Density of stable convex semi-infinite vector optimization problems

In this paper, we show that every convex semi-infinite vector optimization (CSVO for brevity) problem can be arbitrarily approximated by stable CSVO problems, i.e., the set of all stable CSVO problems (the weak solution map is continuous or the solution map is upper semicontinuous) is dense in the set of all CSVO problems with the given topology.     

Applying relational algebra and RelView to coalition formation

We present an application of relational algebra to coalition formation. This leads to specifications, which can be executed with the help of the RelView tool after a simple translation into the tool’s programming language. As an example we consider a simplification of the situation in Poland after the 2001 elections.     

Counting as integration in feasible analysis

Suppose that it is possible to integrate real functions over a weak base theory related to polynomial time computability. Does it follow that we can count? The answer seems to be: obviously yes! We try to convince the reader that the severe restrictions on induction in feasible theories preclude a straightforward answer. Nevertheless, a more sophisticated reflection does indeed show that the answer is affirmative. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

The regularized feasible directions method for nonconvex optimization

This paper develops and studies a feasible directions approach for the minimization of a continuous function over linear constraints in which the update directions belong to a predetermined finite set spanning the feasible set. These directions are recurrently investigated in a cyclic semi-random order, where the stepsize of the update is determined via univariate optimization. We establish that any accumulation point of this optimization procedure is a stationary point of the problem, meaning that the directional derivative in any feasible direction is nonnegative. To assess and establish a rate of convergence, we develop a new optimality measure that acts as a proxy for the stationarity condition, and substantiate its role by showing that it is coherent with first-order conditions in specific scenarios. Finally we prove that our method enjoys a sublinear rate of convergence of this optimality measure in expectation.     

悬链式单点系泊浮筒主尺度可行域研究北大核心CSCD

为了能够设计合理的单点浮筒主尺度,归纳提出了悬链式单点浮筒5条主要设计原则.基于实际工程经验给出了浮筒重量的母型预估方法,探讨储备浮力,提出等干舷方案集,基于静力学原理和水动力分析探讨了浮筒的自由漂浮稳性及浮筒的运动响应,梳理了垂荡及摇摆随浮筒直径的变化规律.以25 m和100 m水深的浮筒直径上限和下限为边界建立可行域,分别以三个实际项目采纳的浮筒直径和45 m水深下单点浮筒直径的可行范围为参考,开展浮筒直径可行域的验证.研究表明:环境条件等不同因素也会引起相同水深下浮筒直径可行域的变化,因此在可行域的实际应用中应进一步考虑一定的偏差裕量.该文的可行域研究对于悬链式单点浮筒的主尺度确定具有积极的指导意义.     

Color-bounded hypergraphs, II: Interval hypergraphs and hypertrees

A color-bounded hypergraph is a hypergraph (set system) with vertex set X and edge set E={E₁,…,E_m}, together with integers s_i and t_i (1≤s_i≤t_i≤|E_i|) for i=1,…,m. A vertex coloring φ is feasible if the number of colors occurring in edge E_i satisfies s_i≤|φ(E_i)|≤t_i, for every i≤m.In this paper we point out that hypertrees-hypergraphs admitting a representation over a (graph) tree where each hyperedge E_i induces a subtree of the underlying tree-play a central role concerning the set of possible numbers of colors that can occur in feasible colorings. We also consider interval hypergraphs and circular hypergraphs, where the underlying graph is a path or a cycle, respectively. Sufficient conditions are given for a ‘gap-free’ chromatic spectrum; i.e., when each number of colors is feasible between minimum and maximum. The algorithmic complexity of colorability is studied, too.Compared with the ‘mixed hypergraphs’-where ‘D-edge’ means (s_i,t_i)=(2,|E_i|), while ‘C-edge’ assumes (s_i,t_i)=(1,|E_i|−1)-the differences are rather significant.     

Combined phase I—phase II methods of feasible directions

This paper presents several new algorithms, generalizing feasible directions algorithms, for the nonlinear programming problem, min{f ⁰ (z) f ^j (z) 0,j = 1, 2, ,m}. These new algorithms do not require an initial feasible point. They automatically combine the operations of initialization (phase I) and optimization (phase II).Research sponsored by the National Science Foundation (RANN) Grant ENV76-04264 and the National Science Foundation Grant ENG73-08214-A01.     

Partial linearization methods in nonlinear programming

In this paper, we characterize a class of feasible direction methods in nonlinear programming through the concept of partial linearization of the objective function. Based on a feasible point, the objective is replaced by an arbitrary convex and continuously differentiable function, and the error is taken into account by a first-order approximation of it. A new feasible point is defined through a line search with respect to the original objective, toward the solution of the approximate problem. Global convergence results are given for exact and approximate line searches, and possible interpretations are made. We present some instances of the general algorithm and discuss extensions to nondifferentiable programming.The author wishes to thank Drs. K. Holmberg, T. Larsson, and A. Migdalas for their helpful comments.     

AN EFFECTIVE CONTINUOUS ALGORITHM FOR APPROXIMATE SOLUTIONS OF LARGE SCALE MAX-CUT PROBLEMS

An effective continuous algorithm is proposed to find approximate solutions of NP-hardmax-cut problems.The algorithm relaxes the max-cut problem into a continuous nonlinearprogramming problem by replacing n discrete constraints in the original problem with onesingle continuous constraint.A feasible direction method is designed to solve the resultingnonlinear programming problem.The method employs only the gradient evaluations ofthe 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 solutionof large size max-cut problems.The convergence properties of the proposed method toKKT points of the nonlinear programming are analyzed.If the solution obtained by theproposed method is a global solution of the nonlinear programming problem,the solutionwill provide an upper bound on the max-cut value.Then an approximate solution to themax-cut problem is generated from the solution of the nonlinear programming and providesa lower bound on the max-cut value.Numerical experiments and comparisons on somemax-cut test problems(small and large size)show that the proposed algorithm is efficientto get the exact solutions for all small test problems and well satisfied solutions for mostof the large size test problems with less calculation costs.