Matrix conditioning and nonlinear optimization

In a series of recent papers, Oren, Oren and Luenberger, Oren and Spedicato, and Spedicato have developed the self-scaling variable metric algorithms. These algorithms alter Broyden's single parameter family of approximations to the inverse Hessian to a double parameter family. Conditions are given on the new parameter to minimize a bound on the condition number of the approximated inverse Hessian while insuring improved step-wise convergence.Davidon has devised an update which also minimizes the bound on the condition number while remaining in the Broyden single parameter family.This paper derives initial scalings for the approximate inverse Hessian which makes members of the Broyden class self-scaling. The Davidon, BFGS, and Oren—Spedicato updates are tested for computational efficiency and stability on numerous test functions, with the results indicating strong superiority computationally for the Davidon and BFGS update over the self-scaling update, except on a special class of functions, the homogeneous functions.     

Scaling by Binormalization

We present an iterative algorithm (BIN) for scaling all the rows and columns of a real symmetric matrix to unit 2-norm. We study the theoretical convergence properties and its relation to optimal conditioning. Numerical experiments show that BIN requires 2–4 matrix–vector multiplications to obtain an adequate scaling, and in many cases significantly reduces the condition number, more than other scaling algorithms. We present generalizations to complex, nonsymmetric and rectangular matrices.     

解非线性方程组的一类算法

§1.引言 求解线性方程组 a_i~Tx=b_i,i=1,2,…,n,(1.1)其中a_1,a_2,…,a_n线性无关. 设y~((1))为初值,U~((1))为任意非奇异n阶矩阵,我们用如下方法求解方程组(1.1). 先考虑前k-1个方程组成的亚定方程组 a_i~Tx=b_i,i=1,2,…,k-1.设{U~((k))}={a_1,a_2,…,a_(k-1)},这里{U~((k))}表示由U~((k))的列组成的子空间.显然,rank(U~((k)))=n-b+1.若y~((k))是相应的亚定方程的一个特解,则将其看作方程组     

Markov chain analysis of genetic algorithms applied to?fitness functions perturbed concurrently by additive and multiplicative noise

We analyze the transition and convergence properties of genetic algorithms (GAs) applied to fitness functions perturbed concurrently by additive and multiplicative noise. Both additive noise and multiplicative noise are assumed to take on finitely many values. We explicitly construct a Markov chain that models the evolution of GAs in this noisy environment and analyze it to investigate the algorithms. Our analysis shows that this Markov chain is indecomposable; it has only one positive recurrent communication class. Using this property, we establish a condition that is both necessary and sufficient for GAs to eventually (i.e., as the number of iterations goes to infinity) find a globally optimal solution with probability 1. Similarly, we identify a condition that is both necessary and sufficient for the algorithms to eventually with probability 1 fail to find any globally optimal solution. Our analysis also shows that the chain has a stationary distribution that is also its steady-state distribution. Based on this property and the transition probabilities of the chain, we compute the exact probability that a GA is guaranteed to select a globally optimal solution upon completion of each iteration.     

A modified scaling parameter for the memoryless BFGS updating formula

Based on an eigenvalue analysis, condition number of the scaled memoryless BFGS (Broyden–Fletcher–Goldfarb–Shanno) updating formula is obtained. Then, a modified scaling parameter is proposed for the mentioned updating formula, minimizing the given condition number. The suggested scaling parameter can be considered as a modified version of the self–scaling parameter proposed by Oren and Spedicato. Numerical experiments are done; they demonstrate practical effectiveness of the proposed scaling parameter.     

