Inverse problem for a class of one-dimensional wave equations with piecewise constant coefficients

We consider the problem of reconstructing the piecewise constant coefficient of a one-dimensional wave equation on the halfline from the knowledge of the displacement on the boundary caused by an impulse at time zero. This problem is formulated as a nonlinear optimization problem. The objective function of this optimization problem has several special features that have been exploited in building an ad hoc optimization method. The optimization method is based on the solution of a nonlinear system of equations by an algorithm consisting of the evaluation of the unknowns one by one.The research of the third author has been made possible through the support and sponsorship of the Italian Government through the Ministero Pubblica Istruzione under Contract M.P.I. 60% 1987 at the Università di Roma—La Sapienza.     

Global Method for Monotone Variational Inequality Problems with Inequality Constraints

We consider optimization methods for monotone variational inequality problems with nonlinear inequality constraints. First, we study the mixed complementarity problem based on the original problem. Then, a merit function for the mixed complementarity problem is proposed, and some desirable properties of the merit function are obtained. Through the merit function, the original variational inequality problem is reformulated as simple bounded minimization. Under certain assumptions, we show that any stationary point of the optimization problem is a solution of the problem considered. Finally, we propose a descent method for the variational inequality problem and prove its global convergence.     

Lower semicontinuity in domain optimization problems

In the present paper, the lower semicontinuity of certain classes of functionals is studied when the domain of integration, which defines the functionals, is not fixed. For this purpose, a certain class of domains introduced by Chenais is employed. For this class of domains, a basic lemma is proved that plays an essential role in the derivations of the lower-semicontinuity theorems. These theorems are applied to the study of the existence of the optimal domain in domain optimization problems; a boundary-value problem of Neumann type or Dirichlet type is the main constraint in these optimization problems.The author wishes to express his sincere thanks to the reviewer for his valuable comments, which made the paper more readable; the reviewer also pointed out that Lemma 2.1 in the text is a direct corollary to a lemma by Chenais (Ref. 9). He thanks Prof. Y. Sakawa of Osaka University for encouragement.     

Splitting-based conjugate gradient method for a multi-dimensional linear inverse heat conduction problem

In this paper we consider a multi-dimensional inverse heat conduction problem with time-dependent coefficients in a box, which is well-known to be severely ill-posed, by a variational method. The gradient of the functional to be minimized is obtained by the aid of an adjoint problem, and the conjugate gradient method with a stopping rule is then applied to this ill-posed optimization problem. To enhance the stability and the accuracy of the numerical solution to the problem, we apply this scheme to the discretized inverse problem rather than to the continuous one. The difficulties with large dimensions of discretized problems are overcome by a splitting method which only requires the solution of easy-to-solve one-dimensional problems. The numerical results provided by our method are very good and the techniques seem to be very promising.     

Coupling ant colony systems with strong local searches

Ant colony system is a well known metaheuristic framework, and many efficient algorithms for different combinatorial optimization problems have been derived from this general framework. In this paper some directions for improving the original framework when a strong local search routine is available, are identified. In particular, some modifications able to speed up the method and make it competitive on large problem instances, on which the original framework tends to be weaker, are described. The resulting framework, called Enhanced Ant Colony System is tested on three well-known combinatorial optimization problems arising in the transportation field. Many new best known solutions are retrieved for the benchmarks available for these optimization problems.     

The statement and numerical solution of an optimization problem in X-ray tomography

A nonclassical problem is considered for the transport equation with coefficients depending on the energy of radiation. The task is to find the discontinuity surfaces for the coefficients of the equation from measurements of the radiation flux leaving the medium. For this tomography problem, an optimization problem is stated and numerically analyzed. The latter consists in determining the radiation energy that ensures the best reconstruction of the unknown medium. A simplified optimization problem is solved analytically.     

