Nonsmooth Equations Research Articles

A Semismooth Newton Algorithm for High-Dimensional Nonconvex Sparse Learning.

The smoothly clipped absolute deviation (SCAD) and the minimax concave penalty (MCP)-penalized regression models are two important and widely used nonconvex sparse learning tools that can handle variable selection and parameter estimation simultaneously and thus have potential applications in various fields, such as mining biological data in high-throughput biomedical studies. Theoretically, these two models enjoy the oracle property even in the high-dimensional settings, where the number of predictors p may be much larger than the number of observations n . However, numerically, it is quite challenging to develop fast and stable algorithms due to their nonconvexity and nonsmoothness. In this article, we develop a fast algorithm for SCAD- and MCP-penalized learning problems. First, we show that the global minimizers of both models are roots of the nonsmooth equations. Then, a semismooth Newton (SSN) algorithm is employed to solve the equations. We prove that the SSN algorithm converges locally and superlinearly to the Karush-Kuhn-Tucker (KKT) points. The computational complexity analysis shows that the cost of the SSN algorithm per iteration is O(np) . Combined with the warm-start technique, the SSN algorithm can be very efficient and accurate. Simulation studies and a real data example suggest that our SSN algorithm, with comparable solution accuracy with the coordinate descent (CD) and the difference of convex (DC) proximal Newton algorithms, is more computationally efficient.

Periodic Orbits and Chaos in Nonsmooth Delay Differential Equations

In this paper, we present a method to find periodic solutions for certain types of nonsmooth differential equations or nonsmooth delay differential equations. We apply the method to three examples, the first is a second-order differential equation with a nonsmooth term, in this case the method allows us to find periodic orbits in a nonlinear center. The two remaining examples are first-order nonsmooth delay differential equations. In the first one, there is a stable periodic solution and in the second, the presence of a chaotic attractor was detected. In the latter, the method allows us to obtain unstable periodic orbits within the attractor. For large values of the delay, both examples can be seen as singularly perturbed delay differential equations. For them, an analysis is performed with an associated discrete map which is obtained in the limit of large delays.

A Class of Derivative-Free CG Projection Methods for Nonsmooth Equations with an Application to the LASSO Problem

In this paper, based on a modified Gram–Schmidt (MGS) process, we propose a class of derivative-free conjugate gradient (CG) projection methods for nonsmooth equations with convex constraints. Two attractive features of the new class of methods are (1) its generated direction contains a free vector, which can be set as any vector such that the denominator of the direction does not equal to zero; (2) it adopts a new line search which can reduce its computing cost. The new class of methods includes many efficient iterative methods for the studied problem as its special cases. When the underlying mapping is monotone, we establish its global convergence and convergence rate. Finally, preliminary numerical results about the LASSO problem show that the new class of methods is promising compared to some existing ones.

On a new exponential iterative method for solving nonsmooth equations

SummaryIn this paper, we propose a new distinctive version of a generalized Newton method for solving nonsmooth equations. The iterative formula is not the classic Newton type, but an exponential one. Moreover, it uses matrices from B‐differential instead of generalized Jacobian. We prove local convergence of the method and we present some numerical examples.

Distributed Real-Time Pricing Method Incorporating Load Uncertainty Based on Nonsmooth Equations for Smart Grid

The real-time pricing mechanism of smart grid based on demand response is an effective means to adjust the balance between energy supply and demand, whose implementation will impact the user's electricity consumption behaviour, the operation, and management in the future power systems. In this paper, we propose a complementarity algorithm to solve the real-time pricing of smart grid. The Karush–Kuhn–Tucker condition is considered in the social welfare maximisation model incorporating load uncertainty to transforming the model into a system of nonsmooth equations with Lagrangian multipliers, i.e., the shadow prices. The shadow price is used to determine the basic price of electricity. The system of nonsmooth equations is a complementarity problem, which enables us to study the existence and uniqueness of the equilibrium price and to design an online distributed algorithm to achieve the equilibrium between energy supply and demand. The proposed method is implemented in a simulation system composed of an energy provider and 100 users. Simulations results show that the proposed algorithm can motivate the users’ enthusiasm to participate in the demand side management and shift the peak loading. Furthermore, the proposed algorithm can improve the supply shortage. When compared with an online distributed algorithm based on the dual optimisation method, the proposed algorithm has a significantly lower running time and more accurate Lagrangian multipliers.

