Penalization in non-classical convex programming via variational convergence

A penalty method for convex functions which cannot necessarily be extended outside their effective domains by an everywhere finite convex function is proposed and combined with the proximal method. Proofs of convergence rely on variational convergence theory.     

Decomposition algorithm for convex differentiable minimization

In this paper, we propose a decomposition algorithm for convex differentiable minimization. This algorithm at each iteration solves a variational inequality problem obtained by adding to the gradient of the cost function a strongly proximal related function. A line search is then performed in the direction of the solution to this variational inequality (with respect to the original cost). If the constraint set is a Cartesian product ofm sets, the variational inequality decomposes intom coupled variational inequalities, which can be solved in either a Jacobi manner or a Gauss-Seidel manner. This algorithm also applies to the minimization of a strongly convex (possibly nondifferentiable) cost subject to linear constraints. As special cases, we obtain the GP-SOR algorithm of Mangasarian and De Leone, a diagonalization algorithm of Feijoo and Meyer, the coordinate descent method, and the dual gradient method. This algorithm is also closely related to a splitting algorithm of Gabay and a gradient projection algorithm of Goldstein and of Levitin-Poljak, and has interesting applications to separable convex programming and to solving traffic assignment problems.This work was partially supported by the US Army Research Office Contract No. DAAL03-86-K-0171 and by the National Science Foundation Grant No. ECS-85-19058. The author thanks the referees for their many helpful comments, particularly for suggesting the use of a general functionH instead of that given by (4).     

Sensitivity analysis in convex vector optimization

We consider a parametrized convex vector optimization problem with a parameter vectoru. LetY(u) be the objective space image of the parametrized feasible region. The perturbation mapW(u) is defined as the set of all minimal points of the setY(u) with respect to an ordering cone in the objective space. The purpose of this paper is to investigate the relationship between the contingent derivativeDW ofW and the contingent derivativeDY ofY. Sufficient conditions for MinDW=MinDY andDW=W minDY are obtained, respectively. Therefore, quantitative information on the behavior of the perturbation map is provided.The author would like to thank the anonymous referees for their helpful comments which improved the quality of this paper. The author would also like to thank Professor P. L. Yu for his encouragement.     

Magnetic specific heat and spin-glass-like ordering in the pyrochlores: RE2Mo2O7 (RE=Y,Sm, Gd,Ho)

The pyrochlores RE₂Mo₂O₇ (RE=Y, Sm, Gd, Ho) display, with the exception of Ho₂Mo₂O₇, magnetic transitions at 18 K, 68 K, 55 K, respectively. No long-range order occurs. The rare-earth atoms remain in the paramagnetic state down to 1.5 K. The magnetic specific heat behaviour is explained by spin-glass-like ordering of Mo⁴⁺ ions, imposing a molecular field of random character on the rare-earth ions, and by crystalline field-splitting effects.     

Primal-dual row-action method for convex programming

We present a primal-dual row-action method for the minimization of a convex function subject to general convex constraints. Constraints are used one at a time, no changes are made in the constraint functions and their Jacobian matrix (thus, the row-action nature of the algorithm), and at each iteration a subproblem is solved consisting of minimization of the objective function subject to one or two linear equations. The algorithm generates two sequences: one of them, called primal, converges to the solution of the problem; the other one, called dual, approximates a vector of optimal KKT multipliers for the problem. We prove convergence of the primal sequence for general convex constraints. In the case of linear constraints, we prove that the primal sequence converges at least linearly and obtain as a consequence the convergence of the dual sequence.The research of the first author was partially supported by CNPq Grant No. 301280/86.     

几种最小二乘估计（LSE）效率的偏序

研究了几种最小二乘估计（ＬＳＥ）效率的偏序，证明了均方误差比（ＭＳＥＲ）效率在几种效率度量中是最优的。     

The 1D spin- AF-Heisenberg model in a staggered field

We investigate the scaling properties of the excitation energies and transition amplitudes of the one-dimensional spin- antiferromagnetic Heisenberg model exposed to an external perturbation. Two types of perturbations are discussed in detail: a staggered field and a dimerized field. Received: 27 February 1998 / Accepted: 17 April 1998     

A concept of negative dependence using stochastic ordering

A concept of negative dependence called negative dependence by stochastic ordering is introduced. This concept satisfies various closure properties. It is shown that three models for negetive dependence satisfy it and that it implies the basic negative orthant inequalities. This concept is also satisfied by the multinomial, multivariate hypergeometric. Dirichlet and Dirichlet compound multinomial distributions. Furthermore, the joint distribution of ranks of a sample and the multivariate normal with nonpositive pairwise correlations also satisfy this condition. The positive dependence analog of this condition is also studied.     

Ratios of affine functions

Aratio of affine functions is a function which can be expressed as the ratio of a vector valued affine function and a scalar affine functional. The purpose of this note is to examine properties of sets which are preserved under images and inverse images of such functions. Specifically, we show that images and inverse images of convex sets under such functions are convex sets. Also, images of bounded, convex polytopes under such functions are bounded, convex polytopes. In addition, we provide sufficient conditions under which the extreme points of images of convex sets are images of extreme points of the underlying domains. Of course, this result is useful when one wishes to maximize a convex function over a corresponding set. The above assertions are well known for affine functions. Applications of the results include a problem that concerns the control of stochastic eigenvectors of stochastic matrices.