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This paper focuses on the solvability of multistage pseudomonotone stochastic variational inequalities (SVIs). On the one hand, some known solvability results of pseudomonotone deterministic variational inequalities cannot be directly extended to multistage pseudomonotone SVIs, so we construct the isomorphism between both and then establish theoretical results on the existence, convexity, boundedness and compactness of the solution set for multistage pseudomonotone SVIs via such an isomorphism. On the other hand, there does not exist a special algorithm for solving the multistage pseudomonotone SVIs so far, so we propose some sufficient conditions on the elicitability of multistage pseudomonotone SVIs, which opens the door for applying Rockafellar's elicited progressive hedging algorithm to solve such SVIs. Numerical results on solving a two-stage stochastic market optimization problem and randomly generated two-stage pseudomonotone linear complementarity problems are presented. [ABSTRACT FROM AUTHOR]

In the setting of the Extended Horizontal Linear Complementarity Problem, we study boundedness, nonemptyness, uniqueness and connectedness issues of solution sets. We introduce column R 0 -W and column SSM -W properties and, using degree theory, establish boundedness and existence results. Some uniqueness results are also covered. We provide a necessary condition and a sufficient condition for EHLCP to have connected solution sets. [ABSTRACT FROM AUTHOR]

The global macroeconomic shocks of the last decade entail the restructuring of national production networks and induce processes of input substitution. We suggest mathematical tools of Young duality for variational inequalities for studying these processes. Based on the tools we provide, a new mathematical model of a production network with several final consumers is created. The model is formulated as a pair of conjugated problems: a complementarity problem for optimal resource allocation with neoclassical production functions and the Young dual problem for equilibrium price indices on network products. The solution of these problems gives an equilibrium point in the space of network inter-industry flows and price indices on goods. Based on our previous results, we suggest an algorithm for model identification with an official economic statistic in the case of constant elasticity of substitution production functions. We give an explicit solution to the complementarity problems in this case and develop the algorithm of the inter-industry flows scenario projection. Since the algorithm needs the scenario projection of final sales structure as its input, we suggest a modified methodology that allows the calculation of scenario shifts in final consumer spending. To do this, we employ the generalized nonparametric method of demand analysis. As a result, we develop new technology for scenario calculation of a national input–output table, including shifts in final consumer spending. The technology takes into account a substitution of inputs in the network and is based on officially published national statistics data. The application of the methodology to study tax collection scenarios for Kazakhstan's production network is demonstrated. [ABSTRACT FROM AUTHOR]

Open government data (OGD) has great potential to produce political, social and economic values, but its utilization is far from expectations. This paper intends to study the factors and mechanisms that affect open data utilization. Drawing from the resource complementarity perspective of the resource-based theory, this study examines the moderating effect of the six dimensions of governance on open data quality and open data utilization. The model is empirically tested by the partial least squares (PLS) method using sample data from 115 countries. The results show that the five dimensions of governance (including voice and accountability, government effectiveness, rule of law, regulatory quality and control of corruption) moderated the relationship of open data quality with open data utilization in a positive direction. In addition, the impact of open data quality on open data utilization still holds true at the global level. The conclusion is that governance, as an important complementary resource to promote open data activities, can better promote open data utilization and value generation when combined with open data resources of various countries. Different governance levels are an important explanatory variable for the large differences of open data utilization among countries. In open data practice, governments should not only improve open data quality, but also improve the national governance level to provide complementary resource support for the utilization and value generation of open data in their countries. [ABSTRACT FROM AUTHOR]

