Some characterizations of duality for DC optimization with composite functions

In this paper, by virtue of the epigraph technique, we first introduce some new regularity conditions and then obtain some complete characterizations of the Fenchel–Lagrange duality and the stable Fenchel–Lagrange duality for a new class of DC optimization involving a composite function. Moreover, we apply the strong and stable strong duality results to obtain some extended (stable) Farkas lemmas and (stable) alternative type theorems for this DC optimization problem. As applications, we obtain the corresponding results for a composed convex optimization problem, a DC optimization problem, and a convex optimization problem with a linear operator, respectively.     

Solving Inverse Problems for Hyperbolic Equations via the Regularization Method

In the paper, we first deduce an optimization problem from an inverse problem for a general operator equation and prove that the optimization problem possesses a unique, stable solution that converges to the solution of the original inverse problem, if it exists, as a regularization factor goes to zero. Secondly, we apply the above results to an inverse problem determining the spatially varying coefficients of a second order hyperbolic equation and obtain a necessary condition, which can be used to get an approximate solution to the inverse problem.     

A nonlinear eigenvalue problem arising in a nanostructured quantum dot

In this paper we investigate a minimization problem related to the principal eigenvalue of the s-wave Schrödinger operator. The operator depends nonlinearly on the eigenparameter. We prove the existence of a solution for the optimization problem and the uniqueness will be addressed when the domain is a ball. The optimized solution can be applied to design new electronic and photonic devices based on the quantum dots.     

Analysis of a Degenerate Obstacle Problem on an Unbounded Set Arising in the Environment

We study a class of optimization dynamics problems related to investment under uncertainty. The general model problem is reformulated in terms of an obstacle problem associated to a second-order elliptic operator which is not in divergence form. The spatial domain is unbounded and no boundary conditions are a priori specified. By using the special structure of the differential operator and the spatial domain, and some approximating arguments, we show the existence and uniqueness of a solution of the problem. We also study the regularity of the solution and give some estimates on the location of the coincidence set.     

Analysis of a Degenerate Obstacle Problem on an Unbounded Set Arising in the Environment

We study a class of optimization dynamics problems related to investment under uncertainty. The general model problem is reformulated in terms of an obstacle problem associated to a second-order elliptic operator which is not in divergence form. The spatial domain is unbounded and no boundary conditions are a priori specified. By using the special structure of the differential operator and the spatial domain, and some approximating arguments, we show the existence and uniqueness of a solution of the problem. We also study the regularity of the solution and give some estimates on the location of the coincidence set.     

The Matrix Multidisk Problem

The solutions of a class of matrix optimization problems (including the Nehari problem and its multidisk generalization) can be identified with the solutions of an abstract operator equation of the form T(., ., .) = 0. This equation can be solved numerically by Newton's method if the differential T' of T is invertible at the points of interest. This is typically too difficult to verify. However, it turns out that under reasonably broad conditions we can identify T' as the sum of a block Toeplitz operator and a compact block Hankel operator. Moreover, we can show that the block Toeplitz operator is a Fredholm operator and and in some cases can calculate its Fredholm index. Thus, T' will also be a Fredholm operator of the same index. In a number of cases that have been checked todate, numerical methods perform well when the Fredholm index is equal to zero and poorly otherwise. The main focus of this paper is on the multidisk problem alluded to above. However, a number of analogies with existing work on matrix optimization have been worked out and incorporated. Submitted: April 23, 2002.     

Optimization on class of operator equations in the probabilistic case setting

In this paper, we introduce a problem of the optimization of approximate solutions of operator equations in the probabilistic case setting, and prove a general result which connects the relation between the optimal approximation order of operator equations with the asymptotic order of the probabilistic width. Moreover, using this result, we determine the exact orders on the optimal approximate solutions of multivariate Preldholm integral equations of the second kind with the kernels belonging to the multivariate Sobolev class with the mixed derivative in the probabilistic case setting.     

Fréchet subdifferentials of efficient point multifunctions in parametric vector optimization

In this paper, we establish new formulae for computing and/or estimating the Fréchet subdifferential of the efficient point multifunction of a parametric vector optimization problem. These formulae are presented in a broad class of conventional vector optimization problems with the presence of geometric, operator and (finite and infinite) functional constraints.     

Reconstruction of Mobilities for Electrons and Holes in Semiconductors

From a simplified approximate semiconductor model, we develop a 1-D identification problem to recover the mobilities for electrons and holes in semiconductors based on the LBIC technique, and cast it as an optimization problem. Its solution is defined by the minimal point of some objective functional. On this argumentation, we derive the gradient operator of objective functional and the necessary condition for the solution of inverse problem. Our result provides a numerical approach to reconstruct the mobilities for electrons and holes in semiconductors.     

Galerkin method for optimal control of second‐order evolution equations

This paper presents the Galerkin approximation of the optimization problem of a system governed by non‐linear second‐order evolution equation where a non‐linear operator depends on derivative of the state of the system. The control is acting on a non‐linear equation. After giving some results on the existence of optimal control we shall prove the existence of the weak and strong condensation points of a set of solutions of the approximate optimization problems. Each of these points is a solution of the initial optimization problem. Finally we shall give a simple example using the obtained results. Copyright © 2004 John Wiley & Sons, Ltd.     

