A derivative-free algorithm for systems of nonlinear inequalities

Recently a new derivative-free algorithm has been proposed for the solution of linearly constrained finite minimax problems. This derivative-free algorithm is based on a smoothing technique that allows one to take into account the non-smoothness of the max function. In this paper, we investigate, both from a theoretical and computational point of view, the behavior of the minmax algorithm when used to solve systems of nonlinear inequalities when derivatives are unavailable. In particular, we show an interesting property of the algorithm, namely, under some mild conditions regarding the regularity of the functions defining the system, it is possible to prove that the algorithm locates a solution of the problem after a finite number of iterations. Furthermore, under a weaker regularity condition, it is possible to show that an accumulation point of the sequence generated by the algorithm exists which is a solution of the system. Moreover, we carried out numerical experimentation and comparison of the method against a standard pattern search minimization method. The obtained results confirm that the good theoretical properties of the method correspond to interesting numerical performance. Moreover, the algorithm compares favorably with a standard derivative-free method, and this seems to indicate that extending the smoothing technique to pattern search algorithms can be beneficial.     

A new inexact SQP algorithm for nonlinear systems of mixed equalities and inequalities

Traditional inexact SQP algorithm can only solve equality constrained optimization (Byrd et al. Math. Program. 122, 273–299 2010). In this paper, we propose a new inexact SQP algorithm with affine scaling technique for nonlinear systems of mixed equalities and inequalities, which arise in complementarity and variational inequalities. The nonlinear systems are transformed into a special nonlinear optimization with equality and bound constraints, and then we give a new inexact SQP algorithm for solving it. The new algorithm equipped with affine scaling technique does not require a quadratic programming subproblem with inequality constraints. The search direction is computed by solving one linear system approximately using iterative linear algebra techniques. Under mild assumptions, we discuss the global convergence. The preliminary numerical results show the effectiveness of the proposed algorithm.     

A quadratically convergent algorithm for solving infinite dimensional inequalities

Many design algorithms can be formulated as determining a set of parameters to satisfy conventional and infinite dimensional constraints. An algorithm, with quadratic rate of convergence, for solving such inequalities, is presented.Research supported by the United Kingdom Science Research Council and the National Science Foundation ECS-79-13148.     

A new iterative algorithm for a class of nonlinear mixed implicit variational inequalities

In this paper, we develop a new iterative algorithm for solving a class of nonlinear mixed implicit variational inequalities and give the convergence analysis of the iterative sequences generated by the algorithms. In our results, we do not assume that the mapping is strongly monotone, nor do we assume that the mapping is surjective.     

Lyapunov-type inequalities for nonlinear systems

In this paper, by using elementary analysis, we establish some new Lyapunov-type inequalities for nonlinear systems of differential equations, special cases of which contain the well-known equations such as Emden-Fowler-type and half-linear equations. The inequalities obtained here can be used as handy tools in the study of qualitative behaviour of solutions of the associated equations.     

A finitely convergent procedure for facial disjunctive programs

This paper addresses an important class of disjunctive programs called facial disjunctive programs, examples of which include the zero-one linear integer programming problem and the linear complementarity problem. Balas has characterized some fundamental properties of such problems, one of which has been used by Jeroslow to obtain a finitely convergent procedure. This paper exploits another basic property of facial disjunctive programs in order to develop an alternative finitely convergent algorithm.     

Globally convergent algorithm for solving nonlinear equations

A general iterative method is proposed for finding the maximal rootx _max of a one-variable equation in a given interval. The method generates a monotone-decreasing sequence of points converging tox _max or demonstrates the nonexistence of a real root. It is globally convergent. A concrete realization of the general algorithm is also given and is shown to be locally quadratically convergent. Computational experience obtained for eight test problems indicates that the new method is comparable to known methods claiming global convergence.     

A globally convergent interior point algorithm for non-convex nonlinear programming

In this paper, a new algorithm for tracing the combined homotopy path of the non-convex nonlinear programming problem is proposed. The algorithm is based on the techniques of β$\beta$-cone neighborhood and a combined homotopy interior point method. The residual control criteria, which ensures that the obtained iterative points are interior points, is given by the condition that ensures the β$\beta$-cone neighborhood to be included in the interior part of the feasible region. The global convergence and polynomial complexity are established under some hypotheses.     

A finitely convergent algorithm for bilinear programming problems using polar cuts and disjunctive face cuts

A finite algorithm is presented in this study for solving Bilinear programs. This is accomplished by developing a suitable cutting plane which deletes at least a face of a polyhedral set. At an extreme point, a polar cut using negative edge extensions is used. At other points, disjunctive cuts are adopted. Computational experience on test problems in the literature is provided.This paper is based upon work supported by the National Science Foundation under Grant No. ENG-77-23683.     

