Minimum Distance Between the Faces of Two Convex Polyhedra: A Sufficient Condition

The problem of finding the Euclidean distance between two convex polyhedra can be reduced to the combinatorial optimization problem of finding the minimum distance between their faces. This paper presents a global optimality criterion for this problem. An algorithm (QLDPA) for the fast computation of the distance between convex and bounded polyhedra is proposed as an application of it. Computer experiments show its fast performance, especially when the total number of vertices is large.     

Moments of an exponential functional of random walks and permutations with given descent sets

The exponential functional of simple, symmetric random walks with negative drift is an infinite polynomial Y = 1 + ξ₁ + ξ₁ξ₂ + ξ₁ξ₂ξ₃ + ⋯ of independent and identically distributed non-negative random variables. It has moments that are rational functions of the variables μ _k = E(ξ ^k ) < 1 with universal coefficients. It turns out that such a coefficient is equal to the number of permutations with descent set defined by the multiindex of the coefficient. A recursion enumerates all numbers of permutations with given descent sets in the form of a Pascal-type triangle. This revised version was published online in August 2006 with corrections to the Cover Date.     

共轭下降法的全局收敛性

本文提出了一种Armijo型的线搜索，并在这种线搜索下讨论了共轭下降法的全局收敛性，且可得方法在每次迭代均产生一个下降搜索方向．     

Steepest Descent, CG, and Iterative Regularization of Ill-Posed Problems

The state of the art iterative method for solving large linear systems is the conjugate gradient (CG) algorithm. Theoretical convergence analysis suggests that CG converges more rapidly than steepest descent. This paper argues that steepest descent may be an attractive alternative to CG when solving linear systems arising from the discretization of ill-posed problems. Specifically, it is shown that, for ill-posed problems, steepest descent has a more stable convergence behavior than CG, which may be explained by the fact that the filter factors for steepest descent behave much less erratically than those for CG. Moreover, it is shown that, with proper preconditioning, the convergence rate of steepest descent is competitive with that of CG.This revised version was published online in October 2005 with corrections to the Cover Date.     

Discrete global descent method for discrete global optimization and nonlinear integer programming

A novel method, entitled the discrete global descent method, is developed in this paper to solve discrete global optimization problems and nonlinear integer programming problems. This method moves from one discrete minimizer of the objective function f to another better one at each iteration with the help of an auxiliary function, entitled the discrete global descent function. The discrete global descent function guarantees that its discrete minimizers coincide with the better discrete minimizers of f under some standard assumptions. This property also ensures that a better discrete minimizer of f can be found by some classical local search methods. Numerical experiments on several test problems with up to 100 integer variables and up to 1.38 × 10¹⁰⁴ feasible points have demonstrated the applicability and efficiency of the proposed method.     

Stochastic algorithms with armijo stepsizes for minimization of functions

Stochastic algorithms for optimization problems, where function evaluations are done by Monte Carlo simulations, are presented. At each iteratex _i, they draw a predetermined numbern(i) of sample points from an underlying probability space; based on these sample points, they compute a feasible-descent direction, an Armijo stepsize, and the next iteratex _i+1. For an appropriate optimality function , corresponding to an optimality condition, it is shown that, ifn(i) , then (x _i) 0, whereJ is a set of integers whose upper density is zero. First, convergence is shown for a general algorithm prototype: then, a steepest-descent algorithm for unconstrained problems and a feasible-direction algorithm for problems with inequality constraints are developed. A numerical example is supplied.     

对固体圆二色光谱测试方法的再认识——兼谈“浓度效应”

对近期发展的固体圆二色(CD)光谱测试方法进行了概述、评价和比较, 着重探讨了“浓度效应”的存在使固体CD光谱失真的原因. 通过对本课题组和其他作者已报道的四种化合物的固体CD谱再测试的反思, 强调了依手性化合物的手性光谱学性质不同, 根据浓度梯度实验选择其合适测试浓度的必要性. 对固有手性的阻转异构化合物(S)-1,1'-联二萘酚(S-BINOL)进行了成膜法固体CD谱浓度梯度测试, 发现所得固体薄膜CD谱中也存在着“浓度效应”     

对固体圆二色光谱测试方法的再认识——兼谈“浓度效应”

对近期发展的固体圆二色(CD)光谱测试方法进行了概述、评价和比较, 着重探讨了“浓度效应”的存在使固体CD光谱失真的原因. 通过对本课题组和其他作者已报道的四种化合物的固体CD谱再测试的反思, 强调了依手性化合物的手性光谱学性质不同, 根据浓度梯度实验选择其合适测试浓度的必要性. 对固有手性的阻转异构化合物(S)-1,1'-联二萘酚(S-BINOL)进行了成膜法固体CD谱浓度梯度测试, 发现所得固体薄膜CD谱中也存在着“浓度效应”     

对固体圆二色光谱测试方法的再认识——兼谈“浓度效应”

对近期发展的固体圆二色(CD)光谱测试方法进行了概述、评价和比较, 着重探讨了“浓度效应”的存在使固体CD光谱失真的原因. 通过对本课题组和其他作者已报道的四种化合物的固体CD谱再测试的反思, 强调了依手性化合物的手性光谱学性质不同, 根据浓度梯度实验选择其合适测试浓度的必要性. 对固有手性的阻转异构化合物(S)-1,1'-联二萘酚(S-BINOL)进行了成膜法固体CD谱浓度梯度测试, 发现所得固体薄膜CD谱中也存在着“浓度效应”