Complementarity Problem Research Articles

A Jacobian smoothing inexact Newton method for solving the nonlinear complementary problem

In this paper, we propose a Jacobian smoothing inexact Newton-type algorithm for solving the nonlinear complementarity problem by reformulating it as a system of nonlinear equations. We show that the algorithm converges up to q-quadratically and present numerical experiments that show its good local performance. In order to compare in terms of time our algorithm with other known algorithms, we introduce a new normalized measurement that we call time index.

On almost semimonotone matrices with non-positive off-diagonal elements and their generalizations

In this paper, we examine almost (strictly) semimonotone matrices which are also either Z-matrices or matrices having Property (++) and show that both parts of Wendler's conjecture [22] (2019) hold for these matrices. Another important result of this paper is the characterization of copositive matrices beyond the matrices with non-positive off-diagonal elements. We discuss some interesting properties related to the structure of almost (strictly) semimonotone matrices and also present results pertaining to the existence and multiplicity of solutions to the linear complementarity problem associated with an almost strictly semimonotone matrix.

The dominant degree for Schur complement of [formula omitted]-strictly diagonally dominant matrix and its applications

We study the dominant degree for Schur complement of S-strictly diagonally dominant matrix and its applications in reducing the order for the solution of large-scale linear systems and the linear complementarity problems. Based on the proposed dominant degree, we obtain an infinite norm bound for the inverse matrix of the Schur complement, and then derive an infinite norm bound for the original matrix. We also give the eigenvalue inclusion set for the Schur complement. In addition, we present a new error bound for the linear complementarity problem with an SB-matrix, which improves the existing results when the dominant degree is tiny. A series of numerical experiments with lots of random matrices are presented to show the efficiency and superiority of our results.

Nonlinearly preconditioned semismooth Newton algorithms for nonlinear nonsmooth systems

We aim to develop efficient and robust algorithms for nonsmooth nonlinear systems arising from complementarity problems. The semismooth Newton algorithm is popular due to its reliability and efficiency. However, it struggles with issues with imbalanced nonlinearities of the problems, leading to degraded convergence rates or failure despite help from the globalization techniques like linesearch or trust region. We introduce a right nonlinearly preconditioned semismooth Newton algorithm to address this difficulty. The critical success ingredient is that before each global Newton update, a nonlinear preconditioning step implicitly removes the so-called ‘bad components’ causing trouble via nonlinear subspace correction, inspired by Gaussian elimination but adapted nonlinearly to balance system nonlinearities. Additionally, our method integrates with a domain decomposition framework, enhancing parallelism. Numerical results on two classes of problems demonstrate significantly improved convergence over standard semismooth Newton methods.

A Methodology for Solving Fuzzy Linear Programming Problem as a Fuzzy Linear Complementarity Problem

A Methodology for Solving Fuzzy Linear Programming Problem as a Fuzzy Linear Complementarity Problem

One-parametric class of smoothing functions for (WSOCCP) and its Jacobian consistency

In this paper, we are interested in the study of weighted second-order cone complementarity problem (WSOCCP) and the smoothing function associated with it. A weighted second-order cone complementarity problem is to find a pair of vectors ( x , y ) that belong to the intersection of a manifold and a second-order cone such that the product of the vectors in a certain algebra, x ∘ y equals a given weight vector w. It is obvious to see that if w is zero, we get a second-order cone complementarity problem (SOCCP). Thus, (WSOCCP) provides the essential framework for a broader class of problems arising from real applications (science and engineering) more conveniently. This paper demonstrates the Jacobian consistency of the one-parametric class of smoothing functions. Furthermore, we provide an estimation of the distance between the subgradient of the one-parametric class of the weighted SOC complementarity functions and the gradient of its associated smoothing function. This estimation facilitates to adjust an appropriate parameter in smoothing methods.

Confidence Intervals for Piecewise Normal Distributions and Stochastic Variational Inequalities

In this paper, we first show how to obtain easy-to-compute confidence intervals for the center of a piecewise normal distribution given a sample from this distribution (assuming that the center belongs to a known affine set parallel to the common lineality space of all cones defining the piecewise normal distribution) by using certain skewed projectors on that space. We then extend this method to an asymptotic setting. Next, we apply this method to compute confidence intervals for the true solution of a stochastic variational inequality given a solution to a sample average approximation (SAA) problem for the general situation in which the asymptotic distribution of SAA solutions is piecewise normal. For stochastic complementarity problems, we obtain asymptotic normality of certain estimators of the true solution when the asymptotic distribution of the SAA solutions is piecewise normal. Funding: The research reported in this paper was supported the National Science Foundation [Grant DMS-1814894].