Exponentially convergent parallel algorithm for nonlinear eigenvalue problems

A. V. Klimenko, V. L. Makarov ^{A new algorithm for nonlinear eigenvalue problems is proposed.The numerical technique is based on a perturbation of the coefficientsof differential equation combined with the Adomian decompositionmethod for the nonlinear part. The approach provides an exponentialconvergence rate with a base which is inversely proportionalto the index of the eigenvalue under consideration. The eigenpairscan be computed in parallel. Numerical examples are presentedto support the theory. They are in good agreement with the spectralasymptotics obtained by other authors.}     

An inexact secant algorithm for large scale nonlinear systems of equalities and inequalities

In this paper, an inexact secant algorithm in association with nonmonotone technique and filter is proposed for solving the large scale nonlinear systems of equalities and inequalities. The systems are transformed into a continuous constrained optimization solved by inexact secant algorithm. Global convergence of the proposed algorithm is established under the reasonable conditions. Numerical results validate the effectiveness of our approach.     

A superlinearly convergent ODE-type trust region algorithm for nonsmooth nonlinear equations

This paper presents a new trust region algorithm for solving nonsmooth nonlinear equation problems which posses the smooth plus non-smooth decomposition. At each iteration, this method obtains a trial step by solving a system of linear equations, hence avoiding the need for solving a quadratic programming subproblem with a trust region bound. From a computational point of view, this approach may reduce computational effort and hence improve computational efficiency. Furthermore, it is proved under appropriate assumptions that this algorithm is globally and locally super-linearly convergent. Some numerical examples are reported.     

A superlinearly convergent projection method for constrained systems of nonlinear equations

In this paper, a new projection method for solving a system of nonlinear equations with convex constraints is presented. Compared with the existing projection method for solving the problem, the projection region in this new algorithm is modified which makes an optimal stepsize available at each iteration and hence guarantees that the next iterate is more closer to the solution set. Under mild conditions, we show that the method is globally convergent, and if an error bound assumption holds in addition, it is shown to be superlinearly convergent. Preliminary numerical experiments also show that this method is more efficient and promising than the existing projection method. This work was done when Yiju Wang visited Chongqing Normal University.     

A new hybrid iterative algorithm for variational inequalities

Let H be a real Hilbert space. Let F:H→H be a strongly monotone and Lipschitzian mapping. Let be an infinite family of non-expansive mappings with common fixed points set . We devise an iterative algorithm

     

A new double projection algorithm for variational inequalities

We present a modification of a double projection algorithm proposed by Solodov and Svaiter for solving variational inequalities. The main modification is to use a different Armijo-type linesearch to obtain a hyperplane strictly separating current iterate from the solutions of the variational inequalities. Our method is proven to be globally convergent under very mild assumptions. If in addition a certain error bound holds, we analyze the convergence rate of the iterative sequence. We use numerical experiments to compare our method with that proposed by Solodov and Svaiter.     

A smoothing self-adaptive Levenberg-Marquardt algorithm for solving system of nonlinear inequalities

In this paper, we consider the smoothing self-adaptive Levenberg-Marquardt algorithm for the system of nonlinear inequalities. By constructing a new smoothing function, the problem is approximated via a family of parameterized smooth equations H(x) = 0. A smoothing self-adaptive Levenberg-Marquardt algorithm is proposed for solving the system of nonlinear inequalities based on the new smoothing function. The Levenberg-Marquardt parameter μ_k is chosen as the product of μ_k = ∥H^k∥^δ with δ ∈ (0, 2] being a positive constant. We will show that if ∥H^k∥^δ provides a local error bound, which is weaker than the non-singularity, the proposed method converges superlinearly to the solution for δ ∈ (0, 1), while quadratically for δ ∈ [1, 2]. Numerical results show that the new method performs very well for system of inequalities.     

Monotonically convergent iterative methods for nonlinear systems of equations

Summary This paper deals with discrete analogues of nonlinear elliptic boundary value problems and with monotonically convergent iterative methods for their numerical solution. The discrete analogues can be written asM(u)u+H(u)=0, whereM(u) is ann%n M-matrix for eachu ⁿ andH: ^{n n} . The numerical methods considered are the natural undeerrelaxation method, the successive underrelaxation method, and the Jacobi underrelaxation method. In the linear case and without underrelaxation these methods correspond to the direct, the Gauss-Seidel, and the Jacobi method for solving the underlying system of equations, resp. For suitable starting vectors and sufficiently strong underrelaxation, the sequence of iterates generated by any of these methods is shown to converge monotonically to a solution of the underlying system.     

A constructive algorithm for folding large-scale systems of linear inequalities

The conventional procedure for folding a system of linear inequalities based on the Fourier-Chernikov algorithm is supplemented with techniques for eliminating redundant inequalities, which considerably counteracts the increase in the system dimension. Exact and approximate methods are proposed, which are brought to algorithmic form and software implementation. Numerical results are discussed.