On stationary points of the implicit Lagrangian for nonlinear complementarity problems

Mangasarian and Solodov have recently introduced an unconstrained optimization problem whose global minima are solutions of the nonlinear complementarity problem (NCP). In this paper, we show that, if the mapping involved in NCP has a positive-definite Jacobian, then any stationary point of the optimization problem actually solves NCP. We also discuss a descent method for solving the unconstrained optimization problem.The authors are indebted to a referee for a helpful suggestion that led them to develop the descent method described in Section 3. They are grateful to Professor F. Facchinei, who kindly pointed out an error in the proof of Theorem 2.3 in an earlier version of the paper. The also thank Professor P. Tseng for a discussion on Theorem 3.1.     

Unilateral Crack Identification: A Filter-Driven, Iterative, Boundary Element Approach

An identification problem for parametric variational inequalities and linear com-plementarity problems is solved here by means of iterative filter techniques. A concrete application in engineering mechanics, the unilateral crack identification problem, is solved. The elastic contact problem is formulated by boundary element-linear complementarity techniques. By means of numerical results and comparison with previous approaches based on optimization and neural networks it is shown that this method is advantageous. In view of the difficulty of the considered bilevel optimization problem, this approach may be of interest for other applications as well.     

Conjugate maps and duality in multiobjective optimization

This paper considers duality in convex vector optimization. A vector optimization problem requires one to find all the efficient points of the attainable value set for given multiple objective functions. Embedding the primal problem into a family of perturbed problems enables one to define a dual problem in terms of the conjugate map of the perturbed objective function. Every solution of the stable primal problem is associated with a certain solution of the dual problem, which is characterized as a subgradient of the perturbed efficient value map. This pair of solutions also provides a saddle point of the Lagrangian map.     

Duality for generalized equilibrium problem

We introduce a generalized equilibrium problem (GEP) that allow us to develop a robust dual scheme for this problem, based on the theory of conjugate functions. We obtain a unified dual analysis for interesting problems. Indeed, the Lagrangian duality for convex optimization is a particular case of our dual problem. We establish necessary and sufficient optimality conditions for GEP that become a well-known theorem given by Mosco and the dual results obtained by Morgan and Romaniello, which extend those introduced by Auslender and Teboulle for a variational inequality problem.     

A deep neural network-based method for solving obstacle problems

In this paper, we propose a method based on deep neural networks to solve obstacle problems. By introducing penalty terms, we reformulate the obstacle problem as a minimization optimization problem and utilize a deep neural network to approximate its solution. The convergence analysis is established by decomposing the error into three parts: approximation error, statistical error and optimization error. The approximate error is bounded by the depth and width of the network, the statistical error is estimated by the number of samples, and the optimization error is reflected in the empirical loss term. Due to its unsupervised and meshless advantages, the proposed method has wide applicability. Numerical experiments illustrate the effectiveness and robustness of the proposed method and verify the theoretical proof.     

Relaxation-based algorithms for minimax optimization problems with resource allocation applications

We consider minimax optimization problems where each term in the objective function is a continuous, strictly decreasing function of a single variable and the constraints are linear. We develop relaxation-based algorithms to solve such problems. At each iteration, a relaxed minimax problem is solved, providing either an optimal solution or a better lower bound. We develop a general methodology for such relaxation schemes for the minimax optimization problem. The feasibility tests and formulation of subsequent relaxed problems can be done by using Phase I of the Simplex method and the Farkas multipliers provided by the final Simplex tableau when the corresponding problem is infeasible. Such relaxation-based algorithms are particularly attractive when the minimax optimization problem exhibits additional structure. We explore special structures for which the relaxed problem is formulated as a minimax problem with knapsack type constraints; efficient algorithms exist to solve such problems. The relaxation schemes are also adapted to solve certain resource allocation problems with substitutable resources. There, instead of Phase I of the Simplex method, a max-flow algorithm is used to test feasibility and formulate new relaxed problems.Corresponding author.Work was partially done while visiting AT&T Bell Laboratories.     