Projected fixed-point method for vertical tensor complementarity problems

Projected fixed-point method for vertical tensor complementarity problems

Finite Elements with Switch Detection for direct optimal control of nonsmooth systems

This paper introduces Finite Elements with Switch Detection (FESD), a numerical discretization method for nonsmooth differential equations. We consider the Filippov convexification of these systems and a transformation into dynamic complementarity systems introduced by Stewart (Numer Math 58(1):299–328, 1990). FESD is based on solving nonlinear complementarity problems and can automatically detect nonsmooth events in time. If standard time-stepping Runge–Kutta (RK) methods are naively applied to a nonsmooth ODE, the accuracy is at best of order one. In FESD, we let the integrator step size be a degree of freedom. Additional complementarity conditions, which we call cross complementarities, enable exact switch detection, hence FESD can recover the high order accuracy that the RK methods enjoy for smooth ODE. Additional conditions called step equilibration allow the step size to change only when switches occur and thus avoid spurious degrees of freedom. Convergence results for the FESD method are derived, local uniqueness of the solution and convergence of numerical sensitivities are proven. The efficacy of FESD is demonstrated in several simulation and optimal control examples. In an optimal control problem benchmark with FESD, we achieve up to five orders of magnitude more accurate solutions than a standard time-stepping approach for the same computational time.

Set-valued rigid-body dynamics for simultaneous, inelastic, frictional impacts

Robotic manipulation and locomotion often entail nearly-simultaneous collisions—such as heel and toe strikes during a foot step—with outcomes that are extremely sensitive to the order in which impacts occur. Robotic simulators and state estimation commonly lack the fidelity and accuracy to predict this ordering, and instead pick one with a heuristic. This discrepancy degrades performance when model-based controllers and policies learned in simulation are placed on a real robot. We reconcile this issue with a set-valued rigid-body model which generates a broad set of outcomes to simultaneous frictional impacts with any impact ordering. We first extend Routh’s impact model to multiple impacts by reformulating it as a differential inclusion (DI), and show that any solution will resolve all impacts in finite time. By considering time as a state, we embed this model into another DI which captures the continuous-time evolution of rigid-body dynamics, and guarantee existence of solutions. We finally cast simulation of simultaneous impacts as a linear complementarity problem (LCP), and develop an algorithm for tight approximation of the post-impact velocity set with probabilistic guarantees. We demonstrate our approach on several examples drawn from manipulation and legged locomotion, and compare the predictions to other models of rigid and compliant collisions.

A general coupled morning–evening traffic equilibrium model with rideshare, ride-hailing, and public transit services

We develop a general equilibrium model to capture the complex interactions between different modes, such as solo driving, public transit, as well as rideshare and ride-hailing services such as Uber and Lyft, under a joint morning and evening commute framework. Formulated as a variational inequality (VI) and equivalently as a mixed complementarity problem (MiCP), the model allows (a) travelers to switch between different transportation modes and (b) passengers from different Origin-Destination (OD) pairs to share a ride together. The computational results on the Sioux-Falls network show that our model captures the possible mode switches and the coupling effects between morning and evening commutes. Furthermore, our numerical examples demonstrate that modelling morning and evening commutes separately tends to overestimate the travelers' disutility and the average Vehicle Miles Traveled (VMT) in the network.

Error Bounds for Linear Complementarity Problems of Nekrasov and Generalized Nekrasov Matrices

Error Bounds for Linear Complementarity Problems of Nekrasov and Generalized Nekrasov Matrices

Coercive and Noncoercive Mixed Generalized Complementarity Problems

Impressed with the very recent developments of noncoercive complementarity problems and the use of recession sets in complementarity problems, here, we discuss mixed generalized complementarity problems in Hausdorff topological vector spaces. We used the Tikhonov regularization procedure, as well as arguments from the recession analysis, to establish the existence of solutions for mixed generalized complementarity problems without coercivity assumptions in Banach spaces.

