On nonoscillating integrals for computing inhomogeneous Airy functions

Integral representations are considered of solutions of the inhomogeneous Airy differential equation . The solutions of these equations are also known as Scorer functions. Certain functional relations for these functions are used to confine the discussion to one function and to a certain sector in the complex plane. By using steepest descent methods from asymptotics, the standard integral representations of the Scorer functions are modified in order to obtain nonoscillating integrals for complex values of . In this way stable representations for numerical evaluations of the functions are obtained. The methods are illustrated with numerical results.

     

CGS algorithms for unconstrained minimization of functions

In 1952, Hestenes and Stiefel first established, along with the conjugate-gradient algorithm, fundamental relations which exist between conjugate direction methods for function minimization on the one hand and Gram-Schmidt processes relative to a given positive-definite, symmetric matrix on the other. This paper is based on a recent reformulation of these relations by Hestenes which yield the conjugate Gram-Schmidt (CGS) algorithm. CGS includes a variety of function minimization routines, one of which is the conjugate-gradient routine. This paper gives the basic equations of CGS, including the form applicable to minimizing general nonquadratic functions ofn variables. Results of numerical experiments of one form of CGS on five standard test functions are presented. These results show that this version of CGS is very effective.The preparation of this paper was sponsored in part by the US Army Research Office, Grant No. DH-ARO-D-31-124-71-G18.The authors wish to thank Mr. Paul Speckman for the many computer runs made using these algorithms. They served as a good check on the results which they had obtained earlier. Special thanks must go to Professor M. R. Hestenes whose constant encouragement and assistance made this paper possible.     

A modified steepest descent method with applications to maximizing likelihood functions

In maximizing a non-linear function G(), it is well known that the steepest descent method has a slow convergence rate. Here we propose a systematic procedure to obtain a 1–1 transformation on the variables , so that in the space of the transformed variables, the steepest descent method produces the solution faster. The final solution in the original space is obtained by taking the inverse transformation. We apply the procedure in maximizing the likelihood functions of some generalized distributions which are widely used in modeling count data. It was shown that for these distributions, the steepest descent method via transformations produced the solutions very fast. It is also observed that the proposed procedure can be used to expedite the convergence rate of the first derivative based algorithms, such as Polak-Ribiere, Fletcher and Reeves conjugate gradient methods as well.     

New Direct Numerical Methods for Some Multidimensional Problems of the Calculus of Variations

In this paper, we develop a new approach to the design of direct numerical methods for multidimensional problems of the calculus of variations. The approach is based on a transformation of the problem with the use of a new class of Sobolev-like spaces that is studied in the article. This transformation allows one to analytically compute the direction of steepest descent of the main functional of the calculus of variations with respect to a certain inner product, and, in turn, to construct new direct numerical methods for multidimensional problems of the calculus of variations. In the end of the paper, we point out how the approach developed in the article can be extended to the case of problems with more general boundary conditions, problems for functionals depending on higher order derivatives, and problems with isoperimetric and/or pointwise constraints.     

Convergence proof of minimization algorithms for nonconvex functions

In this paper, we prove a theorem of convergence to a point for descent minimization methods. When the objective function is differentiable, the convergence point is a stationary point. The theorem, however, is applicable also to nondifferentiable functions. This theorem is then applied to prove convergence of some nongradient algorithms.     

A Solution Method for a Special Class of Nondifferentiable Unconstrained Optimization Problems

We consider quasidifferentiable functions in the sense of Demyanov and Rubinov, i. e. functions, which are directionally differentiable and whose directional derivative can be expressed as a difference of two sublinear functions, so that its subdifferential, called the quasidifferential, consists of a pair of sets. For these functions a generalized gradient algorithm is proposed. Its behaviour is studied in detail for the special class of continuously subdifferentiable functions. Numerical test results are given. Finally, the general quasidifferentiable case is simulated by means of perturbed subdifferentials, where we make use of the non-uniqueness in the quasidifferential representation.     

Unified steerable phase I-phase II method of feasible directions for semi-infinite optimization

In this paper, we complete a cycle in the construction of methods of feasible directions for solving semi-infinite constrained optimization problems. Earlier phase I-phase II methods of feasible directions used one search direction rule in all of ⁿ with two stepsize rules, one for feasible points and one for infeasible points. The algorithm presented in this paper uses both a single search direction rule and a single stepsize rule in all of ⁿ . In addition, the new algorithm incorporates a steering parameter which can be used to control the speed with which feasibility is achieved. The new algorithm is simpler to analyze and performs somewhat better than existing, first order, phase I-phase II methods. The new algorithm is globally convergent, with linear rate.The research reported herein was sponsored in part by the National Science Foundation Grant ECS-8713334, the Air Force Office of Scientific Research Contract AFOSR-86-0116, and the State of California MICRO Program Grant 532410-19900.The authors would like to thank Dr. J. Higgins for providing the C-code of Algorithm 3.1.     

An algorithm for the minimax weber problem

The paper presents an algorithm for finding the weighted absolute center of a given set of points in the Euclidean plane.The algorithm is a specialized implementation of the method of feasible directions.The presentation includes a proof of convergence as well as computational experience.     