For solving horizontal linear complementarity problem (HLCP), we propose a nonlinear lopsided PSS-like modulus-based matrix splitting iteration method and a new nonlinear lopsided PSS-like modulus-based matrix splitting iteration method according to the characteristics of the HLCP. The proposed methods contain the nonlinear lopsided HSS-like modulus-based matrix splitting iteration method and a new nonlinear lopsided HSS-like modulus-based matrix splitting iteration method. When the system matrices are positive definite matrices, we analyze the proposed methods in theory and prove the convergence of these methods. Numerical experiments in this paper demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Model-predictive control (MPC) offers significant advantages in addressing constraint-related challenges and plays a pivotal role in self-driving car technology. Its primary goal is to achieve precise trajectory tracking while prioritizing vehicle stability and safety. However, real-time operations often face challenges related to computational demands and low computational efficiency. To address these challenges, this paper introduces a novel lateral control algorithm for self-driving vehicles, which utilizes the linear complementarity problem (LCP) instead of the conventional quadratic programming (QP) method as the MPC optimization solution. This innovative approach incorporates the electric steering system into the vehicle dynamics model, allowing for precise torque regulation of the steering motor and enhancing control accuracy. The MPC algorithm adopts the LCP solution method to calculate control signals based on the vehicle's state, ensuring both rapid and stable vehicle control. Simulation results demonstrate that the proposed MPC algorithm, utilizing the LCP solution method, effectively addresses efficiency issues in the lateral motion of self-driving cars. This leads to improvements in both driving stability and real-time performance. Overall, this innovative approach lays a solid foundation for the practical implementation of self-driving cars. [ABSTRACT FROM AUTHOR]

In this paper, a new error bound for the linear complementarity problems of B-matrices which is a subclass of the P-matrices is presented. Theoretical analysis and numerical example illustrate that the new error bound improves some existing results. [ABSTRACT FROM AUTHOR]

Graph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing semi-supervised multi-view clustering methods only captures the pairwise relationships between samples, and cannot fully explore the higher-order information and complex structure among multiple sample points. Additionally, most existing methods do not make full use of the complementary information and spatial structure contained in multi-view data, which is crucial to clustering results. We propose a novel hypergraph learning-based semi-supervised multi-view spectral clustering approach to overcome these limitations. Specifically, the proposed method fully considers the relationship between multiple sample points and utilizes hypergraph-induced hyper-Laplacian matrices to preserve the high-order geometrical structure in data. Based on the principle of complementarity and consistency between views, this method simultaneously learns indicator matrices of all views and harnesses the tensor Schatten p-norm to extract both complementary information and low-rank spatial structure within these views. Furthermore, we introduce an auto-weighted strategy to address the discrepancy between singular values, enhancing the robustness and stability of the algorithm. Detailed experimental results on various datasets demonstrate that our approach surpasses existing state-of-the-art semi-supervised multi-view clustering methods. [ABSTRACT FROM AUTHOR]

A dynamic model (DM) is proposed to solve the weighted linear complementarity problem (WLCP). The DM is based on an approximate reformulation of the WLCP into an unconstrained minimization problem. Under certain hypothesis, we prove that the equilibrium point of the DM is asymptotically stable and it is an approximate solution of the monotone WLCP. Simulation results are given to show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper, we propose the horizontal implicit complementarity problems, which cover many complementarity problems that have wide applications. After reformulating the horizontal implicit complementarity problem as an implicit fixed-point equation, the modulus-based matrix splitting iteration methods are applied to solve the problems. Besides, this paper introduces a new kind of matrix splitting, called modulus-based Hermitian and skew-Hermitian matrix relaxation splitting. The corresponding modulus-based matrix splitting iteration method is shown superior to the nonsmooth Newton's method and the existing matrix splitting schemes by some numerical experiments. The convergence analysis is given and validates that our methods are more practical in applications. [ABSTRACT FROM AUTHOR]

We discuss the weighted complementarity problem, extending the nonlinear complementarity problem on R n . In contrast to the NCP, many equilibrium problems in science, engineering, and economics can be transformed into WCPs for more efficient methods. Smoothing Newton algorithms, known for their at least locally superlinear convergence properties, have been widely applied to solve WCPs. We suggest a two-step Newton approach with a local biquadratic order convergence rate to solve the WCP. The new method needs to calculate two Newton equations at each iteration. We also insert a new term, which is of crucial importance for the local biquadratic convergence properties when solving the Newton equation. We demonstrate that the solution to the WCP is the accumulation point of the iterative sequence produced by the approach. We further demonstrate that the algorithm possesses local biquadratic convergence properties. Numerical results indicate the method to be practical and efficient. [ABSTRACT FROM AUTHOR]