A generalized alternating direction method of multipliers for tensor complementarity problems

A generalized alternating direction method of multipliers for tensor complementarity problems

Convergence Analysis of the Projected SOR Iteration Method for Horizontal Linear Complementarity Problems

Convergence Analysis of the Projected SOR Iteration Method for Horizontal Linear Complementarity Problems

Solving forward and inverse problems of contact mechanics using physics-informed neural networks

This paper explores the ability of physics-informed neural networks (PINNs) to solve forward and inverse problems of contact mechanics for small deformation elasticity. We deploy PINNs in a mixed-variable formulation enhanced by output transformation to enforce Dirichlet and Neumann boundary conditions as hard constraints. Inequality constraints of contact problems, namely Karush–Kuhn–Tucker (KKT) type conditions, are enforced as soft constraints by incorporating them into the loss function during network training. To formulate the loss function contribution of KKT constraints, existing approaches applied to elastoplasticity problems are investigated and we explore a nonlinear complementarity problem (NCP) function, namely Fischer–Burmeister, which possesses advantageous characteristics in terms of optimization. Based on the Hertzian contact problem, we show that PINNs can serve as pure partial differential equation (PDE) solver, as data-enhanced forward model, as inverse solver for parameter identification, and as fast-to-evaluate surrogate model. Furthermore, we demonstrate the importance of choosing proper hyperparameters, e.g. loss weights, and a combination of Adam and L-BFGS-B optimizers aiming for better results in terms of accuracy and training time.

Nonsmooth dynamic modeling and simulation of spatial mechanisms with frictional translational clearance joints

This paper presents a nonsmooth dynamic approach of modeling and computation for spatial mechanism including spatial frictional translational joints (SFTJs) with small clearances. In this research, the dynamic formulation is derived in the Lie group setting, which leads to a coordinate-free and compact formulation. In the following, the normal contact interaction between the slider and guide is described by the complementarity relations between the constraint force in the normal direction. The tangent contact interaction in the SFTJ is characterized by the Coulomb’s friction law in the type of a set-valued map. Based on the horizontal linear complementarity problem (HLCP), the non-smooth dynamics can be established and calculated by combining the Lemke’s algorithm and the RK-MK time integration algorithm. Finally, A spatial crank-slider mechanism with SFTJs is shown as a numerical case. The simulation results demonstrate the correctness and effectiveness of the proposed method which can capture the nonsmooth dynamical behavior of the system.

Nonnegative iterative reweighted method for sparse linear complementarity problem

Solution of sparse linear complementarity problem (LCP) has been widely discussed in many applications. In this paper, we consider the ℓp regularization problem with nonnegative constraint for sparse LCP, and propose algorithms based on the iterative reweighted method to approach a sparse solution of the LCP, and then show the convergence to the stationary point of ℓp regularization problem. Numerical results on simulated data exhibit an excellent performance of the proposed algorithms on approaching a sparse solution of the LCP. Finally, we apply this method to the frictional and frictionless contact problems. The numerical experiments demonstrate that the contact problems can be efficiently solved by the proposed algorithm.

Numerical investigation of the wheel-rail interaction in a stacked multibody system with multipoint frictional contact dynamics under strong earthquakes

Stacked multibody systems, exemplified by overhead cranes in nuclear power plants (OCNPP), play a crucial role in engineering applications. Under strong earthquakes, non-smooth behaviors, including detachment and stick-slip between wheels and rails, may induce uncertain motions, potentially triggering catastrophic accidents. In this study, a co-simulation method using ANSYS and MATLAB is proposed for modeling the stacked components. The wheel-rail relationship is modeled with multiple frictional unilateral constraints, which are delineated by a set-valued force law. The resultant contact-impact problem is formulated as a nonlinear complementarity problem, and then reconstructed and rescaled using a model smoothing method combined with a normalization technique to facilitate convergence. Lastly, numerical simulations are conducted, taking into account various wheel locking conditions, to explore the nonlinear behavior and stress distribution. The results illustrate the non-smooth characteristics of frictional contact within the stacked components. The presented work is expected to guide the structural optimization of stacked multibody systems.

The projected-type method for the extended vertical linear complementarity problem revisited

The projected-type method for the extended vertical linear complementarity problem revisited