On the Convergence of the P-Algorithm for One-Dimensional Global Optimization of Smooth Functions

The Wiener process is a widely used statistical model for stochastic global optimization. One of the first optimization algorithms based on a statistical model, the so-called P-algorithm, was based on the Wiener process. Despite many advantages, this process does not give a realistic model for many optimization problems, particularly from the point of view of local behavior. In the present paper, a version of the P-algorithm is constructed based on a stochastic process with smooth sampling functions. It is shown that, in such a case, the algorithm has a better convergence rate than in the case of the Wiener process. A similar convergence rate is proved for a combination of the Wiener model-based P-algorithm with quadratic fit-based local search.     

On a Characterization of Spaces of Differentiable Functions

In this paper, we generalize Bernstein's theorem characterizing the space by means of local approximations. The closed interval is partitioned into disjoint half-intervals on which best approximation polynomials of degree divided by the lengths of these half-intervals taken to the power are considered. The existence of the limits of these ratios as the lengths of the half-intervals tend to zero is a criterion for the existence of the th derivative of a function. We prove the theorem in a stronger form and extend it to the spaces .     

Minimizing Increasing Star-shaped Functions Based on Abstract Convexity

We study a broad class of increasing non-convex functions whose level sets are star shaped with respect to infinity. We show that these functions (we call them ISSI functions) are abstract convex with respect to the set of min-type functions and exploit this fact for their minimization. An algorithm is proposed for solving global optimization problems with an ISSI objective function and its numerical performance is discussed.     

广义拟牛顿算法对一般目标函数的收敛性

本文证明了求解无约束最优化的广义拟牛顿算法在Goldstein非精确线搜索下对一般目标函数的全局收敛性，并在一定条件下证明了算法的局部超线性收敛性。     

An adaptive stochastic global optimization algorithm for one-dimensional functions

In this paper a new algorithm is proposed, based upon the idea of modeling the objective function of a global optimization problem as a sample path from a Wiener process. Unlike previous work in this field, in the proposed model the parameter of the Wiener process is considered as a random variable whose conditional (posterior) distribution function is updated on-line. Stopping criteria for Bayesian algorithms are discussed and detailed proofs on finite-time stopping are provided.This research has been partially supported by Progetto MURST 40% Metodi di Ottimizzazione per le Decisioni.     

On a Class of Right Linearly Differentiable Functions of Cayley-Dickson Variables

The article is devoted to weak super-differentiations of functions of Cayley-Dickson variables. Also a family of general normed algebras A with involution over infinite fields is taken into account. The non-trivial right A-linear weak super-differentiability of non-linear functions is investigated. Several theorems about their existence and families are proved.     

Global Convergence of a Class of Discrete-Time Interconnected Pendulum-Like Systems

This paper focuses on a class of discrete-time interconnected pendulum-like systems. Sufficient conditions for the global convergence of systems with both structured and unstructured uncertainties in its linear part are established in terms of linear matrix inequalities (LMIs). In the traditional decentralized control of large scale systems, the effects of interconnections were seldom studied. In this paper, a square matrix specifying the interconnecting structure is introduced in order to discuss the effects of nonlinear interconnections. It is shown that the global convergence of the whole interconnected system can be achieved by designing an appropriate interconnection matrix. In order to solve the nonlinear matrix inequalities (NMIs) arising in the synthesis problem, a global optimization algorithm is presented which can be used to handle a class of NMI problems. An illustrative example is given to demonstrate the applicability and validity of the main results and the presented algorithm. This work was supported by the National Science Foundation of China under Grants 60404007, 60334030 and the Foundation of Engineering Research Institute of Peking University under Grant 204035.     

Internal modelling of objective functions for global optimization

Global optimization requires an adequate internal representation of the objective function for success in a reasonable number of function evaluations. A method for determining the location of a new function evaluation, based on a representation using a stationary stochastic process model, is investigated and some results are given.     

On Harmonic Continuation of Differentiable Functions Defined on Part of the Boundary

We establish an explicit formula for reconstruction of a harmonic function in a domain from its values and the values of its normal derivative on part of the boundary; i.e., we give an explicit solution to the Cauchy problem for the Laplace equation.     

On Harmonic Continuation of Differentiable Functions Defined on Part of the Boundary

We establish an explicit formula for reconstruction of a harmonic function in a domain from its values and the values of its normal derivative on part of the boundary; i.e., we give an explicit solution to the Cauchy problem for the Laplace equation.     