In this paper, we consider a sparse program with symmetric cone constrained parameterized generalized equations (SPSCC). Such a problem is a symmetric cone analogue with vector optimization, and we aim to provide a smoothing framework for dealing with SPSCC that includes classical complementarity problems with the nonnegative cone, the semidefinite cone and the second-order cone. An effective approximation is given and we focus on solving the perturbation problem. The necessary optimality conditions, which are reformulated as a system of nonsmooth equations, and the second-order sufficient conditions are proposed. Under mild conditions, a smoothing Newton approach is used to solve these nonsmooth equations. Under second-order sufficient conditions, strong BD-regularity at a solution point can be satisfied. An inverse linear program is provided and discussed as an illustrative example, which verified the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Complementarity is common in land-assembly problems, such as a developer buying the entirety of apartment units to complete a development project. Holdout (delay or block of projects by sellers) is common in these problems under unanimity-rule. Motivated by the recent policy practices in strata-sales, we introduce a new bargaining model with quota-rule. We show that under quota-rule, there is no holdout when there are two sellers or players are sufficiently patient. No-holdout equilibrium is unique, efficient and yields immediate sale of all goods as outcome. When quota rule is used, efficient equilibrium is still obtained under alternative bargaining protocols and with multiple sellers. We also show that the no-holdout result is not due to the specific bargaining protocol. [ABSTRACT FROM AUTHOR]

The theoretical reflection on the nature of school algebra and the development of algebraic thinking from the first educational levels is a relevant topic in mathematics education. In this paper, we first summarize and clarify the positions held on this topic by two theoretical frameworks: the Theory of Objectification and the Onto-semiotic Approach. We identify concordances and complementarities in the respective conceptions of algebra and of the processes of developing algebraic thinking, as a result of applying these frameworks to analyse some students' responses to a task on numerical patterns. We also analyse and compare the didactic models proposed in both theoretical frameworks for developing algebraic thinking. The analysis of the algebraic thinking and learning episodes is also used to illustrate a general discussion on the identity and boundaries of these theoretical frameworks and the possibilities of coordination and integration. [ABSTRACT FROM AUTHOR]

In this paper, we go on studying the convergence property of the new modulus-based matrix splitting (NMMS) method for linear complementarity problem of H + -matrix. A new sufficient condition of the NMMS method is obtained, which is weaker than the result in the perviously work by Wu and Li (Opt Lett 16:1427–1443, 2022). [ABSTRACT FROM AUTHOR]

We address a large class of Mathematical Programs with Linear Complementarity Constraints which minimizes a continuously differentiable DC function (Difference of Convex functions) on a set defined by linear constraints and linear complementarity constraints, named Difference of Convex functions programs with Linear Complementarity Constraints. Using exact penalty techniques, we reformulate it, via four penalty functions, as standard Difference of Convex functions programs. The difference of convex functions algorithm (DCA), an efficient approach in nonconvex programming framework, is then developed to solve the resulting problems. Two particular cases are considered: quadratic problems with linear complementarity constraints and asymmetric eigenvalue complementarity problems. Numerical experiments are performed on several benchmark data, and the results show the effectiveness and the superiority of the proposed approaches comparing with some standard methods. [ABSTRACT FROM AUTHOR]

By equivalently transforming a class of weakly nonlinear complementarity problems into a modulus equation, and introducing a smoothing approximation of the absolute value function, a smoothing Newton method is established for solving the weakly nonlinear complementarity problem. Under some mild assumptions, the proposed method is shown to possess global convergence and locally quadratical convergence. Especially, the global convergence results do not need a priori existence of an accumulation point with some suitable conditions. Numerical results are given to show the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a full-Newton step interior-point method for the weighted linear complementarity problem (wLCP) model of the Fisher market equilibrium problem. The weighted complementarity problem (wCP) is a generalization of the complementarity problem (CP) with the nonnegative weight vector, where the zero on the right-hand side is replaced by the nonnegative weight vector. The importance of a wCP lies in the fact that many equilibrium problems in science, engineering, and economics can be reformulated as wCPs, which lead to the development of highly efficient algorithms. We extend a full-Newton step interior-point method (IPM) to the wLCP model of the Fisher problem. New search directions are obtained by using a kernel function in the scaled Newton system. The algorithm takes only full-Newton steps, thus, avoiding the calculation of the step size which is computationally advantageous. Under the suitable assumptions, the algorithm is shown to have global convergence and polynomial complexity. Some numerical results indicate the efficiency of the algorithm. To the best of our knowledge, this is the first full-Newton step IPM for Fisher problems, which uses the kernel function to obtain new search directions. [ABSTRACT FROM AUTHOR]