求解非线性规划问题的一类对偶算法

本文提出了一类求解不等式约束非线性规划问题的构造性对偶算法,我们证明在适当的条件下,势函数的罚参数存在一个阀值,当罚参数小于这个阀值时,由这一方法所产生的序列局部收敛于问题的一个Kuhn-Tucker解,我们也建立了解的依赖于罚参数的误差上界,最后,我们给出了一个特残势函数的数值结果。     

Subdivision of simplices relative to a cutting plane and finite concave minimization

In this article, our primary concern is the classical problem of minimizing globally a concave function over a compact polyhedron (Problem (P)). We present a new simplicial branch and bound approach, which combines triangulations of intersections of simplices with halfspaces and ideas from outer approximation in such a way, that a class of finite algorithms for solving (P) results. For arbitrary compact convex feasible sets one obtains a not necessarily finite but convergent algorithm. Theoretical investigations include determination of the number of simplices in each applied triangulation step and bounds on the number of iterations in the resulting algorithms. Preliminary numerical results are given, and additional applications are sketched.     

Numerical linear stability on controlled solutions of Boussinesq Navier-Stokes

A numerical linear stability analysis on optimally controlled solutions in a thermoconvective problem is presented. The thermoconvective problem is a stationary Boussinesq Navier-Stokes with a gaussian heating at the bottom with a large horizontal temperature gradient. The numerical method is Chebyshev collocation. The optimal control permits to find a boundary condition such that the vorticity is minimized. For some values of the penalizing parameter the controlled solution is more stable than the corresponding uncontrolled one. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A variable-metric method for function minimization derived from invariancy to nonlinear scaling

The effect of nonlinearly scaling the objective function on the variable-metric method is investigated, and Broyden's update is modified so that a property of invariancy to the scaling is satisfied. A new three-parameter class of updates is generated, and criteria for an optimal choice of the parameters are given. Numerical experiments compare the performance of a number of algorithms of the resulting class.The author is indebted to Professor S. S. Oren, Economic Engineering Department, Stanford University, Stanford, California, for stimulating discussions during the development of this paper. He also recognizes the financial support by the National Research Council of Italy (CNR) for his stay at Stanford University.     

Affine scaling interior Levenberg–Marquardt method for bound-constrained semismooth equations under local error bound conditions

We develop and analyze a new affine scaling Levenberg–Marquardt method with nonmonotonic interior backtracking line search technique for solving bound-constrained semismooth equations under local error bound conditions. The affine scaling Levenberg–Marquardt equation is based on a minimization of the squared Euclidean norm of linear model adding a quadratic affine scaling matrix to find a solution that belongs to the bounded constraints on variable. The global convergence results are developed in a very general setting of computing trial directions by a semismooth Levenberg–Marquardt method where a backtracking line search technique projects trial steps onto the feasible interior set. We establish that close to the solution set the affine scaling interior Levenberg–Marquardt algorithm is shown to converge locally Q-superlinearly depending on the quality of the semismooth and Levenberg–Marquardt parameter under an error bound assumption that is much weaker than the standard nonsingularity condition, that is, BD-regular condition under nonsmooth case. A nonmonotonic criterion should bring about speed up the convergence progress in the contours of objective function with large curvature.     

Sequential generation ofD-optimal input designs for linear dynamic systems

This paper considers the problem of sequentially generating test signals for parameter estimation in linear, single-input, single-output, discrete-time systems using a frequency-domain approach based on the theory of optimal experiments in regression analysis. The input signals are power constrained and are optimal in the sense that system information is maximized, where the criterion employed is the determinant of the Fisher information matrix (D-optimality). A class of algorithms is investigated, each member of which generates a sequence of input designs converging to aD-optimum. A number of these algorithms are compared computationally.     

单体型装配问题及其算法

单核苷酸多态性(SNP)单体型装配问题就是从给定的来自某人染色体的SNP片段中去除错误,重构出尽可能与原来片段一致的单体型.这个问题有几个不同的模型最少片段去除(MFR)问题,最少SNP去除(MSR)问题以及最少错误纠正(MEC)问题.前两个问题的复杂性与算法已有一些学者研究过.第三个问题已被证明是NP完全问题,但这个问题的实际算法还没有.该文对MEC问题给出了一个分支定界算法,这个算法能得到问题的全局最优解.通过这个算法对实际数据的计算说明了MEC模型的合理性,即在一定条件下,通过修正最少的错误重构出的单体型确实是真实的单体型.由于分支定界算法对这样一个NP完全问题不能在可接受的时间内解规模较大的问题,文中又给出了求解MEC问题的两个基于动态聚类的算法,以便对规模较大的问题在可接受的时间内得到近似最优解.数值实际表明这两个算法很快,很有效.这两个算法总能得到与分支定界找到的全局最优解很接近的近似最优解.鉴于MEC问题是NP完全的,这两个算法是有效的、实际的算法.     

