Efficient line search algorithm for unconstrained optimization

A new line search algorithm for smooth unconstrained optimization is presented that requires only one gradient evaluation with an inaccurate line search and at most two gradient evaluations with an accurate line search. It terminates in finitely many operations and shares the same theoretical properties as the standard line search rules like the Armijo-Goldstein-Wolfe-Powell rules. This algorithm is especially appropriate for the situation when gradient evaluations are very expensive relative to function evaluations.The authors would like to thank Margaret Wright and Jorge Moré for valuable comments on earlier versions of this paper.     

Adaptive nonmonotone line search method for unconstrained optimization

In this paper, an adaptive nonmonotone line search method for unconstrained minimization problems is proposed. At every iteration, the new algorithm selects only one of the two directions: a Newton-type direction and a negative curvature direction, to perform the line search. The nonmonotone technique is included in the backtracking line search when the Newton-type direction is the search direction. Furthermore, if the negative curvature direction is the search direction, we increase the steplength under certain conditions. The global convergence to a stationary point with second-order optimality conditions is established. Some numerical results which show the efficiency of the new algorithm are reported.      

On the convergence of curvilinear search algorithms in unconstrained optimization

The purpose of this paper is to unify the conditions under which curvilinear algorithms for unconstrained optimization converge. Particularly, two gradient path approximation algorithms and a trust region curvilinear algorithm are examined in this context.     

A descent algorithm without line search for unconstrained optimization

In this paper, a new descent algorithm for solving unconstrained optimization problem is presented. Its search direction is descent and line search procedure can be avoided except for the first iteration. It is globally convergent under mild conditions. The search direction of the new algorithm is generalized and convergence of corresponding algorithm is also proved. Numerical results show that the algorithm is efficient for given test problems.     

A new nonmonotone line search technique for unconstrained optimization

In this paper, we propose a new nonmonotone line search technique for unconstrained optimization problems. By using this new technique, we establish the global convergence under conditions weaker than those of the existed nonmonotone line search techniques.     

Nonmonotone trust region algorithm for unconstrained optimization problems

In this paper, a nonmonotone trust region algorithm for unconstrained optimization problems is presented. In the algorithm, a kind of nonmonotone technique, which is evidently different from Grippo, Lampariello and Lucidi’s approach, is used. Under mild conditions, global and local convergence results of the algorithm are established. Preliminary numerical results show that the new algorithm is efficient.     

A nonmonotone trust-region line search method for large-scale unconstrained optimization

We consider an efficient trust-region framework which employs a new nonmonotone line search technique for unconstrained optimization problems. Unlike the traditional nonmonotone trust-region method, our proposed algorithm avoids resolving the subproblem whenever a trial step is rejected. Instead, it performs a nonmonotone Armijo-type line search in direction of the rejected trial step to construct a new point. Theoretical analysis indicates that the new approach preserves the global convergence to the first-order critical points under classical assumptions. Moreover, superlinear and quadratic convergence are established under suitable conditions. Numerical experiments show the efficiency and effectiveness of the proposed approach for solving unconstrained optimization problems.     

A regularized Newton method without line search for unconstrained optimization

In this paper, we propose a regularized Newton method without line search. The proposed method controls a regularization parameter instead of a step size in order to guarantee the global convergence. We show that the proposed algorithm has the following convergence properties. (a) The proposed algorithm has global convergence under appropriate conditions. (b) It has superlinear rate of convergence under the local error bound condition. (c) An upper bound of the number of iterations required to obtain an approximate solution \(x\) satisfying \(\Vert \nabla f(x) \Vert \le \varepsilon \) is \(O(\varepsilon ^{-2})\) , where \(f\) is the objective function and \(\varepsilon \) is a given positive constant.     

Nonmonotone line search for minimax problems

It was recently shown that, in the solution of smooth constrained optimization problems by sequential quadratic programming (SQP), the Maratos effect can be prevented by means of a certain nonmonotone (more precisely, three-step or four-step monotone) line search. Using a well-known transformation, this scheme can be readily extended to the case of minimax problems. It turns out however that, due to the structure of these problems, one can use a simpler scheme. Such a scheme is proposed and analyzed in this paper. Numerical experiments indicate a significant advantage of the proposed line search over the Armijo search.This research was supported in part by NSF Engineering Research Centers Program No. NSFD-CDR-88-03012, by NSF Grant No. DMC-88-15996, and by a grant from the Westinghouse Corporation.     

A truncated Newton method with nonmonotone line search for unconstrained optimization

In this paper, an unconstrained minimization algorithm is defined in which a nonmonotone line search technique is employed in association with a truncated Newton algorithm. Numerical results obtained for a set of standard test problems are reported which indicate that the proposed algorithm is highly effective in the solution of illconditioned as well as of large dimensional problems.     

Combining nonmonotone conic trust region and line search techniques for unconstrained optimization

In this paper, we propose a trust region method for unconstrained optimization that can be regarded as a combination of conic model, nonmonotone and line search techniques. Unlike in traditional trust region methods, the subproblem of our algorithm is the conic minimization subproblem; moreover, our algorithm performs a nonmonotone line search to find the next iteration point when a trial step is not accepted, instead of resolving the subproblem. The global and superlinear convergence results for the algorithm are established under reasonable assumptions. Numerical results show that the new method is efficient for unconstrained optimization problems.     