In this paper, we develop a competitive supply chain network equilibrium model with multiple products, in which electronic commerce is considered. After analysing equilibrium conditions of the manufactures, retailers, and demand markets, and giving economic interpretations, equilibrium conditions of the supply chain network formulated as an equivalent complementarity model are established. In addition, a modified subgradient extragradient algorithm is proposed to find the solution of the complementarity problem, and the convergence of the proposed algorithm is proved. We also give some numerical examples to demonstrate the efficiency of the given model and algorithm. [ABSTRACT FROM AUTHOR]

A new relaxed acceleration two-sweep modulus-based matrix splitting (NRATMMS) iteration method is developed to solve linear complementarity problems. The convergence of the NRATMMS method is established with the system matrix $ A $ being an $ H_{+} $-matrix. Numerical experiments show that the proposed method is superior to some existing algorithms under appropriate conditions.

For solving the linear complementarity problem (LCP), we propose a preconditioned new modulus-based matrix splitting (PNMMS) iteration method by extending the state-of-the-art new modulus-based matrix splitting (NMMS) iteration method to a more general framework with a customized preconditioner. We devise a generalized preconditioner that is associated with both H+-matrix A and vector q of the LCP. The convergence analysis is conducted under some mild conditions. In particular, we provide a comparison theorem to theoretically show the PNMMS method accelerates the convergence rate. Numerical experiments further illustrate that the PNMMS method is efficient and has better performance for solving the large and sparse LCP.

Simultaneous Localization and Mapping (SLAM) forms the foundation of vehicle localization in autonomous driving. Utilizing high-precision 3D scene maps as prior information in vehicle localization greatly assists in the navigation of autonomous vehicles within large-scale 3D scene models. However, generating high-precision maps is complex and costly, posing challenges to commercialization. As a result, a global localization system that employs low-precision, city-scale 3D scene maps reconstructed by unmanned aerial vehicles (UAVs) is proposed to optimize visual positioning for vehicles. To address the discrepancies in image information caused by differing aerial and ground perspectives, this paper introduces a wall complementarity algorithm based on the geometric structure of buildings to refine the city-scale 3D scene. A 3D-to-3D feature registration algorithm is developed to determine vehicle location by integrating the optimized city-scale 3D scene with the local scene generated by an onboard stereo camera. Through simulation experiments conducted in a computer graphics (CG) simulator, the results indicate that utilizing a completed low-precision scene model enables achieving a vehicle localization accuracy with an average error of 3.91 m, which is close to the 3.27 m error obtained using the high-precision map. This validates the effectiveness of the proposed algorithm. The system demonstrates the feasibility of utilizing low-precision city-scale 3D scene maps generated by unmanned aerial vehicles (UAVs) for vehicle localization in large-scale scenes. [ABSTRACT FROM AUTHOR]

There are many studies on the well‐known modulus‐based matrix splitting (MMS) algorithm for solving complementarity problems, but very few studies on its optimal parameter, which is of theoretical and practical importance. Therefore and here, by introducing a novel mapping to explicitly cast the implicit fixed point equation and thus obtain the iteration matrix involved, we first present the estimation approach of the optimal parameter of each step of the MMS algorithm for solving linear complementarity problems on the direct product of second‐order cones (SOCLCPs). It also works on single second‐order cone and the non‐negative orthant. On this basis, we further propose an iteration‐independent optimal parameter selection strategy for practical usage. Finally, the practicability and effectiveness of the new proposal are verified by comparing with the experimental optimal parameter and the diagonal part of system matrix. In addition, with the optimal parameter, the effectiveness of the MMS algorithm can indeed be greatly improved, even better than the state‐of‐the‐art solvers SCS and SuperSCS that solve the equivalent SOC programming. [ABSTRACT FROM AUTHOR]

