In this paper,we investigate three canonical forms of interval convex quadratic pro-gramming problems.Necessary and suficient conditions for checking weak and strong optimality of given vector corresponding to various...In this paper,we investigate three canonical forms of interval convex quadratic pro-gramming problems.Necessary and suficient conditions for checking weak and strong optimality of given vector corresponding to various forms of feasible region,are established respectively.By using the concept of feasible direction,these conditions are formulated in the form of linear systems with both equations and inequalities.In addition,we provide two specific examples to illustrate the efficiency of the conditions.展开更多
In this paper, a new superlinearly convergent algorithm for nonlinearly constrained optimization problems is presented. The search directions are directly computed by a few formulas, and neither quadratic programming ...In this paper, a new superlinearly convergent algorithm for nonlinearly constrained optimization problems is presented. The search directions are directly computed by a few formulas, and neither quadratic programming nor linear equation need to be sovled. Under mild assumptions, the new algorithm is shown to possess global and superlinear convergence.展开更多
This paper concerns the solution of the NP-hard max-bisection problems. NCP functions are employed to convert max-bisection problems into continuous nonlinear programming problems. Solving the resulting continuous non...This paper concerns the solution of the NP-hard max-bisection problems. NCP functions are employed to convert max-bisection problems into continuous nonlinear programming problems. Solving the resulting continuous nonlinear programming problem generates a solution that gives an upper bound on the optimal value of the max-bisection problem. From the solution, the greedy strategy is used to generate a satisfactory approximate solution of the max-bisection problem. A feasible direction method without line searches is proposed to solve the resulting continuous nonlinear programming, and the convergence of the algorithm to KKT point of the resulting problem is proved. Numerical experiments and comparisons on well-known test problems, and on randomly generated test problems show that the proposed method is robust, and very efficient.展开更多
In this paper, two alternative theorems which differ from Theorem 10.2.6 in [1] and Theorem 1 in [3] are presented for a class of feasible direction algorithms. On the basis of alternative theorems, furthermore, two s...In this paper, two alternative theorems which differ from Theorem 10.2.6 in [1] and Theorem 1 in [3] are presented for a class of feasible direction algorithms. On the basis of alternative theorems, furthermore, two sufficient conditions of global convergence of this class of algorithms are obtained.展开更多
Foreign investors wishing to establish a joint venture with a Chinese partner in China are requested to submit a feasibility study report to the department in charge as done abroad. The article given below describes h...Foreign investors wishing to establish a joint venture with a Chinese partner in China are requested to submit a feasibility study report to the department in charge as done abroad. The article given below describes how to write a feasibility study report according to the Chinese practical situation.展开更多
An effective continuous algorithm is proposed to find approximate solutions of NP-hard max-cut problems. The algorithm relaxes the max-cut problem into a continuous nonlinear programming problem by replacing n discret...An effective continuous algorithm is proposed to find approximate solutions of NP-hard max-cut problems. The algorithm relaxes the max-cut problem into a continuous nonlinear programming problem by replacing n discrete constraints in the original problem with one single continuous constraint. A feasible direction method is designed to solve the resulting nonlinear programming problem. The method employs only the gradient evaluations of the objective function, and no any matrix calculations and no line searches are required. This greatly reduces the calculation cost of the method, and is suitable for the solution of large size max-cut problems. The convergence properties of the proposed method to KKT points of the nonlinear programming are analyzed. If the solution obtained by the proposed method is a global solution of the nonlinear programming problem, the solution will provide an upper bound on the max-cut value. Then an approximate solution to the max-cut problem is generated from the solution of the nonlinear programming and provides a lower bound on the max-cut value. Numerical experiments and comparisons on some max-cut test problems （small and large size） show that the proposed algorithm is efficient to get the exact solutions for all small test problems andwell satisfied solutions for most of the large size test problems with less calculation costs.展开更多
基金Supported by the Natural Science Foundation of Zhejiang Province(LY21A010021)the National Natural Science Foundation of China(11701506)。
文摘In this paper,we investigate three canonical forms of interval convex quadratic pro-gramming problems.Necessary and suficient conditions for checking weak and strong optimality of given vector corresponding to various forms of feasible region,are established respectively.By using the concept of feasible direction,these conditions are formulated in the form of linear systems with both equations and inequalities.In addition,we provide two specific examples to illustrate the efficiency of the conditions.
文摘In this paper, a new superlinearly convergent algorithm for nonlinearly constrained optimization problems is presented. The search directions are directly computed by a few formulas, and neither quadratic programming nor linear equation need to be sovled. Under mild assumptions, the new algorithm is shown to possess global and superlinear convergence.
文摘This paper concerns the solution of the NP-hard max-bisection problems. NCP functions are employed to convert max-bisection problems into continuous nonlinear programming problems. Solving the resulting continuous nonlinear programming problem generates a solution that gives an upper bound on the optimal value of the max-bisection problem. From the solution, the greedy strategy is used to generate a satisfactory approximate solution of the max-bisection problem. A feasible direction method without line searches is proposed to solve the resulting continuous nonlinear programming, and the convergence of the algorithm to KKT point of the resulting problem is proved. Numerical experiments and comparisons on well-known test problems, and on randomly generated test problems show that the proposed method is robust, and very efficient.
文摘In this paper, two alternative theorems which differ from Theorem 10.2.6 in [1] and Theorem 1 in [3] are presented for a class of feasible direction algorithms. On the basis of alternative theorems, furthermore, two sufficient conditions of global convergence of this class of algorithms are obtained.
文摘Foreign investors wishing to establish a joint venture with a Chinese partner in China are requested to submit a feasibility study report to the department in charge as done abroad. The article given below describes how to write a feasibility study report according to the Chinese practical situation.
基金This work is supported by National Natural Science Foundation of China at 10231060.
文摘An effective continuous algorithm is proposed to find approximate solutions of NP-hard max-cut problems. The algorithm relaxes the max-cut problem into a continuous nonlinear programming problem by replacing n discrete constraints in the original problem with one single continuous constraint. A feasible direction method is designed to solve the resulting nonlinear programming problem. The method employs only the gradient evaluations of the objective function, and no any matrix calculations and no line searches are required. This greatly reduces the calculation cost of the method, and is suitable for the solution of large size max-cut problems. The convergence properties of the proposed method to KKT points of the nonlinear programming are analyzed. If the solution obtained by the proposed method is a global solution of the nonlinear programming problem, the solution will provide an upper bound on the max-cut value. Then an approximate solution to the max-cut problem is generated from the solution of the nonlinear programming and provides a lower bound on the max-cut value. Numerical experiments and comparisons on some max-cut test problems （small and large size） show that the proposed algorithm is efficient to get the exact solutions for all small test problems andwell satisfied solutions for most of the large size test problems with less calculation costs.
