes an isotropic scaling ICP algorithm with corner point constraint. First of all, because the corner points can preserve the similarity of the whole shapes, an objective function based on least square error is proposed under the guidance of the corner points. Second, a new ICP algorithm is proposed to complete the isotropic scaling registration. At each iterative step of this new algorithm, the correspondence is built based on the closest point searching, and then a closed-form solution of the transformation is computed. This new algorithm converges monotonically to a local minimum from any given initial scaling transformation. To obtain the expected minimum, the traditional scaling ICP algorithm is applied to compute the initial transformation. The experimental results demonstrate that our algorithm can prevent the influence of the local dissimilarity and improve the registration precision compared with the traditional ICP algorithm. [ABSTRACT FROM AUTHOR]

A newly designed grounded electrical-source airborne transient electromagnetics (GREATEM) system was introduced recently. Detailed data preprocessing techniques to acquire the high-precision measured magnetic field are discussed here. Different from the previous work in which the reconstruction of the underground structure is performed in 1-D, we interpret the GREATEM data in 3-D by the volume integral equation (VIE) method in the frequency domain. Therefore, the VIE in the forward electromagnetic scattering model is formulated in the low-frequency regime. It is solved by using the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method. In the nonlinear inversion, the Born iterative method (BIM) and the conjugate gradient method are adopted to minimize the cost function. A synthetic model of GREATEM survey is used to validate the proposed 3-D forward and inversion algorithms. Then, the field data from two GREATEM surveys are used to test the effectiveness and accuracy of the proposed inversion algorithm. The reconstructed conductivity structures are consistent with geological drilling results, confirming the potential of our method for solving the 3-D GREATEM inversion problems in geophysical engineering applications. This paper represents the first application of the BCGS-FFT and BIM algorithms to a GREATEM system. [ABSTRACT FROM AUTHOR]

A novel localization algorithm, which considers nonline-of-sight (NLOS) propagation, is proposed in this paper. By introducing a transfer function that relates the field at a given receiver to the source as a function of frequency and position, the NLOS effect can be mitigated and the propagation channel can be calibrated back to free space. The simulation results demonstrate that the proposed algorithm performs much better than that using the usual line-of-sight time difference of arrival (TDOA) localization, and the accuracy is only limited by the sampling frequency. The measurements verify the improvement in using the improved TDOA method versus the usual TDOA method. [ABSTRACT FROM AUTHOR]

In this paper, we present a feasible predictor-corrector interior-point method for symmetric cone optimization problem in the large neighbourhood of the central path. The method is generalization of Ai-Zhang's predictor-corrector algorithm to the symmetric cone optimization problem. Starting with a feasible point in given large neighbourhood of the central path, the algorithm still terminates in at most iterations. This matches the best known iteration bound that is usually achieved by short-step methods, thereby, closing the complexity gap between long- and short-step interior-point methods for symmetric cone optimization. The preliminary numerical results on a selected set of NETLIB problems show advantage of the method in comparison with the version of the algorithm that is not based on the predictor-corrector scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a non-monotone line search method for solving optimization problems on Stiefel manifold. The main novelty of our approach is that our method uses a search direction based on a linear combination of descent directions and a Barzilai-Borwein line search. The feasibility is guaranteed by projecting each iterate on the Stiefel manifold through SVD (singular value decomposition) factorizations. Some theoretical results for analysing the algorithm are presented. Finally, we provide numerical experiments for comparing our algorithm with other state-of-the-art procedures. The code is available online. The experimental results show that the proposed algorithm is competitive with other approaches and for particular problems, the computational performance is better than the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Highlights • The proposed method introduced redundant measurements to improve the accuracy. • A weighting algorithm which can make a further improvement of the accuracy. • An iterative method based on the weighting algorithm. • Threshold setting method which can make the accuracy controllable. Abstract As a mature technology, photogrammetry is widely applied in today's engineering measurement field. However, because of the limitation of the port condition, it is impossible to obtain photos that are taken at ideal angles and distances when photogrammetry is used to measure port hoisting machinery, and this leads to invalid measurements with low accuracy data. To solve this problem, a new algorithm is proposed in this work. First, the proposed method introduces redundant measurements through an intersection point prediction algorithm to improve the measurement data's accuracy. Second, a weighting algorithm based on the lens distortion model is then provided to further improve accuracy. Third, an iterative method is established from the threshold setting method based on the weighting algorithm. Thus, the quality of the final measurement could be controllable. Finally, an experiment is devised for the characteristics of the algorithm and the port condition. The results demonstrate that the method described in this paper significantly improves the accuracy of the measuring results of photogrammetry while photos used for the calculation were taken at unsatisfactory angles and distances caused by the limitation of the port condition. [ABSTRACT FROM AUTHOR]

Aligning 2.5D views has been extensively explored in the past decades, where most prior works have concentrated on object data with complex structures. This paper presents a method to align real-word scene scans with challenging features such as noise, poor geometric information, and highly repeatable patterns. Our method consists of two modules: pairwise and multiview alignments. Key to the proposed pairwise alignment method is the rotational contour signature geometric feature and voting-based correspondence selection algorithm. The former promises strong discriminative power for 2.5D scene data, while the latter affords high-quality correspondences via a voting process for all raw feature matches using L2 distance and point pair affinity constraints. For the multiview alignment method, we first use a connected graph algorithm to establish the connections of all 2.5D views for coarse merging; then, we propose a shape-growing iterative closest point algorithm for further refinement. Experiments are conducted on scene point cloud datasets addressing both the indoor and outdoor scenarios, whereby we demonstrate that the proposed pairwise alignment method clearly outperforms the state of the art. Moreover, the proposed multiview alignment method manages to put multiple unordered 2.5D scene fragments into a unified coordinate system automatically, accurately, and efficiently. [ABSTRACT FROM AUTHOR]