At present, speech emotion recognition models based on multi-modal fusion generally suffer from the inability to make full use of the commonality and complementarity between multimodal features, the inability to effectively optimize and aggregate sample features by using the topological structure characteristics between sample features, and the high complexity of existing models. Therefore, in this paper, it introduce graph neural network. On the one hand, in the feature optimization stage, it use the text features optimized by the graph neural network as a shared representation to reconstruct the adjacency matrix based on acoustic features, so that the topological structure characteristics of the acoustic features contain text information, thus achieving multi-modal fusion. On the other hand, in the label prediction stage, it use the graph neural network to fully aggregate the similarity information contained in the adjacent nodes of the current node to optimize the characteristics of the current node globally to improve the accuracy of emotion recognition. At the same time, in order to prevent the over-smoothing problem that may occur during the training of the graph neural network, it perform graph augmentation before the graph neural network training. The experimental results on the public datasets IEMOCAP and RAVDESS show that the proposed model achieves higher recognition accuracy and lower model complexity than the baseline model, and each component of the model contributes to the improvement of model performance. [ABSTRACT FROM AUTHOR]

The class of B 1 -matrices is a subclass of P -matrices and introduced as a generalization of B -matrices. In this paper, we present several properties for B 1 -matrices. Then, the infinity norm upper bound for the inverse of B 1 -matrices is obtained. Furthermore, the error bound for the linear complementarity problem of B 1 -matrices is presented. Finally, some numerical examples are given to illustrate our results. [ABSTRACT FROM AUTHOR]

We study the fair division problem of allocating a mixed manna under additively separable piecewise linear concave (SPLC) utilities. A mixed manna contains goods that everyone likes and bads (chores) that everyone dislikes as well as items that some like and others dislike. The seminal work of Bogomolnaia et al. argues why allocating a mixed manna is genuinely more complicated than a good or a bad manna and why competitive equilibrium is the best mechanism. It also provides the existence of equilibrium and establishes its distinctive properties (e.g., nonconvex and disconnected set of equilibria even under linear utilities) but leaves the problem of computing an equilibrium open. Our main results are a linear complementarity problem formulation that captures all competitive equilibria of a mixed manna under SPLC utilities (a strict generalization of linear) and a complementary pivot algorithm based on Lemke's scheme for finding one. Experimental results on randomly generated instances suggest that our algorithm is fast in practice. Given the PPAD -hardness of the problem, designing such an algorithm is the only non–brute force (nonenumerative) option known; for example, the classic Lemke–Howson algorithm for computing a Nash equilibrium in a two-player game is still one of the most widely used algorithms in practice. Our algorithm also yields several new structural properties as simple corollaries. We obtain a (constructive) proof of existence for a far more general setting, membership of the problem in PPAD , a rational-valued solution, and an odd number of solutions property. The last property also settles the conjecture of Bogomolnaia et al. in the affirmative. Furthermore, we show that, if the number of either agents or items is a constant, then the number of pivots in our algorithm is strongly polynomial when the mixed manna contains all bads. Funding: Financial support from the Division of Computing and Communication Foundations, National Science Foundation (NSF) [Grants 1942321, 1750436] is gratefully acknowledged. [ABSTRACT FROM AUTHOR]

We present a family of inexact Levenberg-Marquardt (LM) methods for the nonlinear equations which takes more general LM parameters and perturbation vectors. We derive an explicit formula of the convergence order of these inexact LM methods under the H derian local error bound condition and the H derian continuity of the Jacobian. Moreover, we develop a family of inexact LM methods with a nonmonotone line search and prove that it is globally convergent. Numerical results for solving the linear complementarity problem are reported. [ABSTRACT FROM AUTHOR]