A new filter QP-free method for the nonlinear inequality constrained optimization problem

In this paper, a filter QP-free infeasible method with nonmonotone line search is proposed for minimizing a smooth optimization problem with smooth inequality constraints. This proposed method is based on the solution of nonsmooth equations, which are obtained by the Lagrangian multiplier method and the function of the nonlinear complementarity problem for the Karush–Kuhn–Tucker optimality conditions. Especially, each iteration of this method can be viewed as a perturbation of a Newton or quasi-Newton iteration on both the primal and dual variables for the solution of the Karush–Kuhn–Tucker optimality conditions. What is more, it is considered to use the function of the nonlinear complementarity problem in the filter, which makes the proposed algorithm avoid the incompatibility. Then the global convergence of the proposed method is given. And under some mild conditions, the superlinear convergence rate can be obtained. Finally, some preliminary numerical results are shown to illustrate that the proposed filter QP-free infeasible method is quite promising.

On Lifting Operators and Regularity of Nonsmooth Newton Methods for Optimal Control Problems of Differential Algebraic Equations

This paper focuses on nonsmooth Newton methods of optimal control problems governed by mixed control–state constraints with differential algebraic equations. In contrast to previous results, we analyze lifting operators involved in nonsmooth Newton methods and establish corresponding convergence results. We also give sufficient conditions for regularity of generalized derivatives of systems of nonsmooth operator equations associated with optimal control problems.

A Nonmonotone Trust Region Method for Nonsmooth Composite Programming Problems

A class of nonsmooth composite minimization problems are considered in this paper. In practice, many problems in computational science, engineering and other enormous areas fall into this class of problems. Two obvious applications of this class of problems are least square problems with nonsmooth data and the problem of solving a system of nonsmooth equations. Because of its practical importance, this class of problem has received wide attention from the mathematical optimization community. In particular, Sampaio, Yuan and Sun has proposed a trust region method for this class of problems and also provided the convergence analysis to support their algorithm. Motivated partly by their work, in this paper a nonmonotone trust region algorithm for this class of nonsmooth composite minimization problems is presented. Different from most existing monotone line search and trust region methods, this method combines the nonmonotone technique to improve the efficiency of the trust region method. After a brief introduction of the class of problems in the first section, some fundamental concepts and properties which will be used in this paper are presented. Then, the new nonmonotone trust region algorithm for the class of problem is described followed by the global convergence analysis of the new algorithm. A simple application of this algorithm is discussed in the last part of this paper.

A Kind of Stochastic Eigenvalue Complementarity Problems

With the development of computer science, computational electromagnetics have also been widely used. Electromagnetic phenomena are closely related to eigenvalue problems. On the other hand, in order to solve the uncertainty of input data, the stochastic eigenvalue complementarity problem, which is a general formulation for the eigenvalue complementarity problem, has aroused interest in research. So, in this paper, we propose a new kind of stochastic eigenvalue complementarity problem. We reformulate the given stochastic eigenvalue complementarity problem as a system of nonsmooth equations with nonnegative constraints. Then, a projected smoothing Newton method is presented to solve it. The global and local convergence properties of the given method for solving the proposed stochastic eigenvalue complementarity problem are also given. Finally, the related numerical results show that the proposed method is efficient.

