Defining continuity of real functions of real variables

Continuity of a real function of a real variable has been defined in various ways over almost 200 years. Contrary to popular belief, the definitions are not all equivalent, because their consequences for four somewhat pathological functions reveal five essentially different cases. The four defensible ones imply just two cases for continuity on an interval if that is defined by using pointwise continuity at each point. Some authors had trouble: two different textbooks each gave two arguably inconsistent definitions, three more changed their definitions in their second editions, two more claimed continuity at a point for functions not defined there, and one gave a definition implying it for a function with no limit there.     

Inexact-Restoration Algorithm for Constrained Optimization1

We introduce a new model algorithm for solving nonlinear programming problems. No slack variables are introduced for dealing with inequality constraints. Each iteration of the method proceeds in two phases. In the first phase, feasibility of the current iterate is improved; in second phase, the objective function value is reduced in an approximate feasible set. The point that results from the second phase is compared with the current point using a nonsmooth merit function that combines feasibility and optimality. This merit function includes a penalty parameter that changes between consecutive iterations. A suitable updating procedure for this penalty parameter is included by means of which it can be increased or decreased along consecutive iterations. The conditions for feasibility improvement at the first phase and for optimality improvement at the second phase are mild, and large-scale implementation of the resulting method is possible. We prove that, under suitable conditions, which do not include regularity or existence of second derivatives, all the limit points of an infinite sequence generated by the algorithm are feasible, and that a suitable optimality measure can be made as small as desired. The algorithm is implemented and tested against the LANCELOT algorithm using a set of hard-spheres problems.     

Comparison of two Eulerian solvers for the four-dimensional Vlasov equation: Part I

This paper presents two methods for solving the four-dimensional Vlasov equation on a grid of the phase space. The two methods are based on the semi-Lagrangian method which consists in computing the distribution function at each grid point by following the characteristic curves ending there. The first method reconstructs the distribution function using local splines which are well suited for a parallel implementation. The second method is adaptive using wavelets interpolation: only a subset of the grid points are conserved to manage data locality. Numerical results are presented in the second part.     

具有变号非线性项的二阶三点边值问题的两个正解

本文首先证明双锥上的一个不动点定理，并通过该定理研究一类具有变号非线性项的二阶三点边值问题两个正解的存在性．同时，该三点边值问题相关的Green函数也被给出．     

A least-distance programming procedure for minimization problems under linear constraints

In this paper, an algorithm is developed for solving a nonlinear programming problem with linear contraints. The algorithm performs two major computations. First, the search vector is determined by projecting the negative gradient of the objective function on a polyhedral set defined in terms of the gradients of the equality constraints and the near binding inequality constraints. This least-distance program is solved by Lemke's complementary pivoting algorithm after eliminating the equality constraints using Cholesky's factorization. The second major calculation determines a stepsize by first computing an estimate based on quadratic approximation of the function and then finalizing the stepsize using Armijo's inexact line search. It is shown that any accumulation point of the algorithm is a Kuhn-Tucker point. Furthermore, it is shown that, if an accumulation point satisfies the second-order sufficiency optimality conditions, then the whole sequence of iterates converges to that point. Computational testing of the algorithm is presented.     

二阶导数的几何意义

对一元函数二阶导数的几何意义进行阐释,认为一元函数的二阶导数是描述函数对应曲线的曲率的一个重要指标:二阶导数的绝对值与曲线曲率成正比;在驻点处,二阶导数的绝对值与曲率相等.     

A penalty-free method with line search for nonlinear equality constrained optimization

A new line search method is introduced for solving nonlinear equality constrained optimization problems. It does not use any penalty function or a filter. At each iteration, the trial step is determined such that either the value of the objective function or the measure of the constraint violation is sufficiently reduced. Under usual assumptions, it is shown that every limit point of the sequence of iterates generated by the algorithm is feasible, and there exists at least one limit point that is a stationary point for the problem. A simple modification of the algorithm by introducing second order correction steps is presented. It is shown that the modified method does not suffer from the Maratos’ effect, so that it converges superlinearly. The preliminary numerical results are reported.     

二阶多项式系统固定奇点附近解的解析展开

研究二阶多项式系统所确定的通过固定奇点的解．利用牛顿图法研究了这种解在固定奇点处的幂级数展开式,给出有无穷多个解通过固定奇点的判据,并在只有有限个解通过固定奇点的情况下,给出了过固定奇点的解个数的上界．     

奇异一阶周期系统的多重正解

主要建立了奇异一阶周期系统的多重正解,证明了在适当的条件下这个问题至少存在两个解.第一个正解的存在性是利用非线性L eray-Schauder抉择定理得到的,第二个解是利用K rasnoselsk ii锥不动点定理得到的.     

Level bundle-like algorithms for convex optimization

We propose two restricted memory level bundle-like algorithms for minimizing a convex function over a convex set. If the memory is restricted to one linearization of the objective function, then both algorithms are variations of the projected subgradient method. The first algorithm, proposed in Hilbert space, is a conceptual one. It is shown to be strongly convergent to the solution that lies closest to the initial iterate. Furthermore, the entire sequence of iterates generated by the algorithm is contained in a ball with diameter equal to the distance between the initial point and the solution set. The second algorithm is an implementable version. It mimics as much as possible the conceptual one in order to resemble convergence properties. The implementable algorithm is validated by numerical results on several two-stage stochastic linear programs.     

Partial Zeta Functions of Algebraic Varieties over Finite Fields

Motivated by arithmetic applications, we introduce the notion of a partial zeta function which generalizes the classical zeta function of an algebraic variety defined over a finite field. We then explain two approaches to the general structural properties of the partial zeta function in the direction of the Weil-type conjectures. The first approach, using an inductive fibred variety point of view, shows that the partial zeta function is rational in an interesting case, generalizing Dwork's rationality theorem. The second approach, due to Faltings, shows that the partial zeta function is always nearly rational.     

