On uniqueness of Kuhn-Tucker multipliers in nonlinear programming

Recently Fujiwara, Han and Mangasarian introduced a new constraint qualification which is a slight tightening of the well-known Mangasarian—Fromovitz constraint qualification. We show that this new qualification is a necessary and sufficient condition for the uniqueness of Kuhn—Tucker multipliers. We also show that it implies the satisfaction of second order necessary optimality conditions at a local minimum.     

Using separable programming to solve the multi-product multiple ex-ante constraint newsvendor problem and extensions

This paper provides an approximating programming technique to solve the multi-product newsvendor model in which product demands are independent and stocking quantities are subject to two or more ex-ante linear contraints, such as budget or volume constraints. Previous research has attempted to solve this problem with Lagrange relaxation techniques or by limiting the distribution of demand. However, by taking advantage of the separable nature of the problem, a close approximation of the optimal solution can be found using convex separable programming for any demand distribution in the traditional newsvendor model and extensions. Sensitivity analysis of the linear program provides managerial insight into the effects of parameters of the problem on the optimal solution and future decisions.     

Integrating operations research in constraint programming

This paper presents constraint programming (CP) as a natural formalism for modelling problems, and as a flexible platform for solving them. CP has a range of techniques for handling constraints including several forms of propagation and tailored algorithms for global constraints. It also allows linear programming to be combined with propagation and novel and varied search techniques which can be easily expressed in CP. The paper describes how CP can be used to exploit linear programming within different kinds of hybrid algorithm. In particular it can enhance techniques such as Lagrangian relaxation, Benders decomposition and column generation.     

Optimality conditions in multiobjective differentiable programming

A theorem stated in Ref. 1 is corrected.     

Critical sets in parametric optimization

We deal with one-parameter families of optimization problems in finite dimensions. The constraints are both of equality and inequality type. The concept of a generalized critical point (g.c. point) is introduced. In particular, every local minimum, Kuhn-Tucker point, and point of Fritz John type is a g.c. point. Under fairly weak (even generic) conditions we study the set consisting of all g.c. points. Due to the parameter, the set is pieced together from one-dimensional manifolds. The points of can be divided into five (characteristic) types. The subset of nondegenerate critical points (first type) is open and dense in (nondegenerate means: strict complementarity, nondegeneracy of the corresponding quadratic form and linear independence of the gradients of binding constraints). A nondegenerate critical point is completely characterized by means of four indices. The change of these indices along is presented. Finally, the Kuhn-Tucker subset of is studied in more detail, in particular in connection with the (failure of the) Mangasarian-Fromowitz constraint qualification.     

Constraint qualifications in quasidifferentiable optimization

The classical linearization procedure for differentiable nonlinear programming problems can be naturally generalized to the quasidifferentiable case. As in the classical case one has to impose so-called constraint qualifications on the constraint functions in order to ensure that optimality of a feasible point implies optimality of the nullvector for the corresponding quasilinearized problem. We present various constraint qualifications in a unified setting, propose a new one, and investigate the relations between these conditions.Supported by DFG Grant Pa 219/5-1.     

Core-generating approximate minimum entropy discretization for rough set feature selection in pattern classification

Rough set feature selection (RSFS) can be used to improve classifier performance. RSFS removes redundant attributes whilst retaining important ones that preserve the classification power of the original dataset. Reducts are feature subsets selected by RSFS. Core is the intersection of all the reducts of a dataset. RSFS can only handle discrete attributes, hence, continuous attributes need to be discretized before being input to RSFS. Discretization determines the core size of a discrete dataset. However, current discretization methods do not consider the core size during discretization. Earlier work has proposed core-generating approximate minimum entropy discretization (C-GAME) algorithm which selects the maximum number of minimum entropy cuts capable of generating a non-empty core within a discrete dataset. The contributions of this paper are as follows: (1) the C-GAME algorithm is improved by adding a new type of constraint to eliminate the possibility that only a single reduct is present in a C-GAME-discrete dataset; (2) performance evaluation of C-GAME in comparison to C4.5, multi-layer perceptrons, RBF networks and k-nearest neighbours classifiers on ten datasets chosen from the UCI Machine Learning Repository; (3) performance evaluation of C-GAME in comparison to Recursive Minimum Entropy Partition (RMEP), Chimerge, Boolean Reasoning and Equal Frequency discretization algorithms on the ten datasets; (4) evaluation of the effects of C-GAME and the other four discretization methods on the sizes of reducts; (5) an upper bound is defined on the total number of reducts within a dataset; (6) the effects of different discretization algorithms on the total number of reducts are analysed; (7) performance analysis of two RSFS algorithms (a genetic algorithm and Johnson’s algorithm).     

带模糊约束的最大流问题

首次提出带模糊约束的最大流问题，并根据网络中的弧容量限制是否带有模糊性，分别建立数学模型，给出求解这两个模型的相应算法和有关实例。