Multi-focus image fusion (MFIF) combines information by utilizing various image sequences of the same scenes at different of focus depths. The available MFIF method based on generative adversarial networks (GAN) lacks the feature complementarity of multi-focus images, resulting in the loss of details and noises in the generated decision maps. To resolve this problem, the learning framework of a joint distribution was developed via Siamese conditional generative adversarial network (SCGAN). This framework utilizes Siamese conditional generator that produces two-probabilistic feature maps from multi-focus images with complementary information. Additionally, the proposed framework also considers both the diversity of datasets and network convergence. The structural sparse objective function is designed to penalize the prediction of low confidence by sparse calculation of the rows and columns of the matrix. So, it endows a better Dice coefficient with higher values and improves the generalization capability of the GAN. Also, Wasserstein Divergence (DIV) is utilized to optimize the discrimination performance with stable training. In both quantitative and qualitative experiments, SCGAN has better scores on MFIF than other methods. [ABSTRACT FROM AUTHOR]

This paper concerns upper estimates of the projectional coderivative of implicit mappings and corresponding applications on analyzing the relative Lipschitz-like property. Under different constraint qualifications, we provide upper estimates of the projectional coderivative for solution mappings of parametric systems. For the solution mapping of affine variational inequalities, a generalized critical face condition is obtained for sufficiency of its Lipschitz-like property relative to a polyhedral set within its domain under a constraint qualification. The necessity is also obtainable under some regularity or when the polyhedral set is further the domain of the solution mapping. We further discuss possible conditions for the necessity and consider the solution mapping of a linear complementarity problem with a Q0 matrix as an example. [ABSTRACT FROM AUTHOR]

Linear complementarity programming is a generalization of linear programming which encompasses the computation of Nash equilibria for bimatrix games. While the latter problem is PPAD-complete, we show that the tropical analogue of the complementarity problem associated with Nash equilibria can be solved in polynomial time. Moreover, we prove that the Lemke-Howson algorithm carries over the tropical setting and performs a linear number of pivots in the worst case. A consequence of this result is a new class of (classical) bimatrix games for which Nash equilibria computation can be done in polynomial time. [ABSTRACT FROM AUTHOR]

In this article, the implicit-explicit (IMEX) compact schemes are proposed to solve the partial integro-differential equations (PIDEs), and the linear complementarity problems (LCPs) arising in option pricing. A diagonally dominant tri-diagonal system of linear equations is achieved for a fully discrete problem by eliminating the second derivative approximation using the variable itself and its first derivative approximation. The stability of the fully discrete problem is proved using Schur polynomial approach. Moreover, the problem's initial condition is smoothed to ensure the fourth-order convergence of the proposed IMEX compact schemes. Numerical illustrations for solving the PIDEs and the LCPs with constant and variable coefficients are presented. For each case, obtained results are compared with the IMEX finite difference scheme, and it is observed that proposed approach significantly outperforms the finite difference scheme. [ABSTRACT FROM AUTHOR]

In this paper, the interpolating element-free Galerkin method is presented for the p-Laplace double obstacle mixed complementarity problem when 1 < p < 2 and p > 2 . First, a nonlinear power penalty equation is obtained by a power penalty approximation method and the existence and uniqueness of the solution to the power penalty equation are proved when 1 < p < 2 and p > 2 . The convergence of the power penalty solution to the original problem and the penalty estimates are analyzed. Second, the interpolating element-free Galerkin method is constructed for the nonlinear power penalty equation. The numerical implementation is introduced in detail and the convergence of the interpolating element-free Galerkin method is also given. Error estimates indicate that the convergence order depends on not only the spatial step h and the number of bases functions m in the interpolating element-free Galerkin method, but also the index k in the penalty term, the penalty factor λ and p. For different p, the method that how to choose the optimal k and λ is also given. Numerical examples verify error estimates and illustrate the influence of each parameter on the solution. [ABSTRACT FROM AUTHOR]

Some inequalities for elements relation of strictly diagonally dominant M-matrix and its inverse are given, and new upper bounds of the infinity norm for the inverse of strictly diagonally dominant M-matrix are presented. We apply these new bounds to linear complementarity problems (LCPs) and obtain an alternative error bound for LCPs of B S -matrices. A lower bound for the smallest singular value is also provided. Theoretical analysis and numerical examples show the validity of the results. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine interactions between multiple management accounting routines in integrating a new management accounting routine into a routine cluster. Design/methodology/approach: This study uses a theoretical framework based on routine clusters, including routine complementarities. The authors use an in-depth case study to explore interactions of a management accounting routine integrating into a routine cluster. Findings: The findings show that complementarity between an existing and a new management accounting routine facilitates integration of the new routine into a routine cluster. They also suggest that when an ostensive understanding of a routine exists, the integration of the new management accounting routine is stronger, as the new and existing routines in the routine cluster are more closely intertwined. Originality/value: To the best of the authors' knowledge, this paper is among the first to explore the role of intertwinedness of a new management accounting routine and existing organizational routines in integrating a new management accounting routine into a routine cluster. The findings imply that future management accounting research may need to distinguish between different forms of complementarity. [ABSTRACT FROM AUTHOR]

In this article, we investigate the solution of a new class of the absolute value equation (NCAVE) A1x - |B1x - c| = d. Based on spectral radius condition, singular value condition and row and column W-property, some necessary and sufficient conditions for unique solvability for NCAVE are gained. Some new results for the unique solvability of the new generalized absolute value equation (NGAVE) A1x-|B1x| = d are also obtained. [ABSTRACT FROM AUTHOR]

This paper presents delay-dependent stabilization criteria for linear time-varying delay systems. A less conservative stabilization criterion is derived by invoking a new Lyapunov{ Krasovskii functional and then, extended reciprocally convex inequality in combination with Wirtinger's inequality is exploited to obtain an improved stabilization criterion where a set of nonlinear matrix inequalities is solved by applying the cone complementarity algorithm. The proposed stabilization technique transforms a non-convex problem into a nonlinear trace minimization problem which is solved by an iterative approach. Numerical examples are considered to demonstrate the effectiveness of the proposed stabilization criteria and the presented iterative algorithm outperforms some existing results. [ABSTRACT FROM AUTHOR]

To address the problem of accurate recognition and localization of multiple clusters of green persimmons with similar color to the background under natural environment, this study proposes a multi-cluster green persimmon identification method based on improved Faster RCNN was proposed by using the self-built green persimmon dataset. The feature extractor DetNet is used as the backbone feature extraction network, and the model detection attention is focused on the target object itself by adding the weighted ECA channel attention mechanism to the three effective feature layers in the backbone, and the detection accuracy of the algorithm is improved. By maximizing the pooling of the lower layer features with the added attention mechanism, the high and low dimensions and magnitudes are made the same. The processed feature layers are combined with multi-scale features using a serial layer-hopping connection structure to enhance the robustness of feature information, effectively copes with the problem of target detection of objects with obscured near scenery in complex environments and accelerates the detection speed through feature complementarity between different feature layers. In this study, the K-means clustering algorithm is used to group and anchor the bounding boxes so that they converge to the actual bounding boxes, The average mean accuracy (mAP) of the improved Faster RCNN model reaches 98.4%, which was 11.8% higher than that of traditional Faster RCNN model, which also increases the accuracy of object detection during regression prediction. and the average detection time of a single image is improved by 0.54s. The algorithm is significantly improved in terms of accuracy and speed, which provides a basis for green fruit growth state monitoring and intelligent yield estimation in real scenarios. [ABSTRACT FROM AUTHOR]

Trunk-sharing (TS), a nascent ride-sharing (RS)-based crowdsourced delivery service, has been reshaping the pattern of urban logistics and the spatial distribution of urban traffic congestion. However, it is not yet clear how RS and TS interact and affect the urban traffic system. To this end, a link-node-based user equilibrium model -is proposed to describe the users' decisions in the presence of RS and TS program and thereby to forecast the long-term effects of these two modes and of existed policies, such as car restrictions and cordon tolling. The model is formulated as a nonlinear complementarity problem and the existence of equilibria is proven under some mild conditions. Numerical results showed that integrating RS and TS can dramatically decreases the total travel time. Moreover, car restriction- can incentivize more TS to enter the central business districts (CBDs), while cordon tolling- can incentivize more RS participants to enter the CBDs. [ABSTRACT FROM AUTHOR]

In this paper, we propose to solve Pareto eigenvalue complementarity problems by using interior-point methods. Precisely, we focus the study on an adaptation of the Mehrotra Predictor Corrector Method (MPCM) and a Non-Parametric Interior Point Method (NPIPM). We compare these two methods with two alternative methods, namely the Lattice Projection Method (LPM) and the Soft Max Method (SM). On a set of data generated from the Matrix Market, the performance profiles highlight the efficiency of MPCM and NPIPM for solving eigenvalue complementarity problems. We also consider an application to a concrete and large size situation corresponding to a geomechanical fracture problem. Finally, we discuss the extension of MPCM and NPIPM methods to solve quadratic pencil eigenvalue problems under conic constraints. [ABSTRACT FROM AUTHOR]

In this paper, first we propose a general modulus-based matrix splitting iteration method for solving horizontal linear complementarity problems. In order to improve the computing efficiency, we further propose a preconditioned general modulus-based matrix splitting iteration method. We establish the convergence theorems when the coefficient matrices are (symmetric) positive definite matrices and H+-matrices, respectively. Numerical results show that the proposed preconditioned general modulus-based matrix splitting iteration method is superior than some existing methods and the general modulus-based matrix splitting iteration method. [ABSTRACT FROM AUTHOR]

In this paper, for solving large sparse vertical linear complementarity problems, the modulus-based synchronous multisplitting iteration method is established. Convergence theorems of the proposed method are given, which are shown to generalize and improve the existing results of the serial method. Numerical tests by OpenACC technique are given to show the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers polynomial optimization with unbounded sets. We give a homogenization formulation and propose a hierarchy of Moment-SOS relaxations to solve it. Under the assumptions that the feasible set is closed at infinity and the ideal of homogenized equality constraining polynomials is real radical, we show that this hierarchy of Moment-SOS relaxations has finite convergence, if some optimality conditions (i.e., the linear independence constraint qualification, strict complementarity and second order sufficient conditions) hold at every minimizer, including the one at infinity. Moreover, we prove extended versions of Putinar-Vasilescu type Positivstellensatz for polynomials that are nonnegative on unbounded sets. The classical Moment-SOS hierarchy with denominators is also studied. In particular, we give a positive answer to a conjecture of Mai, Lasserre and Magron in their recent work. [ABSTRACT FROM AUTHOR]

In this paper, we consider solving the tensor complementarity problem (TCP). We first introduce the concept of the implicit Z-tensor, which is a generalization of Z-tensor. Then, based on a new fixed point reformulation of the TCP, we design an iterative algorithm for solving the TCP with an implicit Z-tensor under the assumption that the feasible set of the problem involved is nonempty. We prove that the proposed fixed point iterative method converges monotonically downward to a solution of the TCP. Furthermore, we establish the global linear rate of convergence of the proposed method under some reasonable assumptions. Compared with the existing related studies, the proposed method not only solves a wider range of TCPs, but also has a lower computational cost. The numerical results verify our theoretical findings. [ABSTRACT FROM AUTHOR]

In this article, we present a class of new modified modulus-based matrix spitting methods to process the large and sparse linear complementarity problem (LCP). Using two positive diagonal matrices, we formulate an implicit fixed-point equation that is equivalent to a LCP and an iterative method is presented to solve the LCP with a P-matrix based on a fixed-point equation. Also, we provide some sufficient convergence conditions for the proposed methods when the system matrix is an H + -matrix. We provide two numerical examples to demonstrate the efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

A new relaxed acceleration two-sweep modulus-based matrix splitting (NRATMMS) iteration method is developed to solve linear complementarity problems. The convergence of the NRATMMS method is established with the system matrix being an -matrix. Numerical experiments show that the proposed method is superior to some existing algorithms under appropriate conditions. [ABSTRACT FROM AUTHOR]