FINDING THE STRICTLY LOCAL AND ε-GLOBAL MINIMIZERS OF CONCAVE MINIMIZATION WITH LINEAR CONSTRAINTS

This paper considers the concave minimization problem with linear constrailits,proposes a technique which may avoid the unsuitable Karush-Kuhn-Tucker poiats,then combines this technique with nank-Wolfe method and simplex method to form a pivoting method which can determine a strictly local minimizer of the problem in a finite number of iterations. Basing on strictly local minimizers, a new cutting plane method is proposed. Under some mild conditions, the new cutting plane method is proved to be finitely terminated at an θ-global minimizer of the problem.

A Filled Function with Adjustable Parameters for Unconstrained Global Optimization

A filled function with adjustable parameters is suggested in this paper for finding a global minimum point of a general class of nonlinear programming problems with a bounded and closed domain. This function has two adjustable parameters. We will discuss the properties of the proposed filled function. Conditions on this function and on the values of parameters are given so that the constructed function has the desired properties of traditional filled function.

Interval Algorithm for a Kind of Nonsmooth Global Optimization

Based on the interval analysis,a practical interval algorithm is developed for finding all global minimizers of a nonsmooth function on a closed domain XR n, which is given by defining a special derivative to the function and using the interval inclusion of derivative. Both theoretical analysis and numerical results show that this method is practical and effective.

Integral Global Minimization of Constrained Problems with Discontinuous Penalty Functions

A class of discontinuous penalty functions was proposed to solve constrained minimization problems with the integral approach to global optimization, m-mean value and v-variance optimality conditions of a constrained and penalized minimization problem were investigated. A nonsequential algorithm was proposed. Numerical examples were given to illustrate the effectiveness of the algorithm.

A Global Minimization Algorithm for Empirical Contact Potential Functions

Global minimization algorithm is indispensable for solving protein folding problems based on thermodynamic hypothesis. A contact difference （CD） based on pseudo potential function, for simulating empirical contact potential functions and testing global minimization algorithm was proposed. The present article describes a conformational sampiing and global minimization algorithm, which is called WL, based on Monte Carlo simulation and simulated annealing. It can be used to locate CD＇s globe minimum and refold extended protein structures, as small as 0. 03 nm, from the native structures, back to ones with root mean square distance（RMSD）. These results demonstrate that the global minimization problems for empirical contact potential functions may be solvable.

A Global Minimization Algorithm for Empirical Contact Potential Functions

Global minimization algorithm is indispensable to solving the protein folding problem based upon thermodynamic hypothesis. Here we propose a pseudo potential function, contact difference(CD), for simulating empirical contact potential functions and testing global minimization algorithm. The present paper covers conformational sampling and global minimization algorithm called BML03, based upon Monte Carlo and simulated annealing, which is able to locate CD′s global minimum and refold extended protein structures into ones with root mean square distance(RMSD) as small as 0.03 nm from the native structures. For empirical contact potential functions, these results demonstrate that their global minimization problems may be solvable.

Two-parameters quasi-filled function algorithm for nonlinear integer programming

A quasi-filled function for nonlinear integer programming problem is given in this paper. This function contains two parameters which are easily to be chosen. Theoretical properties of the proposed quasi-filled function are investigated. Moreover, we also propose a new solution algorithm using this quasi-filled function to solve nonlinear integer programming problem in this paper. The examples with 2 to 6 variables are tested and computational results indicated the efficiency and reliability of the pro- posed quasi-filled function algorithm.

One-parameter quasi-filled function algorithm for nonlinear integer programming

A definition of the quasi-filled function for nonlinear integer programming problem is given in this paper. A quasi-filled function satisfying our definition is presented. This function contains only one parameter. The properties of the pro- posed quasi-filled function and the method using this quasi-filled function to solve nonlinear integer programming problem are also discussed in this paper. Numerical results indicated the efficiency and reliability of the proposed quasi-filled function algo- rithm.

A One-parameter Filled Function Method for Nonlinear Integer Programming

This paper gives a new definition of the filled function for nonlinear integer programming problem. A filled function satisfying our definition is presented. This function contains only one parameter. The properties of the proposed filled function and the method using this filled function to solve nonlinear integer programming problem are also discussed. Numerical results indicate the efficiency and reliability of the proposed filled function algorithm.

A DERIVATIVE-FREE ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the search step of pattern search algorithm,the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication,crossover and mutation, a finite set of points can be used. In theory,the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems ,which other pattern search algorithms don＇t bear.

