Sufficient Global Optimality Conditions for Bivalent Quadratic Optimization

We prove a sufficient global optimality condition for the problem of minimizing a quadratic function subject to quadratic equality constraints where the variables are allowed to take values –1 and 1. We extend the condition to quadratic problems with matrix variables and orthonormality constraints, and in particular to the quadratic assignment problem.     

Sufficient Conditions for Global Optimality of Bivalent Nonconvex Quadratic Programs with Inequality Constraints

We present sufficient conditions for the global optimality of bivalent nonconvex quadratic programs involving quadratic inequality constraints as well as equality constraints. By employing the Lagrangian function, we extend the global subdifferential approach, developed recently in Jeyakumar et al. (J. Glob. Optim., 2007, to appear; Math. Program. Ser. A, 2007, to appear) for studying bivalent quadratic programs without quadratic constraints, and derive global optimality conditions. The authors are grateful to the referees for constructive comments and suggestions which have contributed to the final preparation of the paper. Z.Y. Wu’s current address: School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Victoria, Australia. The work of this author was completed while at the Department of Applied Mathematics, University of New South Wales, Sydney, Australia.     

A Necessary and Sufficient Condition for Admissibility of Nonnegative Quadratic Estimator

ＡＮｅｃｅｓｓａｒｙａｎｄＳｕｆｆｉｃｉｅｎｔＣｏｎｄｉｔｉｏｎｆｏｒＡｄｍｉｓｓｉｂｉｌｉｔｙｏｆＮｏｎｎｅｇａｔｉｖｅＱｕａｄｒａｔｉｃＥｓｔｉｍａｔｏｒＬｕＣｈａｎｇｙｕ（鹿长余）（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，Ｎｏｒｔｈｅａ...     

Global Optimality Conditions in Maximizing a Convex Quadratic Function under Convex Quadratic Constraints

For the problem of maximizing a convex quadratic function under convex quadratic constraints, we derive conditions characterizing a globally optimal solution. The method consists in exploiting the global optimality conditions, expressed in terms of -subdifferentials of convex functions and -normal directions, to convex sets. By specializing the problem of maximizing a convex function over a convex set, we find explicit conditions for optimality.     

Global and Local Quadratic Minimization

We present a method which when applied to certain non-convex QP will locatethe globalminimum, all isolated local minima and some of the non-isolated localminima. The method proceeds by formulating a (multi) parametric convex QP interms ofthe data of the given non-convex QP. Based on the solution of the parametricQP,an unconstrained minimization problem is formulated. This problem ispiece-wisequadratic. A key result is that the isolated local minimizers (including theglobalminimizer) of the original non-convex problem are in one-to-one correspondencewiththose of the derived unconstrained problem.The theory is illustrated with several numerical examples. A numericalprocedure isdeveloped for a special class of non-convex QP's. It is applied to a problemfrom theliterature and verifies a known global optimum and in addition, locates apreviously unknown local minimum.     

On the Necessary and Sufficient Condition of the Local Optimal Solution of Quadratic Programming

In this paper a necessary and sufficient aptimality condition of quadratic programming is giver. The main result is Theorem 3.1. It is shown that a point x_0 is local optimal solution of quadratic programming (1, l)if and only if x_0 is a Kuhn-Tu cker point and h^Tch is non-negative for every point im{h|a^T_jh=0,j\in J^*,a^T_jh \leq 0,j\in J_1\J^*}.     

多元二次函数极值的研究

本文用多元函数的二阶泰勒展式给出了多元二次函数极值存在的充分必要条件,同时证明极值也是最值;用线性代数知识刻画了多元二次函数无极值时的特性.将这些方法和结论融入教学,设计了若干思考题.     

关于微分同胚的一个充分必要条件

确定两个流形是否C^r-微分同胚是微分流形研究中的重要课题,本文定义了反层的概念,给出了反层范畴,由此找到两个微分流形,C^r-微分同胚的特征刻画,于是给出了一种较以往更优的判定法．     

二次曲线上射影变换是对合的充要条件及对合的变换式

本文研究了二次曲线上射影变换是对合的充要条件 ,同时给出对合的变换式 ,填补了《射影几何》教材中的空白 .本文的主要结论是定理 3、定理 4 .     

二次系统存在抛物线奇异环的充要条件

In this paper . we obtained a necessary and sufficient condition for the existence of parabolic separatrix cycles in a quadratic system, and drawed its all possible phase-portraits. Further we obtain an accurate parameter interval that a Ⅲ type system exist ltmit cycles.     

Approximating Global Quadratic Optimization with Convex Quadratic Constraints

We consider the problem of approximating the global maximum of a quadratic program (QP) subject to convex non-homogeneous quadratic constraints. We prove an approximation quality bound that is related to a condition number of the convex feasible set; and it is the currently best for approximating certain problems, such as quadratic optimization over the assignment polytope, according to the best of our knowledge.     

Sufficient Optimality Criterion for Linearly Constrained,Separable Concave Minimization Problems

A sufficient optimality criterion for linearly-constrained concave minimization problems is given in this paper. Our optimality criterion is based on the sensitivity analysis of the relaxed linear programming problem. The main result is similar to that of Phillips and Rosen (Ref. 1); however, our proofs are simpler and constructive.In the Phillips and Rosen paper (Ref. 1), they derived a sufficient optimality criterion for a slightly different linearly-constrained concave minimization problem using exponentially many linear programming problems. We introduce special test points and, using these for several cases, we are able to show optimality of the current basic solution.The sufficient optimality criterion described in this paper can be used as a stopping criterion for branch-and-bound algorithms developed for linearly-constrained concave minimization problems.This research was supported by a Bolyai János Research Fellowship BO/00334/00 of the Hungarian Academy of Science and by the Hungarian Scientific Research Foundation, Grant OTKA T038027.     

拟共形映射的一个充要条件

设f:R^n_→ Rⁿ是一同胚,该文证明了 f 是拟共形映射的充要条件是 f 将 Rⁿ 中的任一John域映成 Rⁿ 中的John域.     

自对偶图的充要条件

给出自对偶图的充要条件,并利用此充要条件,能构造出所有自对偶图.     

非奇H-矩阵的充要条件

推广了"非奇H-矩阵的条件"一文中的主要结果,得到一类特殊矩阵为非奇H-矩阵的充分必要条件.     

Sufficient Optimality Conditions for Optimal Control Problems with State Constraints

It is well-known in optimal control theory that the maximum principle, in general, furnishes only necessary optimality conditions for an admissible process to be an optimal one. It is also well-known that if a process satisfies the maximum principle in a problem with convex data, the maximum principle turns to be likewise a sufficient condition. Here an invexity type condition for state constrained optimal control problems is defined and shown to be a sufficient optimality condition. Further, it is demonstrated that all optimal control problems where all extremal processes are optimal necessarily obey this invexity condition. Thus optimal control problems which satisfy such a condition constitute the most general class of problems where the maximum principle becomes automatically a set of sufficient optimality conditions.     

A Decomposition Method for Global and Local Quadratic Minimization

We present a decomposition method for indefinite quadratic programming problems having n variables and m linear constraints. The given problem is decomposed into at most m QP subproblems each having m linear constraints and n-1 variables. All global minima, all isolated local minima and some of the non-isolated local minima for the given problem are obtained from those of the lower dimensional subproblems. One way to continue solving the given problem is to apply the decomposition method again to the subproblems and repeatedly doing so until subproblems of dimension 1 are produced and these can be solved directly. A technique to reduce the potentially large number of subproblems is formulated.