具有n阶转向点方程的渐近解的完全表达式

本文研究二阶线性常微分方程,使用广义Airy函数得到了方程在转向点附近形式一致有效渐近解的完全表达式。     

Imaging from monostatic scattered intensities

This paper is about inverse scattering from monostatic intensity‐only measurements. We first formally rederive the geometrical optics approximation for a penetrable convex target. We then derive two direct imaging methods. The first one finds the point that best maps measurements over one circle onto a second one, due to amplitude decay of the scattered wave. The second one is a linearization of the problem, based on estimating the curvature of the object as a function of the measurements, due to the geometrical optics approximation. The first method aims at estimating the position of the target, whereas the second one aims at reconstructing the shape. The paper finishes with numerical tests showing the relevance and the limits of the proposed methods. Copyright © 2013 John Wiley & Sons, Ltd.     

A language for nonlinear programming problems

An algebraic-like language for nonlinear programming problems is described. The rationale for the computation of the function values, gradients, and scond partial derivatives of the functions from their algebraic representation is developed. Each function is translated into an explicit factorable form or hierarchical representation which is used interpretively to compute the function value, gradient, and second partials of the function at each point for which such values are required. Computational efficiency is achieved by computing the matrix of second partials as the sum of a set of vector outer products, the vectors having resulted from the gradient computation, plus a diagonal matrix. An experimental computer program which implements the language and ties it to SUMT is described. In the experience with this program the computer times required have ranged from 4 to 30 times those times required by computer solutions to the same problems by using analyst-prepared programs to compute the function values, gradients, and second partial derivatives. A program based on a compiler approach to implementing the language, rather than the interpretative approach of the experimental program, will probably result in computer times between one and two times those required by using analyst-prepared programs.     

An algorithm for numerical differentiation of a function of one real variable

An Algol 60 procedure is described which will estimate the first, second or third order derivative of a function f(x) at a point x₀, using polynomial interpolation to values of f(x) at points on the real axis. This procedure does not require the user to provide information about the accuracy of the function values. The procedure itself monitors the noise level in these values, and allows for the effect of noise on the estimated derivative. The evaluation points may be restricted to one side of x₀ if f(x) makes this necessary, and are chosen either to minimise the error in the result, or to achieve a specified tolerance with as few function evaluations as possible.     

Examples of non-convergence of conjugate descent algorithms with exact line-searches

Examples are given in which quasi-Newton and other conjugate descent algorithms fail to converge to a minimum of the object function. In the first there is convergence to a point where the gradient is infinite; in the second, a region characterized by a fine terraced structure causes the iterates to spiral indefinitely. A modification of the second construction gives convergence to a point where the gradient is non-zero, but the gradient is not continuous at this point. The implication of these examples is discussed.     

Generalized Trajectory Methods for Finding Multiple Extrema and Roots of Functions

Two generalized trajectory methods are combined to provide a novel and powerful numerical procedure for systematically finding multiple local extrema of a multivariable objective function. This procedure can form part of a strategy for global optimization in which the greatest local maximum and least local minimum in the interior of a specified region are compared to the largest and smallest values of the objective function on the boundary of the region. The first trajectory method, a homotopy scheme, provides a globally convergent algorithm to find a stationary point of the objective function. The second trajectory method, a relaxation scheme, starts at one stationary point and systematically connects other stationary points in the specified region by a network of trjectories. It is noted that both generalized trajectory methods actually solve the stationarity conditions, and so they can also be used to find multiple roots of a set of nonlinear equations.     

扁柱面网壳的非线性动力学行为

用连续化法建立了正三角形网格的三向单层扁柱面网壳的非线性动力学方程和协调方程．在两对边简支条件下用分离变量函数法给出扁柱面网壳的横向位移．由协调方程求出张力,通过Galerkin作用得到了一个含二次、三次的非线性动力学微分方程．通过求Floquet指数讨论平衡点邻域的稳定性,用复变函数留数理论求出Melnikov函数,可得到该动力学系统发生混沌运动的临界条件．通过数值计算模拟和Poincaré映射也证明了混沌运动存在．     

On the Definition of a Minimum in Parameter Optimization

For the unconstrained minimization of an ordinary function, there are essentially two definitions of a minimum. The first involves the inequality \(\le \), and the second, the inequality <. The purpose of this Forum is to discuss the consequences of using these definitions for finding local and global minima of the constant objective function. The first definition says that every point on the constant function is a local minimum and maximum, as well as a global minimum and maximum. This is not a rational result. On the other hand, the second definition says that the constant function cannot be minimized in an unconstrained problem. It must be treated as a constrained problem where the constant function is the lower boundary of the feasible region. This is a rational result. As a consequence, it is recommended that the standard definition (\(\le \)) for a minimum be replaced by the second definition (<).     

An algorithm for linearly constrained nonlinear programming problems

In this paper an algorithm for solving a linearly constrained nonlinear programming problem is developed. Given a feasible point, a correction vector is computed by solving a least distance programming problem over a polyhedral cone defined in terms of the gradients of the “almost” binding constraints. Mukai's approximate scheme for computing the step size is generalized to handle the constraints. This scheme provides an estimate for the step size based on a quadratic approximation of the function. This estimate is used in conjunction with Armijo line search to calculate a new point. It is shown that each accumulation point is a Kuhn-Tucker point to a slight perturbation of the original problem. Furthermore, under suitable second order optimality conditions, it is shown that eventually only one trial is needed to compute the step size.