EXACT AUGMENTED LAGRANGIAN FUNCTION FOR NONLINEAR PROGRAMMING PROBLEMS WITH INEQUALITY CONSTRAINTS

An exact augmented Lagrangian function for the nonlinear nonconvex programming problems with inequality constraints was discussed. Under suitable hypotheses, the relationship was established between the local unconstrained minimizers of the augmented Lagrangian function on the space of problem variables and the local minimizers of the original constrained problem. Furthermore, under some assumptions, the relationship was also established between the global solutions of the augmented Lagrangian function on some compact subset of the space of problem variables and the global solutions of the constrained problem. Therefore, f^om the theoretical point of view, a solution of the inequality constrained problem and the corresponding values of the Lagrange multipliers can be found by the well-known method of multipliers which resort to the unconstrained minimization of the augmented Lagrangian function presented.

A ONE-PARAMETER FILLED FUNCTION FOR NONSMOOTH GLOBAL OPTIMIZATION AND ITS APPLICATION

This paper presents a modified definition of the filled function for finding a global minimizer of a nonsmooth function on a closed bounded set, and then give a one-parameter filled function. Theoretical and numerical properties of the proposed filled function are investigated and a corresponding solution algorithm is proposed. The proposed filled function＇s parameter is easier to be appropriately chosen than previous functions in literatures. Numerical results obtained indicate the efficiency of the proposed filled function method. An improved fingerprint recognition method using global filled function is also reported.

NONLINEAR INTEGER PROGRAMMING AND GLOBALOPTIMIZATION

Various approaches have been developed for solving a variety of continuous global optimization problems. But up to now, less work has been devoted to solving nonlinear integer programming problems due to the inherent difficulty. This paper manages to transform the general nonlinear integer programming problem into an equivalent' special continuous global minimization problem. Thus any effective global optimization algorithm can be used to solve nonlinear integer programming problems. This result will also promote the research on global optimization. We present an interval Branch-and-Bound algorithm. Numerical experiments show that this approach is efficient. (Author abstract) 11 Refs.

ROBUST ANALYSIS AND GLOBAL MINIMIZATION OF A CLASS OF DISCONTINUOUS FUNCTIONS (Ⅱ)

In this Paper we continue to investigate global minimization problems. An integral approach is applied to treat a global minimization problem of a discontinuous function. With the help ofthe theory of measure (Q-measure) and integration, optimality conditions of a robust function over arobust set are derived. Algorithms and their implementations for finding global minima are proposed.Numerical tests and applications show that the algorithms are effective.

ON MAXIMA OF DUAL FUNCTION OF THE CDT SUBPROBLEM

Focuses on a study which determined the geometry meaning of the maxima of the CDT mathematical subproblem's dual function. Properties of trust region subproblem; Approximation of the CDT feasible region; Relations between the CDT problem and the trust region problem; Illustration of the geometry meaning of the jump parameter.

On Ginzburg-Landau Vortices of Superconducting Thin Films

In this paper, we discuss the vortex structure of the superconducting thin films placed in a magnetic field. We show that the global minimizer of the functional modelling the superconducting thin films has a bounded number of vortices when the applied magnetic field hex 〈 Hc1 ＋ K log ｜ logε｜ where Hc1 is the lower critical field of the film obtained by Ding and Du in SIAM J. Math. Anal., 2002. The locations of the vortices are also given.

SNIG PROPERTY OF MATRIX LOW-RANK FACTORIZATION MODEL

Recently, the matrix factorization model attracts increasing attentions in handling large-scale rank minimization problems, which is essentially a nonconvex minimization problem. Specifically, it is a quadratic least squares problem and consequently a quartic polynomial optimization problem. In this paper, we introduce a concept of the SNIG （＂Second-order Necessary optimality Implies Global optimality＂） condition which stands for the property that any second-order stationary point of the matrix factorization model must be a global minimizer. Some scenarios under which the SNIG condition holds are presented. Furthermore, we illustrate by an example when the SNIG condition may fail.

A NEW FILLED FUNCTION METHOD FOR INTEGER PROGRAMMING

The Filled Function Method is a class of effective algorithms for continuous global optimization. In this paper, a new filled function method is introduced and used to solve integer programming. Firstly, some basic definitions of discrete optimization are given. Then an algorithm and the implementation of this algorithm on several test problems are showed. The computational results show the algorithm is effective.