Stochastic Global Optimization: Problem Classes and Solution Techniques

There is a lack of a representative set of test problems for comparing global optimization methods. To remedy this a classification of essentially unconstrained global optimization problems into unimodal, easy, moderately difficult, and difficult problems is proposed. The problem features giving this classification are the chance to miss the region of attraction of the global minimum, embeddedness of the global minimum, and the number of minimizers. The classification of some often used test problems are given and it is recognized that most of them are easy and some even unimodal. Global optimization solution techniques treated are global, local, and adaptive search and their use for tackling different classes of problems is discussed. The problem of fair comparison of methods is then adressed. Further possible components of a general global optimization tool based on the problem classes and solution techniques is presented.     

Infinite-dimensional quadratic optimization: Interior-point methods and control applications

An infinite-dimensional convex optimization problem with the linear-quadratic cost function and linear-quadratic constraints is considered. We generalize the interior-point techniques of Nesterov-Nemirovsky to this infinite-dimensional situation. The complexity estimates obtained are similar to finite-dimensional ones. We apply our results to the linear-quadratic control problem with quadratic constraints. It is shown that for this problem the Newton step is basically reduced to the standard LQ problem. This research was supported in part by the Cooperative Research Centre for Robust and Adaptive Systems while the first author visited the Australian National University and by NSF Grant DMS 94-23279.     

On the need for special purpose algorithms for minimax eigenvalue problems

It has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimization problems involving smooth objective and constraint functions. This result seems very appealing since minimax eigenvalue problems are known to be typically nondifferentiable. In this paper, we show, however, that general purpose nonlinear optimization algorithms usually fail to find a solution to these smooth problems even in the simple case of minimization of the maximum eigenvalue of an affine family of symmetric matrices, a convex problem for which efficient algorithms are available.This work was supported in part by NSF Engineering Research Centers Program No. NSFD-CDR-88-03012 and NSF Grant DMC-84-20740. The author wishes to thank Drs. M. K. H. Fan and A. L. Tits for their many useful suggestions.     

A General Linea-Quadratic Optimization Problem

A linear-quadratic optimization problem is formulated in a dynamic programming manner. An updating formula for obtaining the solutions to such a problem is provided and illustrated using a few simple examples. This updating formula is also compared to a well-known updating formula for obtaining the inverses of symmetric positive-definite matrices. Numerical results are given.     

二次规划逆问题的牛顿方法

针对二次规划逆问题,将其表达为带有互补约束的锥约束优化问题.借助于对偶理论,将问题转化为变量更少的线性互补约束非光滑优化问题.通过扰动的方法求解转化后的问题并证明了收敛性.采用非精确牛顿法求解扰动问题,给出了算法的全局收敛性与局部二阶收敛速度.最后通过数值实验验证了该算法的可行性.     

移动通信系统中的最优功率控制算法

本文研究了移动通信系统中的功率最优控制问题.我们首先将这一个工程问题转化为最大可满足线性不等式组问题的一个特殊情形,然后通过对这个组合优化问题的最优解的性质研究,给出了求解该问题的多项式时间算法.     

Necessary conditions for a domain optimization problem with various constraints

In this paper, a scheme for obtaining the necessary conditions in a wide class of domain optimization problems is given. In the case of a linear boundary-value problem of the Dirichlet type, necessary conditions are given.     

Stability in disjunctive linear optimization I: continuity of the feasible set

The aim of this paper is to study the continuous dependence of the feasible set of a disjunctive semi-infinite linear optimization problem on all involved parameters (matrix and right-hand side). The feasible set of such an optimization problem is the union of (a. possible infinite number of) convex sets, which each is described by a finite or an infinite number of strict and non-strict linear inequalities. We derive necessary and sufficient conditions for the upper- and lower-semi-continuity, and the closedness of the feasible-set-mapping Z Especially, the compactness of the boundary of the feasible set and the closedness of Z are equivalent to the upper-semi-continuity of Zwhile the lower semi-continuity of Z is equivalent to a certain constraint qualification. This constraint qualification is a strengthened kind of Slater condition, rrom tuese investigations, we derive known results in parametric semi-infinite optimization and parametric integer programming.