On Superlinear Convergence of Infeasible Interior-Point Algorithms for Linearly Constrained Convex Programs

This note derives bounds on the length of the primal-dual affinescaling directions associated with a linearly constrained convexprogram satisfying the following conditions: 1) the problem has asolution satisfying strict complementarity, 2) the Hessian of theobjective function satisfies a certain invariance property. Weillustrate the usefulness of these bounds by establishing thesuperlinear convergence of the algorithm presented in Wright andRalph [22] for solving the optimality conditions associatedwith a linearly constrained convex program satisfying the aboveconditions.     

Conditional Value-at-Risk in Stochastic Programs with Mixed-Integer Recourse

In classical two-stage stochastic programming the expected value of the total costs is minimized. Recently, mean-risk models - studied in mathematical finance for several decades - have attracted attention in stochastic programming. We consider Conditional Value-at-Risk as risk measure in the framework of two-stage stochastic integer programming. The paper addresses structure, stability, and algorithms for this class of models. In particular, we study continuity properties of the objective function, both with respect to the first-stage decisions and the integrating probability measure. Further, we present an explicit mixed-integer linear programming formulation of the problem when the probability distribution is discrete and finite. Finally, a solution algorithm based on Lagrangean relaxation of nonanticipativity is proposed. Received: April, 2004     

Finding Convex Sets in Convex Position

Let F{\cal{F}} denote a family of pairwise disjoint convex sets in the plane. F{\cal{F}} is said to be in convex position, if none of its members is contained in the convex hull of the union of the others. For any fixed k 3 5k\ge5, we give a linear upper bound on P_k(n)P_k(n), the maximum size of a family F{\cal{F}} with the property that any k members of F{\cal{F}} are in convex position, but no n are.     

Lagrange Multiplier Conditions Characterizing the Optimal Solution Sets of Cone-Constrained Convex Programs

Various characterizations of optimal solution sets of cone-constrained convex optimization problems are given. The results are expressed in terms of subgradients and Lagrange multipliers. We establish first that the Lagrangian function of a convex program is constant on the optimal solution set. This elementary property is then used to derive various simple Lagrange multiplier-based characterizations of the solution set. For a finite-dimensional convex program with inequality constraints, the characterizations illustrate that the active constraints with positive Lagrange multipliers at an optimal solution remain active at all optimal solutions of the program. The results are applied to derive corresponding Lagrange multiplier characterizations of the solution sets of semidefinite programs and fractional programs. Specific examples are given to illustrate the nature of the results.     

Characterizations of the Solution Sets of Convex Programs and Variational Inequality Problems

For a convex program in a normed vector space with the objective function admitting the Gateaux derivative at an optimal solution, we show that the solution set consists of the feasible points lying in the hyperplane whose normal vector equals the Gateaux derivative. For a general continuous convex program, a feasible point is an optimal solution iff it lies in a hyperplane with a normal vector belonging to the subdifferential of the objective function at this point. In several cases, the solution set of a variational inequality problem is shown to coincide with the solution set of a convex program with its dual gap function as objective function, while the mapping involved can be used to express the above normal vectors.The research was supported by the National Science Council of the Republic of China. The authors are grateful to the referees for valuable comments and constructive suggestions.     

Irreducible Algebraic Sets in Metabelian Groups

We present the construction for a u-product G₁ ○ G₂ of two u-groups G₁ and G₂, and prove that G₁ ○ G₂ is also a u-group and that every u-group, which contains G₁ and G₂ as subgroups and is generated by these, is a homomorphic image of G₁ ○ G₂. It is stated that if G is a u-group then the coordinate group of an affine space Gⁿ is equal to G ○ F_n, where F_n is a free metabelian group of rank n. Irreducible algebraic sets in G are treated for the case where G is a free metabelian group or wreath product of two free Abelian groups of finite ranks. __________ Translated from Algebra i Logika, Vol. 44, No. 5, pp. 601–621, September–October, 2005. Supported by RFBR grant No. 05-01-00292, by FP “Universities of Russia” grant No. 04.01.053, and by RF Ministry of Education grant No. E00-1.0-12.     

Constructible Convex Sets

We investigate when closed convex sets can be written as countable intersections of closed half-spaces in Banach spaces. It is reasonable to consider this class to comprise the constructible convex sets since such sets are precisely those that can be defined by a countable number of linear inequalities, hence are accessible to techniques of semi-infinite convex programming. We also explore some model theoretic implications. Applications to set convergence are given as limiting examples.     