On large increments of a two-parameter fractional Wiener process

In this paper, how big the increments are and some liminf behaviors are studied of a two-parameter fractional Wiener process. The results are based on some inequalities on the suprema of this process, which also are of independent interest     

On a Partially Observed Illness-Death Model

A biological population with N individuals assumed to be healthy is monitored over time. A portion of these individuals die from natural causes while other may get infected with a disease and become sick. A number of the sick individuals will then die from natural causes and other may die from the disease. A stochastic model for the various transitions ‘healthy, sick, death’, is studied where it is assumed that the only observed states are deaths. Based on these information, optimal filters for the number of individuals in each state are derived. This revised version was published online in August 2006 with corrections to the Cover Date.     

On the Representation of Infinitely Differentiable Functions by Series of Exponentials

We consider the problem of representing elements of a weighted space of infinitely differentiable functions on the real line by series of exponentials.     

Convergence and Application of a Decomposition Method Using Duality Bounds for Nonconvex Global Optimization

The subject of this article is a class of global optimization problems, in which the variables can be divided into two groups such that, in each group, the functions involved have the same structure (e.g. linear, convex or concave, etc.). Based on the decomposition idea of Benders (Ref. 1), a corresponding master problem is defined on the space of one of the two groups of variables. The objective function of this master problem is in fact the optimal value function of a nonlinear parametric optimization problem. To solve the resulting master problem, a branch-and-bound scheme is proposed, in which the estimation of the lower bounds is performed by applying the well-known weak duality theorem in Lagrange duality. The results of this article concentrate on two subjects: investigating the convergence of the general algorithm and solving dual problems of some special classes of nonconvex optimization problems. Based on results in sensitivity and stability theory and in parametric optimization, conditions for the convergence are established by investigating the so-called dual properness property and the upper semicontinuity of the objective function of the master problem. The general algorithm is then discussed in detail for some nonconvex problems including concave minimization problems with a special structure, general quadratic problems, optimization problems on the efficient set, and linear multiplicative programming problems.     

On the Continuity of the Generalized Nemytskii Operator on Spaces of Differentiable Functions

We obtain sufficient conditions for the continuity of the general nonlinear superposition operator (generalized Nemytskii operator) acting from the space of differentiable functions on a bounded domain to the Lebesgue space . The values of operators on a function are locally determined by the values of both the function itself and all of its partial derivatives up to order inclusive. In certain particular cases, the sufficient conditions obtained are proved to be necessary as well. The results are illustrated by several examples, and an application to the theory of Sobolev spaces is also given.     

BFGS算法对非凸函数优化问题的收敛性

ＢＦＧＳ算法是无约束最优化中最著名的数值算法之一,对非凸函数ＢＦＧＳ算法是否具有整体收敛性,这是一个ｏｐｅｎ问题,本文考虑Ｗｏｌｆｏ线搜索下目标函数非凸的ＢＦＧＳ算法,我们给出一个使该算法收敛的充分条件。     

Global Convergence Analysis of a New Nonmonotone BFGS Algorithm on Convex Objective Functions

In this paper, a new nonmonotone BFGS algorithmfor unconstrained optimization is introduced. Under mild conditions,the global convergence of this new algorithm on convex functions isproved. Some numerical experiments show that this new nonmonotoneBFGS algorithm is competitive to the BFGS algorithm.     

一类锥模型非单调信赖域算法及收敛性分析

本文对无约束优化问题提出了一类基于锥模型的非单调信赖域算法.二次模型非单调信赖域算法是新算法的特例.在适当的条件下,证明了算法的全局收敛性及Q-二次收敛性.     

An Algorithm of Global Optimization for Rational Functions with Rational Constraints

To solve a system of nonlinear equations, Wu wen-tsun introduced a new formative elimination method. Based on Wu's method and the theory of nonlinear programming, we here propose a global optimization algorithm for nonlinear programming with rational objective function and rational constraints. The algorithm is already programmed and the test results are satisfactory with respect to precision and reliability.     

New Classes of Globally Convexized Filled Functions for Global Optimization

We propose new classes of globally convexized filled functions. Unlike the globally convexized filled functions previously proposed in literature, the ones proposed in this paper are continuously differentiable and, under suitable assumptions, their unconstrained minimization allows to escape from any local minima of the original objective function. Moreover we show that the properties of the proposed functions can be extended to the case of box constrained minimization problems. We also report the results of a preliminary numerical experience.