In this paper, a new class of three term memory gradient method with non-monotone line search technique for unconstrained optimization is presented. Global convergence properties of the new methods are discussed. Comb...In this paper, a new class of three term memory gradient method with non-monotone line search technique for unconstrained optimization is presented. Global convergence properties of the new methods are discussed. Combining the quasi-Newton method with the new method, the former is modified to have global convergence property. Numerical results show that the new algorithm is efficient.展开更多
The non-quasi-Newton methods for unconstrained optimization was investigated. Non-monotone line search procedure is introduced, which is combined with the non-quasi-Newton family. Under the uniform convexity assumptio...The non-quasi-Newton methods for unconstrained optimization was investigated. Non-monotone line search procedure is introduced, which is combined with the non-quasi-Newton family. Under the uniform convexity assumption on objective function, the global convergence of the non-quasi-Newton family was proved. Numerical experiments showed that the non-monotone line search was more effective.展开更多
In this paper, we propose and analyze a non-monotone trust region method with non-monotone line search strategy for unconstrained optimization problems. Unlike the traditional non-monotone trust region method, our alg...In this paper, we propose and analyze a non-monotone trust region method with non-monotone line search strategy for unconstrained optimization problems. Unlike the traditional non-monotone trust region method, our algorithm utilizes non-monotone Wolfe line search to get the next point if a trial step is not adopted. Thus, it can reduce the number of solving sub-problems. Theoretical analysis shows that the new proposed method has a global convergence under some mild conditions.展开更多
In this paper,we propose a regularized version of the generalized NCPfunction proposed by Hu,Huang and Chen[J.Comput.Appl.Math.,230(2009),pp.69–82].Based on this regularized function,we propose a semismooth Newton me...In this paper,we propose a regularized version of the generalized NCPfunction proposed by Hu,Huang and Chen[J.Comput.Appl.Math.,230(2009),pp.69–82].Based on this regularized function,we propose a semismooth Newton method for solving nonlinear complementarity problems,where a non-monotone line search scheme is used.In particular,we show that the proposed non-monotone method is globally and locally superlinearly convergent under suitable assumptions.We test the proposed method by solving the test problems from MCPLIB.Numerical experiments indicate that this algorithm has better numerical performance in the case of p=5 andθ∈[0.25,075]than other cases.展开更多
文摘In this paper, a new class of three term memory gradient method with non-monotone line search technique for unconstrained optimization is presented. Global convergence properties of the new methods are discussed. Combining the quasi-Newton method with the new method, the former is modified to have global convergence property. Numerical results show that the new algorithm is efficient.
基金Sponsored by Natural Science Foundation of Beijing Municipal Commission of Education(Grant No.KM200510028019).
文摘The non-quasi-Newton methods for unconstrained optimization was investigated. Non-monotone line search procedure is introduced, which is combined with the non-quasi-Newton family. Under the uniform convexity assumption on objective function, the global convergence of the non-quasi-Newton family was proved. Numerical experiments showed that the non-monotone line search was more effective.
文摘In this paper, we propose and analyze a non-monotone trust region method with non-monotone line search strategy for unconstrained optimization problems. Unlike the traditional non-monotone trust region method, our algorithm utilizes non-monotone Wolfe line search to get the next point if a trial step is not adopted. Thus, it can reduce the number of solving sub-problems. Theoretical analysis shows that the new proposed method has a global convergence under some mild conditions.
文摘In this paper,we propose a regularized version of the generalized NCPfunction proposed by Hu,Huang and Chen[J.Comput.Appl.Math.,230(2009),pp.69–82].Based on this regularized function,we propose a semismooth Newton method for solving nonlinear complementarity problems,where a non-monotone line search scheme is used.In particular,we show that the proposed non-monotone method is globally and locally superlinearly convergent under suitable assumptions.We test the proposed method by solving the test problems from MCPLIB.Numerical experiments indicate that this algorithm has better numerical performance in the case of p=5 andθ∈[0.25,075]than other cases.
