A new approach to constrained optimization via image space analysis

In this article, by introducing a class of nonlinear separation functions, the image space analysis is employed to investigate a class of constrained optimization problems. Furthermore, the equivalence between the existence of nonlinear separation function and a saddle point condition for a generalized Lagrangian function associated with the given problem is proved.     

Some applications of the image space analysis to the duality theory for constrained extremum problems

By means of the Image Space Analysis, duality properties of a constrained extremum problem are investigated. The analysis of the lower semicontinuity of the perturbation function, related to a right-hand side perturbation of the given problem, leads to a characterization of zero duality gap in the image space.     

An implicit function approach to constrained optimization with applications to asymptotic expansions

In this article, an unconstrained Taylor series expansion is constructed for scalar-valued functions of vector-valued arguments that are subject to nonlinear equality constraints. The expansion is made possible by first reparameterizing the constrained argument in terms of identified and implicit parameters and then expanding the function solely in terms of the identified parameters. Matrix expressions are given for the derivatives of the function with respect to the identified parameters. The expansion is employed to construct an unconstrained Newton algorithm for optimizing the function subject to constraints.Parameters in statistical models often are estimated by solving statistical estimating equations. It is shown how the unconstrained Newton algorithm can be employed to solve constrained estimating equations. Also, the unconstrained Taylor series is adapted to construct Edgeworth expansions of scalar functions of the constrained estimators. The Edgeworth expansion is illustrated on maximum likelihood estimators in an exploratory factor analysis model in which an oblique rotation is applied after Kaiser row-normalization of the factor loading matrix. A simulation study illustrates the superiority of the two-term Edgeworth approximation compared to the asymptotic normal approximation when sampling from multivariate normal or nonnormal distributions.     

Nonlinear separation functions and constrained extremum problems

In this paper, the image space analysis is employed to investigate constrained extremum problems. A new nonlinear separation function for the constrained extremum problems is given. Some optimality conditions and a strong duality theorem for the constrained extremum problem are obtained. These results extend and improve the corresponding ones in the literature.     

Quasi-equilibrium problems with applications to infinite optimization and constrained games in general topological spaces

By applying a new Fan-Browder type fixed point point theorem due to author, an existence theorem of quasi-equilibrium problem is proved in general topological space. As applications, some existence theorems of solutions for noncompact infinite optimization problems and noncompact constrained game problems are obtained in general topological spaces. These theorems improve and generalize a number of important results in recent literature.     

Continuity of the null space basis and constrained optimization

Many constrained optimization algorithms use a basis for the null space of the matrix of constraint gradients. Recently, methods have been proposed that enable this null space basis to vary continuously as a function of the iterates in a neighborhood of the solution. This paper reports results from topology showing that, in general, there is no continuous function that generates the null space basis of all full rank rectangular matrices of a fixed size. Thus constrained optimization algorithms cannot assume an everywhere continuous null space basis. We also give some indication of where these discontinuities must occur. We then propose an alternative implementation of a class of constrained optimization algorithms that uses approximations to the reduced Hessian of the Lagrangian but is independent of the choice of null space basis. This approach obviates the need for a continuously varying null space basis.Research supported by NSF grant MCS 81-15475 and DCR-8403483Research supported by ARO contracts DAAG 29-81-K-0108 and DAAG 29-84-K-0140     

Conjugate dual problems in constrained set-valued optimization and applications

In this paper, two conjugate dual problems are proposed by considering the different perturbations to a set-valued vector optimization problem with explicit constraints. The weak duality, inclusion relations between the image sets of dual problems, strong duality and stability criteria are investigated. Some applications to so-called variational principles for a generalized vector equilibrium problem are shown.     

Regularity conditions for constrained extremum problems via image space

Exploiting the image-space approach, we give an overview of regularity conditions. A notion of regularity for the image of a constrained extremum problem is given. The relationship between image regularity and other concepts is also discussed. It turns out that image regularity is among the weakest conditions for the existence of normal Lagrange multipliers.     

Higher order separation and comparison theorems,with applications to solution space problems

Summary General separation and comparison theorems for n-th order ordinary linear equations are established, which reduce to the Sturm theorems when the operator is self-adjoint and n is an even integer. The study of solution spaces of third order equations and their adjoints, as begun by Dolan (J.D.E.,7 (1970),pp. 367–388), is completed here, with structure theorems for special kinds of solution spaces, and the presentation of two examples: 1) an equation Lu=0 of order three with all solutions of Lu=0 and L*v=0 oscillatory, 2) an equation Lu=0 of order three such that all two-dimensional subspaces of the solution space contain both oscillatory and non-oscillatory solutions. The general separation theorem is used to obtain example 1), and the structure theorems for solution spaces are used to obtain example 2). Entrata in Redazione il 6 febbraio 1972.     

Function space interior point methods for PDE constrained optimization

A primal‐dual interior point method for optimal control problems with PDE constraints is considered. The algorithm is directly applied to the infinite dimensional problem. Existence and convergence of the central path are analyzed. Numerical results from an inexact continuation method applied to a model problem are shown. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Constraint qualifications for constrained Lipschitz optimization problems and applications to a MPCC

Three constraint qualifications (the weak generalized Robinson constraint qualification, the bounded constraint qualification, and the generalized Abadie constraint qualification), which are weaker than the generalized Robinson constraint qualification (GRCQ) given by Yen (1997) [1], are introduced for constrained Lipschitz optimization problems. Relationships between those constraint qualifications and the calmness of the solution mapping are investigated. It is demonstrated that the weak generalized Robinson constraint qualification and the bounded constraint qualification are easily verifiable sufficient conditions for the calmness of the solution mapping, whereas the proposed generalized Abadie constraint qualification, described in terms of graphical derivatives in variational analysis, is weaker than the calmness of the solution mapping. Finally, those constraint qualifications are written for a mathematical program with complementarity constraints (MPCC), and new constraint qualifications ensuring the C-stationary point condition of a MPCC are obtained.     

Infinite-dimensional convex programming with applications to constrained approximation

In this paper, existence and characterization of solutions and duality aspects of infinite-dimensional convex programming problems are examined. Applications of the results to constrained approximation problems are considered. Various duality properties for constrained interpolation problems over convex sets are established under general regularity conditions. The regularity conditions are shown to hold for many constrained interpolation problems. Characterizations of local proximinal sets and the set of best approximations are also given in normed linear spaces.The author is grateful to the referee for helpful suggestions which have contributed to the final preparation of this paper. This research was partially supported by Grant A68930162 from the Australian Research Council.     

A Kronecker approximation with a convex constrained optimization method for blind image restoration

In many problems of linear image restoration, the point spread function is assumed to be known even if this information is usually not available. In practice, both the blur matrix and the restored image should be performed from the observed noisy and blurred image. In this case, one talks about the blind image restoration. In this paper, we propose a method for blind image restoration by using convex constrained optimization techniques for solving large-scale ill-conditioned generalized Sylvester equations. The blur matrix is approximated by a Kronecker product of two matrices having Toeplitz and Hankel forms. The Kronecker product approximation is obtained from an estimation of the point spread function. