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Having in mind singular solutions of smooth reformulations of complementarity problems, arising unavoidably when the solution in question violates strict complementarity, we study the behavior of Newton-type methods near singular solutions of nonlinear equations, assuming that the operator of the equation possesses a strongly semismooth derivative, but is not necessarily twice differentiable. These smoothness restrictions give rise to peculiarities of the analysis and results on local linear convergence and asymptotic acceptance of the full step, the issues addressed in this work. Moreover, we consider not only the basic Newton method, but also some stabilized versions of it intended for tackling singular (including nonisolated) solutions. Applications to nonlinear complementarity problems are also dealt with. [ABSTRACT FROM AUTHOR]

For constrained equations with nonisolated solutions and a certain family of Newton-type methods, it was previously shown that if the equation mapping is 2-regular at a given solution with respect to a direction which is interior feasible and which is in the null space of the Jacobian, then there is an associated large (not asymptotically thin) domain of starting points from which the iterates are well defined and converge to the specific solution in question. Under these assumptions, the constrained local Lipschitzian error bound does not hold, unlike the common settings of convergence and rate of convergence analyses. In this work, we complement those previous results by considering the case when the equation mapping is 2-regular with respect to a direction in the null space of the Jacobian which is in the tangent cone to the set, but need not be interior feasible. Under some further conditions, we still show linear convergence of order 1/2 from a large domain around the solution (despite degeneracy, and despite that there may exist other solutions nearby). Our results apply to constrained variants of the Gauss–Newton and Levenberg–Marquardt methods, and to the LP-Newton method. An illustration for a smooth constrained reformulation of the nonlinear complementarity problem is also provided. [ABSTRACT FROM AUTHOR]

In this paper, a modulus-based Shamanskii-Like Levenberg-Marquardt method is proposed for solving nonlinear complementarity problems (NCPs). First, the NCP is reformulated in the form of an equivalent non-smooth system of equations. Then, a non-smooth Shamanskii-Like Levenberg-Marquardt method using a non-monotone r-order Armijo line search is developed by generalizing a smooth Levenberg-Marquardt method to solve the resulting system. Global convergence of the proposed method is achieved under some suitable assumptions. Numerical experiments verify the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a Mehrotra-type predictor–corrector algorithm for monotone nonlinear complementarity problems over symmetric cones. The proposed algorithm is a second-order predictor–corrector interior point algorithm. Based on a one-norm wide neighbourhood, the iteration complexity of the algorithm is estimated and some numerical results are provided. The numerical results show that the algorithm is efficient and reliable. [ABSTRACT FROM AUTHOR]

We present a sufficient condition for monotonicity of the nonlinear nonsmooth system generated by Fischer–Burmeister function associated with nonlinear complementarity problem. Based on the presented condition, the nonlinear complementarity problem considered in this paper is equivalently formulated to a nonsmooth monotone system. We then propose a modified multivariate spectral gradient projection method for the resulting system, and establish the global convergence without smoothness and Lipschitz condition. Preliminary numerical experiments show that, compared to some existing methods, the proposed method is effective. [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]

Among the most successful techniques for solving nonlinear complementarity problems is the one consisting of reformulation of the problem in question as a system of nonlinear equations, by means of the so-called complementarity functions. Different complementarity functions lead to nonlinear systems with different smoothness and regularity properties, thus allowing for application of different classed of numerical methods. In this paper we compare the Newton method for the smooth reformulation with the semismooth Newton method for the reformulation relying on the nonsmooth Fischer--Burmeister complementarity function, with a special emphasis on the cases when the solution in question violates the strict complementarity condition. [ABSTRACT FROM AUTHOR]

We enrich the theory of variational inequalities in the case of an aggregative structure by implementing recent results obtained by using the Selten–Szidarovszky technique. We derive existence, semi-uniqueness and uniqueness results for solutions and provide a computational method. As an application we derive very powerful practical equilibrium results for Nash equilibria of sum-aggregative games and illustrate with Cournot oligopolies. [ABSTRACT FROM AUTHOR]

We consider a geometrical approach to the optimisation problems motivated by transportation system management. Here, we provide a comprehensive account of geometric programming based on the elementary Finsler geometry in R n . Then, we present a Finslerian dynamical model for the nonlinear complementarity problem of traffic equilibrium that can be applied to a variety of equilibrium problems. [ABSTRACT FROM AUTHOR]

Nonlinear complementarity problems (NCP) have been extensively studied in optimization due to its widespread applications. In this paper, we utilize the neural dynamic approach to solve the NCP. By integrating the famous FB function an NR function, we construct a new type of complementarity functions with one parameter p , which is aesthetically pleasing and easy to apply. Combined with the Lagrange multiplier method, a new type of merit function is also developed. Based on the complementarity function and merit function, we transform the NCP into an unconstrained minimization problem. Then, by KKT condition and gradient descent method, we propose a Lagrange neural network method. Under mild conditions, every equilibrium point of the proposed neural network model is a solution of the NCP. More importantly, by throwing a delay factor, we also develop a novel delayed neural network model. Both of these networks are shown to be global convergent, Lyapunov stable and exponential stable. Finally, we give some numerical experiments of the two neural network approaches and also give some applications to the compressed sensing signal reconstruction. Simulation results indicate that the parameter p in the complementarity function plays an important role on the convergence rate of the two neural networks. The delayed neural network outperforms the non-delayed neural network in some specific situations. It also demonstrates that the two networks can efficiently reconstruct the original signals. • A new type of complementary functions is constructed. • A new neural network method is proposed. • We propose a delayed neural network and give its complete convergence analysis. • The two neural networks are applied to solve the CS efficiently. [ABSTRACT FROM AUTHOR]

The article presents a novel model order reduction method for mechanical problems in linear elasticity with nonlinear contact conditions. Recently, we have proposed an efficient reduction scheme for the node‐to‐node formulation [Manvelyan et al., Comput Mech 68, 1283–1295 (2021)] that leads to linear complementarity problems (LCPs). Here, we enhance the underlying contact problem to a node‐to‐segment formulation, which leads to quadratic inequalities as constraints. The adjoint system corresponds to a nonlinear complementarity problem that describes the Lagrange multiplier. The latter is solved by a Newton‐type iteration based on a LCP solver in each time step. Since the maximal set of potential contact nodes is predefined, an additional substructuring by Craig–Bampton can be performed. This contact treatment turns out to be necessary and allows exclusion of the Lagrange multipliers and the nodal displacements at contact from reduction. The numerical solutions of the reduced contact problem achieve high accuracy and the dynamic contact carries over the behavior of the full order model. Moreover, if the contact area is small compared to the overall structure, the reduction scheme performs very efficiently. The performance of the resulting reduction method is assessed on two 2D computational examples from linear elasticity. [ABSTRACT FROM AUTHOR]

In this paper, we systematically study the properties of penalized NCP-functions in derivative-free algorithms for nonlinear complementarity problems (NCPs), and give some regular conditions for stationary points of penalized NCP-functions to be solutions of NCPs. The main contribution is to unify and generalize previous results. Based on one of above penalized NCP-functions, we analyze a scaling algorithm for NCPs. The numerical results show that the scaling can greatly improve the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the modulus-based matrix splitting iteration method for solving a class of nonlinear complementarity problems under a weakened condition, and present the general convergence conditions for the method in terms of spectral radius and matrix norm, respectively. Moreover, for some special cases of the method, we propose the concrete convergence conditions and optimal parameters. These convergence theories improve the existing results to some extent. The numerical experiments verify the validity and practicality of the presented results. [ABSTRACT FROM AUTHOR]

In this paper, we establish a nonlinear complementarity model and algorithm for supply chain equilibrium management problem consisting of manufacturers, retailers and consumer markets. This work focus on the price of the goods of retailer sell to consumer market in which is a function of the amount of products that are transacted between the retailer and the consumer. Based on this, we investigate the optimizing behavior of the various decision-makers, derive the equilibrium conditions of the manufacturers, the retailers and the consumer markets respectively, and establish a nonlinear complementarity model of this problem. To obtain optimal decision for the problem, we propose a new type of algorithm based on established model, and its global convergence is presented without the assumption of global Lipschitz continuous in detail. The efficiency of given algorithm is also illustrated through some numerical examples. [ABSTRACT FROM AUTHOR]

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• A general algorithm for multisurface plasticity is presented. • A general frame to define sequential linear complementarity problem for plasticity. • The set of active constraints is naturally identified by Lemke's algorithm. • Stress is updated using the method of sequential linear complementarity problem. • The robustness of the algorithm is demonstrated via the 3D isoerror map. In this paper, a general algorithm is presented for the integration of multisurface plasticity models. The algorithm combines the return mapping with the technique of the sequential linear complementarity problem (SLCP) to update stress. The set of active constraints is naturally identified by the classical Lemke's algorithm. With the assumptions of isotropic linear elasticity, perfect plasticity, and the associated flow rule, all details are provided in matrix notations to facilitate computer implementation. The extension to hardening/softening multisurface plasticity models is also presented. The application of the algorithm is demonstrated via simulation of three types of geotechnical problems in 2D and 3D. Both linear and nonlinear multisurface plasticity models, e.g. Mohr-Coulomb and generalized Hoek-Brown yield criteria, are examined within the framework of the proposed algorithm. The numerical results are in good agreement with the analytic solutions. Moreover, the accuracy of the proposed stress integration procedure is investigated through 3D isoerror map. The convergence using the consistent tangent matrix at the global level is examined by a one-increment example consisting of one element. [ABSTRACT FROM AUTHOR]

In this paper we develop a numerical method for pricing American options under regime-switching model, whose solutions are option buyer indifference prices. The problem is formulated as a nonlinear complementarity problem. We apply interior penalty method to approximate the differential complementarity problem, which results in a system of one degenerate parabolic equation and one ordinary differential equation, weakly coupled by nonlinear exponential term. We formulate the problems in variational form in appropriate spaces and prove comparison principle. Then, as a consequence, we derive estimates about the convergence rate of the interior penalty method. We discretize the penalized problem by fully implicit finite difference scheme and prove that the early exercise constraint is strictly satisfied. We establish comparison principle, uniqueness and boundedness of the numerical solution. To solve the nonlinear system of algebraic equations we use Newton method and partial Newton method. We present numerical results that confirm the theoretical statements. To improve the computational efficiency, we unfold the two-grid idea, combining both iteration processes – Newton method and partial Newton method. We illustrate the efficiency of this approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a preconditioned general two-step modulus-based accelerated overrelaxation (MAOR) iteration method for solving a class of nonlinear complementarity problems. The convergence analysis and the condition of the iterative parameters are given when the system matrix is either positive definite or an H + -matrix. Numerical examples further illustrate that the proposed method is efficient and has better performance than some existing modulus-based iteration methods in aspects of the number of iteration steps and CPU time. [ABSTRACT FROM AUTHOR]

In this paper, we present a smoothing of a family of nonlinear complementarity functions and use its properties in combination with the smooth Jacobian strategy to present a new generalized Newton-type algorithm to solve a nonsmooth system of equations equivalent to the Nonlinear Complementarity Problem. In addition, we prove that the algorithm converges locally and q-quadratically, and analyze its numerical performance.

For a class of second-order cone nonlinear complementarity problems, abbreviated as SOCNCPs, we establish the modulus-based matrix splitting relaxation iteration methods, which are obtained by reformulating equivalently SOCNCP as an implicit fixed-point equation based on Jordan algebra associated with the second-order cone. The global convergence theorems are given under suitable choices of the involved splitting matrix and parameter matrices. When the splitting matrix is symmetric positive definite, the strategy choice of the parameters is discussed. Numerical experiments have shown that the modulus-based iteration methods are effective for solving SOCNCPs. [ABSTRACT FROM AUTHOR]

In this paper we consider the nonlinear complementarity problem over circular cones (CCNCP) which contains a lot of circular cone optimization problems. We study a one-parametric class of smoothing functions which can be used to reformulate the CCNCP as a system of smooth nonlinear equations. Based on the equivalent reformulation, we propose a smoothing inexact Newton method to solve the CCNCP. In each iteration, the proposed method solves the nonlinear equations only approximately. Since the inexact direction is not necessarily descent, a new derivative-free nonmonotone line search is developed to ensure that the proposed method has global and local superlinear and quadratical convergence. Some numerical results are also reported. [ABSTRACT FROM AUTHOR]

The natural residual (NR) function is a mapping often used to solve nonlinear complementarity problems (NCPs). Recently, three discrete-type families of complementarity functions with parameter p ⩾ 3 (where p is odd) based on the NR function were proposed. Using a neural network approach based on these families, it was observed from some preliminary numerical experiments that lower values of p provide better convergence rates. Moreover, higher values of p require larger computational time for the test problems considered. Hence, the value p = 3 is recommended for numerical simulations, which is rather unfortunate since we cannot exploit the wide range of values for the parameter p of the family of NCP functions. This paper is a follow-up study on the aforementioned results. Motivated by previously reported numerical results, we formulate a continuous-type generalization of the NR function and two corresponding symmetrizations. The new families admit a continuous parameter p > 0 , giving us a wider range of choices for p and smooth NCP functions when p > 1. Moreover, the generalization subsumes the discrete-type generalization initially proposed. The numerical simulations show that in general, increased stability and better numerical performance can be achieved by taking values of p in the interval (1 , 3). This is indeed a significant improvement of preceding studies. [ABSTRACT FROM AUTHOR]

In this paper, we present a one-layer recurrent neural network (NN) for solving convex optimization problems by using the Mangasarian and Solodov (MS) implicit Lagrangian function. In this paper by using Krush–Kuhn–Tucker conditions and MS function the NN model was derived from an unconstrained minimization problem. The proposed NN model is one layer and compared to the available NNs for solving convex optimization problems, which has a better performance in convergence time. The proposed NN model is stable in the sense of Lyapunov and globally convergent to optimal solution of the original problem. Finally, simulation results on several numerical examples are presented and the validity of the proposed NN model is demonstrated. [ABSTRACT FROM AUTHOR]

Based on the generalized Fischer–Burmeister smoothing function, we propose a regularization Newton method for solving the nonlinear complementarity problem. The proposed method views the regularization parameter as an independent variable. Moreover, it solves a perturbed Newton equation to obtain the search direction and adopts a simple nonmonotone line search scheme to improve the numerical results. Under suitable assumptions, we prove that our method has global and local quadratic convergence and the regularization parameter converges to zero globally Q-linearly. Numerical results shows that there does exist new smoothing function which is better than the Fischer–Burmeister smoothing function. [ABSTRACT FROM AUTHOR]

Numerically robust algorithmic formulations suitable for rate-independent crystal plasticity are presented. They cover classic local models as well as gradient-enhanced theories in which the gradients of the plastic slips are incorporated by means of the micromorphic approach. The elaborated algorithmic formulations rely on the underlying variational structure of (associative) crystal plasticity. To be more precise and in line with so-called variational constitutive updates or incremental energy minimization principles, an incrementally defined energy derived from the underlying time-continuous constitutive model represents the starting point of the novel numerically robust algorithmic formulations. This incrementally defined potential allows to compute all variables jointly as minimizers of this energy. While such discrete variational constitutive updates are not new in general, they are considered here in order to employ powerful techniques from non-linear constrained optimization theory in order to compute robustly the aforementioned minimizers. The analyzed prototype models are based on (1) nonlinear complementarity problem (NCP) functions as well as on (2) the augmented Lagrangian formulation. Numerical experiments show the numerical robustness of the resulting algorithmic formulations. Furthermore, it is shown that the novel algorithmic ideas can also be integrated into classic, non-variational, return-mapping schemes. • Novel algorithmic formulations for rate-independent crystal plasticity theory based on incremental energy minimization. • Algorithms can also be applied to conventional, non-variational plasticity models. • Algorithms can be applied to local as well as to gradient-enhanced plasticity models. • Algorithm guarantees a regular tangent matrix and identification of active slip systems. • Reinterpretation of active-set algorithms as nonlinear complementarity problem (NCP). [ABSTRACT FROM AUTHOR]

In this paper, by the relaxation technique, the relaxation modulus-based matrix splitting iteration method is presented for solving a class of nonlinear complementarity problems. The convergence conditions are given when the system matrix is a positive definite matrix or an H-matrix. The strategy of the choice of the relaxation parameters is discussed. Numerical examples show that the proposed method is efficient and accelerates the convergence performance of the modulus-based matrix splitting iteration method and the projection-based matrix splitting iteration method. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we establish a direct preconditioned modulus-based iteration method for solving a class of nonlinear complementarity problems with the system matrix being an H -matrix. The convergence theorems of the proposed method are given, which generalize and improve the existing ones. Numerical examples show that the proposed method is efficient. [ABSTRACT FROM AUTHOR]

This paper establishes a theoretical framework of infeasible Mehrotra-type predictor-corrector algorithm for Cartesian nonlinear complementarity problems over symmetric cones which can be regarded as an extension of Liu et al. [Polynomial convergence of Mehrotra-type predictor-corrector algorithm for the Cartesian -LCP over symmetric cones, Optimization 64(4) (2013), pp. 1-23] for Cartesian linear complementarity problems over symmetric cones to Cartesian nonlinear complementarity problems over symmetric cones. The iteration complexities of the proposed algorithm are estimated and some numerical results are provided. The numerical results show that the proposed algorithm is efficient and reliable. [ABSTRACT FROM AUTHOR]

In this paper, using the sign patterns of the solution of the equivalent modulus equation, the resolution of the nonlinear complementarity problem shrinks to find the zero of a differentiable nonlinear function. Then, a sign-based Newton's method is established by applying the Newton's iteration. The theoretical analysis for the sign patterns of the solution of the equivalent modulus equation is given under the assumption of strictly complementarity. Moreover, by using the known modulus-based matrix splitting iteration method to detect the sign patterns of the solution of the equivalent modulus equation, a practical sign-detection Newton's method is proposed. Numerical examples show that the new methods are efficient and accelerate the convergence performance with higher precision and less CPU time than the existing modulus-based matrix splitting iteration method and the projection-based matrix splitting iteration method, especially for the large sparse problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new version of the generalized damped Gauss–Newton method for solving nonlinear complementarity problems based on the transformation to the nonsmooth equation, which is equivalent to some unconstrained optimization problem. The B-differential plays the role of the derivative. We present two types of algorithms (usual and inexact), which have superlinear and global convergence for semismooth cases. These results can be applied to efficiently find all solutions of the nonlinear complementarity problems under some mild assumptions. The results of the numerical tests are attached as a complement of the theoretical considerations.