A Quadratically Convergent Polynomial Long - Stepalgorithm for a Class of Nonlinearmonotone Complementarity Problems
نویسندگان
چکیده
Several interior point algorithms have been proposed for solving nonlinear monotone complementarity problems. Some of them have polynomial worst-case complexity but have to connne to short steps, whereas some of the others can take long steps but no polynomial complexity is proven. This paper presents an algorithm which is both long-step and polynomial. In addition, the sequence generated by the algorithm, as well as the corresponding complementarity gap, converges quadratically. The proof of the polynomial complexity requires that the monotone mapping satisses a scaled Lipschitz condition, while the quadratic rate of convergence is derived under the assumptions that the problem has a strictly complementary solution and that the Jacobian of the mapping satisses certain regularity conditions.
منابع مشابه
A Quadratically Convergent Interior-Point Algorithm for the P*(κ)-Matrix Horizontal Linear Complementarity Problem
In this paper, we present a new path-following interior-point algorithm for -horizontal linear complementarity problems (HLCPs). The algorithm uses only full-Newton steps which has the advantage that no line searchs are needed. Moreover, we obtain the currently best known iteration bound for the algorithm with small-update method, namely, , which is as good as the linear analogue.
متن کاملA Parameter-self-adjusting Levenberg-marquardt Method for Solving Nonsmooth Equations
A parameter-self-adjusting Levenberg-Marquardt method (PSA-LMM) is proposed for solving a nonlinear system of equations F (x) = 0, where F : R → R is a semismooth mapping. At each iteration, the LM parameter μk is automatically adjusted based on the ratio between actual reduction and predicted reduction. The global convergence of PSALMM for solving semismooth equations is demonstrated. Under th...
متن کاملCorrector-predictor methods for sufficient linear complementarity problems
We present a new corrector-predictor method for solving sufficient linear complementarity problems for which a sufficiently centered feasible starting point is available. In contrast with its predictor-corrector counterpart proposed by Miao, the method does not depend on the handicap κ of the problem. The method has O((1+ κ)√nL)-iteration complexity, the same as Miao’s method, but our error est...
متن کاملA Global Linear and Local Quadratic Noninterior Continuation Method for Nonlinear Complementarity Problems Based on Chen-Mangasarian Smoothing Functions
A non-interior continuation method is proposed for nonlinear complementarity problems. The method improves the non-interior continuation methods recently studied by Burke and Xu 1] and Xu 29]. Our deenition of neighborhood for the central path is simpler and more natural. In addition, our continuation method is based on a broader class of smooth functions introduced by Chen and Mangasarian 7]. ...
متن کاملA semidefinite relaxation scheme for quadratically constrained
Semidefinite optimization relaxations are among the widely used approaches to find global optimal or approximate solutions for many nonconvex problems. Here, we consider a specific quadratically constrained quadratic problem with an additional linear constraint. We prove that under certain conditions the semidefinite relaxation approach enables us to find a global optimal solution of the unde...
متن کامل