求解凸二次规划问题的不可行内点算法

该文对一般的凸二次规划问题，给出了一个不可行内点算法，并证明了该算法经过犗（狀２犔）步迭代之后，要么得到问题的一个近似最优解，要么说明该问题在某个较大的区域内无解．     

Translating Finite Sets into Convex Sets

Let X be a reflexive Banach space, and let C X be a closed,convex and bounded set with empty interior. Then, for every > 0, there is a nonempty finite set F X with an arbitrarilysmall diameter, such that C contains at most .|F| points ofany translation of F. As a corollary, a separable Banach spaceX is reflexive if and only if every closed convex subset ofX with empty interior is Haar null. 2000 Mathematics SubjectClassification 46B20 (primary), 28C20 (secondary).     

Pebble Sets in Convex Polygons

Lukács and András posed the problem of showing the existence of a set of n−2 points in the interior of a convex n-gon so that the interior of every triangle determined by three vertices of the polygon contains a unique point of S. Such sets have been called pebble sets by De Loera, Peterson, and Su. We seek to characterize all such sets for any given convex polygon in the plane. We first consider a certain class of pebble sets, called peripheral because they consist of points that lie close to the boundary of the polygon. We characterize all peripheral pebble sets, and show that for regular polygons, these are the only ones. Though we demonstrate examples of polygons where there are other pebble sets, we nevertheless provide a characterization of the kinds of points that can be involved in non-peripheral pebble sets. We furthermore describe algorithms to find such points.     

Convex Sets in Acyclic Digraphs

A non-empty set X of vertices of an acyclic digraph is called connected if the underlying undirected graph induced by X is connected and it is called convex if no two vertices of X are connected by a directed path in which some vertices are not in X. The set of convex sets (connected convex sets) of an acyclic digraph D is denoted by and its size by co(D) (cc(D)). Gutin et al. (2008) conjectured that the sum of the sizes of all convex sets (connected convex sets) in D equals Θ(n · co(D)) (Θ(n · cc(D))) where n is the order of D. In this paper we exhibit a family of connected acyclic digraphs with and . We also show that the number of connected convex sets of order k in any connected acyclic digraph of order n is at least n − k + 1. This is a strengthening of a theorem of Gutin and Yeo.     

Computer Codes for the Analysis of Infeasible Linear Programs

As linear programs have grown larger and more complex, infeasible models are appearing more frequently. Because of the scale and complexity of the models, automated assistance is very often needed in determining the cause of the infeasibility so that model repairs can be made. Fortunately, researchers have developed algorithms for analysing infeasible LPs in recent years, and these have lately found their way into commercial LP computer codes. This paper briefly reviews the underlying algorithms, surveys the computer codes, and compares their performance on a set of test problems.     

Extremal Convex Planar Sets

Each convex planar set K has a perimeter C, a minimum width E, an area A, and a diameter D. The set of points (E,C, A^1/2, D) corresponding to all such sets is shown to occupy a cone in the non-negative orthant of R⁴with its vertex at the origin. Its three-dimensional cross section S in the plane D = 1 is investigated. S lies in a rectangular parallelepiped in R³. Results of Lebesgue, Kubota, Fukasawa, Sholander, and Hemmi are used to determine some of the boundary surfaces of S, and new results are given for the other boundary surfaces. From knowledge of S, all inequalities among E, C ,A, and D can be found.     

A Combined Homotopy Infeasible Interior-Point Method for Convex Nonlinear Programming

In this paper, on the basis of the logarithmic barrier function and KKT conditions , we propose a combined homotopy infeasible interior-point method (CHIIP) for convex nonlinear programming problems. For any convex nonlinear programming, without strict convexity for the logarithmic barrier function, we get different solutions of the convex programming in different cases by CHIIP method.     

基于全牛顿步长求解凸二次规划问题的不可行内点算法

借助于全牛顿步长对凸二次规划问题提出了一种新的不可行内点算法.算法主要迭代由可行迭代步和中心路径邻域迭代步组成.其优点是线性搜寻方向是不需要的.最后证明算法迭代复杂性为O(nlogn/ε),与目前最好的不可行内点算法复杂性一致.     

全局收敛的凸规划的原始—对偶不可行内点算法

本对一类凸规划提出了一个原始-对偶不可行内点算法,并证明了算法的全局收敛性。