This paper presents a macromodeling technique for modeling distributed circuits characterized by noisy frequency-domain data. The proposed method is based on an iterative Loewner matrix (LM) approach. Using the LM approximation of previous iterations, the singular values and orthonormal matrices of the Loewner pencil are made to be more accurate. This approach is shown to minimize the biasing effect of the noisy data due to the nonlinearity of the singular value decomposition operation, resulting in more accurate poles and residues. Numerical examples illustrate that for noisy frequency-domain data, the proposed algorithm creates more accurate macromodels when compared with the traditional LM approach. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study the problem of adaptive trajectory tracking control for a class of nonlinear systems with structured parametric uncertainties. We propose to use an iterative modular approach: we first design a robust nonlinear state feedback that renders the closed‐loop input‐to‐state stable (ISS). Here, the input is considered to be the estimation error of the uncertain parameters, and the state is considered to be the closed‐loop output tracking error. Next, we propose an iterative adaptive algorithm, where we augment this robust ISS controller with an iterative data‐driven learning algorithm to estimate online the parametric uncertainties of the model. We implement this method with two different learning approaches. The first one is a data‐driven multiparametric extremum seeking method, which guarantees local convergence results, and the second is a Bayesian optimization‐based method called Gaussian Process Upper Confidence Bound, which guarantees global results in a compact search set. The combination of the ISS feedback and the data‐driven learning algorithms gives a learning‐based modular indirect adaptive controller. We show the efficiency of this approach on a two‐link robot manipulator numerical example. [ABSTRACT FROM AUTHOR]

Highlights • Thermal resistance network model based on three-dimensional structures. • Complete 3-D temperature distribution at arbitrary position. • The convection coefficient calculation based on iterative algorithm. • Accurate temperature distribution suitable for three-dimensional structures. Abstract A thermal resistance network model based on three-dimensional structure is proposed in this paper. In a chip package newly developed method (three-dimensional stacked package), the thermal distribution management is essential. In order to improve the thermal design of a chip package structure, a thermal resistance network model of temperature distribution is established based on the thermal resistance network and the principle of spherical wall heat conduction. The principle of spherical wall heat conduction is adopted to calculate the complete 3-D temperature distribution at arbitrary position. For the analysis of the thermal resistance of thermal convection, the convection coefficient is calculated based on iterative algorithm. When the heating power arises from 0.27 W to 2.18 W, the errors between the calculation results and the experimental results are less than 5%. The experimental results validate the calculation results well. [ABSTRACT FROM AUTHOR]

Highlights • A composite vision sensing system is proposed and used in our study. • A spatial vision calibration is also proposed. • A multistep predictive model is established to estimate pool tail height. • The 3D surface of GMAW pool is estimated and reconstructed. Abstract Weld pool is the most direct signal to reflect welding quality, that means measurement and reconstruction of the pool surface is one of the most urgent task in industrial robotic welding. A lot of researches have been done in this area, but the strong welding arc and the violent oscillation of weld pool affect their effectiveness. To better resolve this problem, a composite vision sensing system was proposed in our previous study, which consists of an active vision part and a passive vision part. The geometric parameters of the weld cross section and the weld pool have been successfully extracted by using this system. Based on the obtained information, the dynamic relationship between the weld speed and the bead geometric parameters is established by multistep predictive model based on neural network. By using the established model and iterative algorithm, the pool tail height is estimated in this paper. A spatial vision calibration is also proposed to calculate the real geometric parameters of the weld pool. Then the 3D surface of the weld pool is also reconstructed by the means of space curved surface fitting. Finally, the verification experiment is also conducted to verify the feasibility of this method. [ABSTRACT FROM AUTHOR]

This paper develops a parameter estimation algorithm for linear continuous-time systems based on the hierarchical principle and the parameter decomposition strategy. Although the linear continuous-time system is a linear system, its output response is a highly nonlinear function with respect to the system parameters. In order to propose a direct estimation algorithm, a criterion function is constructed between the response output and the observation output by means of the discrete sampled data. Then a scheme by combining the Newton iteration and the least squares iteration is builded to minimise the criterion function and derive the parameter estimation algorithm. In light of the different features between the system parameters and the output function, two sub-algorithms are derived by using the parameter decomposition. In order to remove the associate terms between the two sub-algorithms, a Newton and least squares iterative algorithm is deduced to identify system parameters. Compared with the Newton iterative estimation algorithm without the parameter decomposition, the complexity of the hierarchical Newton and least squares iterative estimation algorithm is reduced because the dimension of the Hessian matrix is lessened after the parameter decomposition. The experimental results show that the proposed algorithm has good performance. [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]

Compressive sensing (CS) has recently drawn considerable attentions in signal and image processing communities as a joint sampling and compression approach. Generally, the image CS reconstruction can be formulated as an optimization problem with a properly chosen regularization function based on image priors. In this paper, we propose an efficient image block compressive sensing (BCS) reconstruction method, which combine the best of group-based sparse representation (GSR) model and nonlocal total variation (NLTV) model to regularize the solution space of the image CS recovery optimization problem. Specifically, the GSR model is utilized to simultaneously enforce the intrinsic local sparsity and the nonlocal self-similarity of natural images, while the NLTV model is explored to characterize the smoothness of natural images on a larger scale than the classical total variation (TV) model. To efficiently solve the proposed joint regularized optimization problem, an algorithm based on the split Bregman iteration is developed. The experimental results demonstrate that the proposed method outperforms current state-of-the-art image BCS reconstruction methods in both objective quality and visual perception. [ABSTRACT FROM AUTHOR]