非线性不等式约束最优化问题一个新的广义既约梯度法

本文借助一种新的求基转轴运算建立了带非线性不等式约束最优化问题的一个新的广义既约梯度法．算法不引入任何松驰变量，以致扩大问题的规模，也不需对约束函数和变量的界预先估计．另一重要特点是方法不再使用隐函数理论确定搜索方向，而是由简单的显式给出．因此方法计算量小，结构简单，便于应用．对于非Ｋ—Ｔ点ｘ，我们构造的方向为可行下降的．本文证明了算法具有全局收敛性．     

A trust region method for minimization of nonsmooth functions with linear constraints

We introduce a trust region algorithm for minimization of nonsmooth functions with linear constraints. At each iteration, the objective function is approximated by a model function that satisfies a set of assumptions stated recently by Qi and Sun in the context of unconstrained nonsmooth optimization. The trust region iteration begins with the resolution of an “easy problem”, as in recent works of Martínez and Santos and Friedlander, Martínez and Santos, for smooth constrained optimization. In practical implementations we use the infinity norm for defining the trust region, which fits well with the domain of the problem. We prove global convergence and report numerical experiments related to a parameter estimation problem. Supported by FAPESP (Grant 90/3724-6), FINEP and FAEP-UNICAMP. Supported by FAPESP (Grant 90/3724-6 and grant 93/1515-9).     

Linearly constrained global minimization of functions with concave minorants

In this note, we show how a recent approach for solving linearly constrained multivariate Lipschitz optimization problems and corresponding systems of inequalities can be generalized to solve optimization problems where the objective function is only assumed to possess a concave minorant at each point. This class of functions includes not only Lipschitz functions and some generalizations, such as certain -convex functions and Hölder functions with exponent greater than one, but also all functions which can be expressed as differences of two convex functions (d.c. functions). Thus, in particular, a new approach is obtained for the important problem of minimizing a d.c. function over a polytope.     

Simple global minimization algorithm for one-variable rational functions

Sturm's chain technique for evaluation of a number of real roots of polynomials is applied to construct a simple algorithm for global optimization of polynomials or generally for rational functions of finite global minimal value. The method can be applied both to find the global minimum in an interval or without any constraints. It is shown how to use the method to minimize globally a truncated Fourier series. The results of numerical tests are presented and discussed. The cost of the method scales as the square of the degree of the polynomial.     

Pshenichnyi's theorem for vector minimization

Analogues of Pshenichnyi's necessary and sufficient condition for minima in convex programming problems are given for the case where the objective function is vector-valued.     

Low complexity secant quasi-Newton minimization algorithms for nonconvex functions

In this work some interesting relations between results on basic optimization and algorithms for nonconvex functions (such as BFGS and secant methods) are pointed out. In particular, some innovative tools for improving our recent secant BFGS-type and algorithms are described in detail.     

The Construction of cubature rules for multivariate highly oscillatory integrals

We present an efficient approach to evaluate multivariate highly oscillatory integrals on piecewise analytic integration domains. Cubature rules are developed that only require the evaluation of the integrand and its derivatives in a limited set of points. A general method is presented to identify these points and to compute the weights of the corresponding rule.

The accuracy of the constructed rules increases with increasing frequency of the integrand. For a fixed frequency, the accuracy can be improved by incorporating more derivatives of the integrand. The results are illustrated numerically for Fourier integrals on a circle and on the unit ball, and for more general oscillators on a rectangular domain.

     

Stochastic penalty function methods for nonsmooth constrained minimization

This paper presents a stochastic algorithm with proper stopping rules for nonsmooth inequality-constrained minimization problems. The algorithm is based on an augmented Lagrangian dual problem transformed from a primal one, and it consists of two loops: an outer loop, which is the iteration for the approximate Lagrange multipliers, and an inner loop, which is a nonsmooth unconstrained minimization subroutine. Under mild assumptions, the algorithm is proved to be almost surely convergent.This work was partially supported by the Science Foundation of Ningbo University. The author is grateful to Professor D. Q. Mayne for his help with this work and to two referees for their helpful comments.     

Steepest descent method with a generalized Armijo search for quasiconvex functions on Riemannian manifolds

This paper extends the full convergence of the steepest descent method with a generalized Armijo search and a proximal regularization to solve minimization problems with quasiconvex objective functions on complete Riemannian manifolds. Previous convergence results are obtained as particular cases and some examples in non-Euclidian spaces are given. In particular, our approach can be used to solve constrained minimization problems with nonconvex objective functions in Euclidian spaces if the set of constraints is a Riemannian manifold and the objective function is quasiconvex in this manifold.     

On the Behavior of the Gradient Norm in the Steepest Descent Method

It is well known that the norm of the gradient may be unreliable as a stopping test in unconstrained optimization, and that it often exhibits oscillations in the course of the optimization. In this paper we present results descibing the properties of the gradient norm for the steepest descent method applied to quadratic objective functions. We also make some general observations that apply to nonlinear problems, relating the gradient norm, the objective function value, and the path generated by the iterates.