Generalized Sensitivity Analysis of Nonlinear Programs

This paper extends classical sensitivity results for nonlinear programs to cases in which parametric perturbations cause changes in the active set. This is accomplished using lexicographic directional derivatives, a recently developed tool in nonsmooth analysis based on Nesterov's lexicographic differentiation. A nonsmooth implicit function theorem is augmented with generalized derivative information and applied to a standard nonsmooth reformulation of the parametric KKT system. It is shown that the sufficient conditions for this implicit function theorem variant are implied by a KKT point satisfying the linear independence constraint qualification and strong second-order sufficiency. Mirroring the classical theory, the resulting sensitivity system is a nonsmooth equation system which admits primal and dual sensitivities as its unique solution. Practically implementable algorithms are provided for calculating the nonsmooth sensitivity system's unique solution, which is then used to furnish B-subdifferenti...

Studies on Static Frictional Contact Problems of Double Cantilever Beam Based on SBFEM

Introduction: The frictional contact problem is one of the most important and challenging topics in solids mechanics, and often encountered in the practical engineering. Method: The nonlinearity and non-smooth properties result in that the convergent solutions can't be obtained by the widely used trial-error iteration method. Mathematical Programming which has good convergence properties and rigorous mathematical foundation is an effective alternative solution method, in which, the frictional contact conditions can be expressed as Non-smooth Equations, B-differential equations, Nonlinear Complementary Problem, etc. Result: In this paper, static frictional contact problems of double cantilever beam are analyzed by Mathematical Programming in the framework of Scaled Boundary Finite Element Method (SBFEM), in which the contact conditions can be expressed as the B-differential Equations. Conclusion The contact forces and the deformation with different friction factors are solved and compared with those obtained by ANSYS, by which the accuracy of solving contact problems by SBFEM and B-differential Equations is validated.

A globally convergent LP-Newton method for piecewise smooth constrained equations: escaping nonstationary accumulation points

The LP-Newton method for constrained equations, introduced some years ago, has powerful properties of local superlinear convergence, covering both possibly nonisolated solutions and possibly nonsmooth equation mappings. A related globally convergent algorithm, based on the LP-Newton subproblems and linesearch for the equation’s infinity norm residual, has recently been developed. In the case of smooth equations, global convergence of this algorithm to B-stationary points of the residual over the constraint set has been shown, which is a natural result: nothing better should generally be expected in variational settings. However, for the piecewise smooth case only a property weaker than B-stationarity could be guaranteed. In this paper, we develop a procedure for piecewise smooth equations that avoids undesirable accumulation points, thus achieving the intended property of B-stationarity.

Approximations and generalized Newton methods

We present approaches to (generalized) Newton methods in the framework of generalized equations $$0\in f(x)+M(x)$$ , where f is a function and M is a multifunction. The Newton steps are defined by approximations $${\hat{f}}$$ of f and the solutions of $$0\in {\hat{f}}(x)+M(x)$$ . We give a unified view of the local convergence analysis of such methods by connecting a certain type of approximation with the desired kind of convergence and different regularity conditions for $$f+M$$ . Our paper is, on the one hand, thought as a survey of crucial parts of the topic, where we mainly use concepts and results of the monograph (Klatte and Kummer in Nonsmooth equations in optimization: regularity, calculus, methods and applications, Kluwer Academic Publishers, Dordrecht, 2002). On the other hand, we present original results and new features. They concern the extension of convergence results via Newton maps (Klatte and Kummer in Nonsmooth equations in optimization: regularity, calculus, methods and applications, Kluwer Academic Publishers, Dordrecht, 2002; Kummer, in: Oettli, Pallaschke (eds) Advances in optimization, Springer, Berlin, 1992) from equations to generalized equations both for linear and nonlinear approximations $${\hat{f}}$$ , and relations between semi-smoothness, Newton maps and directional differentiability of f. We give a Kantorovich-type statement, valid for all sequences of Newton iterates under metric regularity, and recall and extend results on multivalued approximations for general inclusions $$0\in F(x)$$ . Equations with continuous, non-Lipschitzian f are considered, too.