Conjugate gradient path method without line search technique for derivative-free unconstrained optimization

In this paper, we propose conjugate gradient path method for solving derivative-free unconstrained optimization. The iterative direction is obtained by constructing and solving quadratic interpolation model of the objective function with conjugate gradient methods. The global convergence and local superlinear convergence rate of the proposed algorithm are established under some reasonable conditions. Finally, the numerical results are reported to show the effectiveness of the proposed algorithm.     

An inexact line search approach using modified nonmonotone strategy for unconstrained optimization

This paper concerns with a new nonmonotone strategy and its application to the line search approach for unconstrained optimization. It has been believed that nonmonotone techniques can improve the possibility of finding the global optimum and increase the convergence rate of the algorithms. We first introduce a new nonmonotone strategy which includes a convex combination of the maximum function value of some preceding successful iterates and the current function value. We then incorporate the proposed nonmonotone strategy into an inexact Armijo-type line search approach to construct a more relaxed line search procedure. The global convergence to first-order stationary points is subsequently proved and the R-linear convergence rate are established under suitable assumptions. Preliminary numerical results finally show the efficiency and the robustness of the proposed approach for solving unconstrained nonlinear optimization problems.     

An unconstrained optimization method using nonmonotone second order Goldstein's line search

In this paper,an unconstrained optimization method using the nonmonotone second order Goldstein's line search is proposed.By using the negative curvature information from the Hessian, the sequence generated is shown to converge to a stationary point with the second order optimality conditions.Numerical tests on a set of standard test problems confirm the efficiency of our new method.     

A nonmonotone trust region method with new inexact line search for unconstrained optimization

In this paper, a new nonmonotone inexact line search rule is proposed and applied to the trust region method for unconstrained optimization problems. In our line search rule, the current nonmonotone term is a convex combination of the previous nonmonotone term and the current objective function value, instead of the current objective function value . We can obtain a larger stepsize in each line search procedure and possess nonmonotonicity when incorporating the nonmonotone term into the trust region method. Unlike the traditional trust region method, the algorithm avoids resolving the subproblem if a trial step is not accepted. Under suitable conditions, global convergence is established. Numerical results show that the new method is effective for solving unconstrained optimization problems.     

A Newton-type curvilinear search method for optimization

A new search method is presented for unconstrained optimization. The method requires the evaluation of first and second derivatives and defines a curve along which a undimensional step takes place. For large step-size, the method performs as Newton's method, but it does not fail where the latter fails. For small step-size, the method behaves as the gradient method.     

Variational quasi-Newton methods for unconstrained optimization

In this paper, we propose new members of the Broyden family of quasi-Newton methods. We develop, on the basis of well-known least-change results for the BFGS and DFP updates, a measure for the Broyden family which seeks to take into account the change in both the Hessian approximation and its inverse. The proposal is then to choose the formula which gives the least value of this measure in terms of the two parameters available, and hence to produce an update which is optimal in the sense of the given measure. Several approaches to the problem of minimizing the measure are considered, from which new updates are obtained. In particular, one approach yields a new variational result for the Davidon optimally conditioned method and another yields a reasonable modification to this method. The paper is also concerned with the possibility of estimating, in a certain sense, the size of the eigenvalues of the Hessian approximation on the basis of two available scalars. This allows one to derive further modifications to the above-mentioned methods. Comparisons with the BFGS and Davidson methods are made on a set of standard test problems that show promising results for certain new methods.Part of this work was done during the author's visits at International Centre for Theoretical Physics, Trieste, Italy, at Systems Department, University of Calabria, Cosenza, Italy, and at Ajman University College of Science and Technology, Ajman, United Arab Emirates.The author expresses his gratitude to Professor L. Grandinetti for his encouragement and thanks the anonymous referees for their careful reading of an earlier draft of the paper and valuable comments, which led to a substantial improvement of the original paper.     

Parallel quasi-Newton methods for unconstrained optimization

We discuss methods for solving the unconstrained optimization problem on parallel computers, when the number of variables is sufficiently small that quasi-Newton methods can be used. We concentrate mainly, but not exclusively, on problems where function evaluation is expensive. First we discuss ways to parallelize both the function evaluation costs and the linear algebra calculations in the standard sequential secant method, the BFGS method. Then we discuss new methods that are appropriate when there are enough processors to evaluate the function, gradient, and part but not all of the Hessian at each iteration. We develop new algorithms that utilize this information and analyze their convergence properties. We present computational experiments showing that they are superior to parallelization either the BFGS methods or Newton's method under our assumptions on the number of processors and cost of function evaluation. Finally we discuss ways to effectively utilize the gradient values at unsuccessful trial points that are available in our parallel methods and also in some sequential software packages.Research supported by AFOSR grant AFOSR-85-0251, ARO contract DAAG 29-84-K-0140, NSF grants DCR-8403483 and CCR-8702403, and NSF cooperative agreement DCR-8420944.     

A nonmonotone conic trust region method based on line search for solving unconstrained optimization

In this paper, we present a nonmonotone conic trust region method based on line search technique for unconstrained optimization. The new algorithm can be regarded as a combination of nonmonotone technique, line search technique and conic trust region method. When a trial step is not accepted, the method does not resolve the trust region subproblem but generates an iterative point whose steplength satisfies some line search condition. The function value can only be allowed to increase when trial steps are not accepted in close succession of iterations. The local and global convergence properties are proved under reasonable assumptions. Numerical experiments are conducted to compare this method with the existing methods.