Variational analysis of circular cone programs

This paper conducts variational analysis of circular programs, which form a new class of optimization problems in nonsymmetric conic programming, important for optimization theory and its applications. First, we derive explicit formulas in terms of the initial problem data to calculate various generalized derivatives/co-derivatives of the projection operator associated with the circular cone. Then we apply generalized differentiation and other tools of variational analysis to establish complete characterizations of full and tilt stability of locally optimal solutions to parameterized circular programs.     

Stability and Regularization of Vector Problems of Integer Linear Programming

In this article we consider the problem of finding the Pareto set, and also the problem of lexicographic optimization. We study several types of stability, understood as preservation of certain properties of the efficient solution set under "small" changes of input data. The borders of such changes are ascertained. Necessary and sufficient conditions of stability are specified. A regularizing operator is proposed for transferring a probably unstable problem to a series of stable ones.     

Optimality conditions for a Timoshenko beam equation with periodic constraint

This article is concerned with an optimization problem governed by a Timoshenko beam equation with periodic constraints. Firstly, a detail spectral analysis is conducted for the Timoshenko operator related to the homogeneous system. Then, we derive a closed range property of the state equation. Finally, we present the main results: the optimality conditions and the maximum principle for our problem via the closed range theorem.     

多元积分方程自适应解法的优化

考虑核属于各向异性的Sobolev类的积分方程自适应直接方法的优化,得到误差阶的精确估计及相应的最优方法.     

On the minimal displacement vector of the Douglas–Rachford operator

The Douglas–Rachford algorithm can be represented as the fixed point iteration of a firmly nonexpansive operator. When the operator has no fixed points, the algorithm’s iterates diverge, but the difference between consecutive iterates converges to the so-called minimal displacement vector, which can be used to certify infeasibility of an optimization problem. In this paper, we establish new properties of the minimal displacement vector, which allow us to generalize some existing results.     

基于迭代投影的梯度硬阈值追踪算法

梯度硬阈值追踪算法是求解稀疏优化问题的有效算法之一.考虑到算法中投影对最优解的影响,提出一种比贪婪策略更好的投影算法是很有必要的.针对一般的稀疏约束优化问题,利用整数规划提出一种迭代投影策略,将梯度投影算法中的投影作为一个子问题求解.通过迭代求解该子问题得到投影的指标集,并以此继续求解原问题,以提高梯度硬阈值追踪算法的计算效果.证明了算法的收敛性,并通过数值实例验证了算法的有效性.     

The Forward–Backward Algorithm and the Normal Problem

The forward–backward splitting technique is a popular method for solving monotone inclusions that have applications in optimization. In this paper, we explore the behaviour of the algorithm when the inclusion problem has no solution. We present a new formula to define the normal solutions using the forward–backward operator. We also provide a formula for the range of the displacement map of the forward–backward operator. Several examples illustrate our theory.     

A new iterative algorithm for the variational inequality problem over the fixed point set of a firmly nonexpansive mapping

Many problems to appear in signal processing have been formulated as the variational inequality problem over the fixed point set of a nonexpansive mapping. In particular, convex optimization problems over the fixed point set are discussed, and operators which are considered to the problems satisfy the monotonicity. Hence, the uniqueness of the solution of the problem is not always guaranteed. In this article, we present the variational inequality problem for a monotone, hemicontinuous operator over the fixed point set of a firmly nonexpansive mapping. The main aim of the article is to solve the proposed problem by using an iterative algorithm. To this goal, we present a new iterative algorithm for the proposed problem and its convergence analysis. Numerical examples for the proposed algorithm for convex optimization problems over the fixed point set are provided in the final section.     

Fitness Landscape Analysis and Metaheuristics Efficiency

Landscape analysis has been identified as a promising way to develop efficient optimization methods. Nevertheless, the links between properties of the landscape and efficiency of methods is not easy to understand. In this article, we propose to give a contribution in this field using a vehicle routing problem as an illustration. Metaheuristics use a neighborhood operator that connects solutions of the search space. Thus, this operator acts on the dynamics of the search and impacts metaheuristics efficiency. Therefore, we characterize two landscapes differenciated by their neighborhood function and then, we analyze the performance of classical metaheuristics using one or the other neighborhood operator. Finally, a discussion provides insights on the relations between results of the landscape analysis and results of methods performance.     

Optimization of Approximate Solution of Integral Equations of Several Variables

In this paper, we consider the problem of optimization of adaptive direct methods of operator equations. Adaptivity of a direct method is understood in the sense that the subspace on the basis of which it is constructed is chosen depending on the operator of the concrete equation (otherwise, nonadaptive direct method is then concerned), which would essentially let us increase the precision. For some classes of the second kind of Fredhlom integral equations with anisotropic smooth kernels we determine the exact order of the error of adaptive direct methods, and we also give an optimal algorithm. Received April 13, 2001, Accepted September 13, 2001