An adaptive trust region algorithm for large-residual nonsmooth least squares problems

In this paper, an adaptive trust region algorithm in which the trust region radius converges to zero is presented for solving large-residual nonsmooth least squares problems. This algorithm uses the smoothing technique of the approximation function, and it combines an adaptive trust region radius. Moreover, this algorithm differs from the existing methods for solving nonsmooth equations through use of the approximation function of second-order information, which improves the convergence rate for large-residual nonsmooth least squares problems. Under some suitable conditions, the global and local superlinear convergences of the proposed method are proven. The preliminary numerical results indicate that the proposed algorithm is effective and suitable for solving large-residual nonsmooth least squares problems.

Solving nearly-separable quadratic optimization problems as nonsmooth equations

An algorithm for solving nearly-separable quadratic optimization problems (QPs) is presented. The approach is based on applying a semismooth Newton method to solve the implicit complementarity problem arising as the first-order stationarity conditions of such a QP. An important feature of the approach is that, as in dual decomposition methods, separability of the dual function of the QP can be exploited in the search direction computation. Global convergence of the method is promoted by enforcing decrease in component(s) of a Fischer---Burmeister formulation of the complementarity conditions, either via a merit function or through a filter mechanism. The results of numerical experiments when solving convex and nonconvex instances are provided to illustrate the efficacy of the method.

Numerical comparisons based on four smoothing functions for absolute value equation

The system of absolute value equation, denoted by AVE, is a non-differentiable NP-hard problem. Many approaches have been proposed during the past decade and most of them focus on reformulating it as complementarity problem and then solve it accordingly. Another approach is to recast the AVE as a system of nonsmooth equations and then tackle with the nonsmooth equations. In this paper, we follow this path. In particular, we rewrite it as a system of smooth equations and propose four new smoothing functions along with a smoothing-type algorithm to solve the system of equations. The main contribution of this paper focuses on numerical comparisons which suggest a better choice of smoothing function along with the smoothing-type algorithm.

Modeling phase changes in multistream heat exchangers

A new method for modeling phase changes in multistream heat exchangers (MHEXs) is presented. In many industrially relevant applications, streams in MHEXs will undergo phase changes between their inlet and outlet. In this model, nonsmooth equations are formulated which properly account for the existence or nonexistence of phases in heat integration, flash and physical property calculations in a MHEX. These new equations are used in conjunction with a recently developed nonsmooth model for MHEXs to create a compact equation system which can be used for the simulation and design of complex processes. Notably, this formulation does not involve the solution of a difficult optimization problem, since it avoids the use of either disjunctive or complementarity constraints. The robustness and functionality of the new formulation is illustrated through several simulations of the well-known Poly Refrigerant Integrated Cycle Operations (PRICO) process for liquefied natural gas production.

Newton-type Methods for Solving the Nonsmooth Equations with Finitely Many Maximum Functions

Newton-type Methods for Solving the Nonsmooth Equations with Finitely Many Maximum Functions

Simulation and Design Methods for Multiphase Multistream Heat Exchangers

Multistream heat exchangers (MHEXs) are found in many energy intensive and industrially relevant cryogenic processes. However, design and optimization of such processes is rendered difficult by the inability to simulate MHEXs robustly as most current flowsheet-level MHEX models solve only an energy balance with no constraint on second law feasibility The new model described herein combines an extension of the classical pinch analysis algorithm with explicit dependence on the heat exchange area to formulate a nonsmooth equation system which can be solved for up to three unknown variables in an MHEX. The resulting model is further augmented to simulate realistic thermodynamic phenomena, such as phase change and equilibrium, which are also naturally described by additional nonsmooth equations.

Non-smooth singular Newton's method for positive semidefinite solution of nonlinear matrix equations

In this paper, we propose a new method for solving conic constrained nonlinear matrix equations. With the use of the orthogonal projection onto the positive semidefinite cone of matrices, the conic constrained equation is transformed to a non-smooth unconstrained equation which is solved by the non-smooth Newton's method. Here, we use an explicit expression of the Clarke generalised Jacobian of the projection onto the cone of positive semidefinite matrices as developed by several authors. We prove under natural assumptions that the method converges locally and superlinearly.