Proximal Methods in View of Interior-Point Strategies

This paper deals with regularized penalty-barrier methods for convex programming problems. In the spirit of an iterative proximal regularization approach, an interior-point method is constructed, in which at each step a strongly convex function has to be minimized and the prox-term can be scaled by a variable scaling factor. The convergence of the method is studied for an axiomatically given class of barrier functions. According to the results, a wide class of barrier functions (in particular, logarithmic and exponential functions) can be applied to design special algorithms. For the method with a logarithmic barrier, the rate of convergence is investigated and assumptions that ensure linear convergence are given.     

A polynomial algorithm for minimum quadratic cost flow problems

Network flow problems with quadratic separable costs appear in a number of important applications such as; approximating input-output matrices in economy; projecting and forecasting traffic matrices in telecommunication networks; solving nondifferentiable cost flow problems by subgradient algorithms. It is shown that the scaling technique introduced by Edmonds and Karp (1972) in the case of linear cost flows for deriving a polynomial complexity bound for the out-of-kilter method, may be extended to quadratic cost flows and leads to a polynomial algorithm for this class of problems. The method may be applied to the solution of singly constrained quadratic programs and thus provides an alternative approach to the polynomial algorithm suggested by Helgason, Kennington and Lall (1980).     

Using concave envelopes to globally solve the nonlinear sum of ratios problem

This article presents a branch and bound algorithm for globally solving the nonlinear sum of ratios problem (P). The algorithm works by globally solving a sum of ratios problem that is equivalent to problem (P). In the algorithm, upper bounds are computed by maximizing concave envelopes of a sum of ratios function over intersections of the feasible region of the equivalent problem with rectangular sets. The rectangular sets are systematically subdivided as the branch and bound search proceeds. Two versions of the algorithm, with convergence results, are presented. Computational advantages of these algorithms are indicated, and some computational results are given that were obtained by globally solving some sample problems with one of these algorithms.     

Estimates of the rate of convergence of a dynamic reconstruction algorithm under incomplete information about the phase state

In this paper, we study a dynamic reconstruction algorithm which reconstructs the unknown unbounded input and all unobservable phase coordinates from the results of measurements of part of the coordinates. An upper and a lower bound for the accuracy of the reconstruction is obtained. We determine the class of inputs for which the upper bound is uniform. We give a condition for optimally matching the algorithm parameters, ensuring the highest order of the upper bound and equating the orders of the upper and lower bounds. Thus, we establish the sharpness of the upper bound.     

Optimally exact algorithm for solution of a certain numerical integration problem

We study optimally exact algorithms for calculation of integrals of certain quickly oscillating functions. We construct an optimally exact passive algorithm for a class of functions with a known number of extrema.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 43, No. 1, pp. 53–59, January, 1991.     

Asymptotically unbiased estimation of the second order tail parameter

We introduce a class of asymptotically unbiased estimators for the second order parameter in extreme value statistics. The estimators are constructed by means of an appropriately chosen linear combination of two simple, but biased, kernel estimators for the second order parameter. Asymptotic normality is proven under a third order condition on the tail behavior, some conditions on the kernel functions and for an intermediate number of upper order statistics. A specific member from the proposed class, obtained with power kernel functions, is derived and its finite sample behavior studied in a small simulation experiment.     

Stabilization of Affine Systems by Bounded Control

We consider stabilization of nonlinear affine dynamical systems by bounded control. Boundedness of control is achieved by specifying the parameter variation program for a one-parameter family of stabilizing controls. A class of globally stabilizable systems is identified. Conditions are derived when the affine system is stabilizable by a bounded control in a region, and a bound on this stabilization region is constructed using Lyapunov functions.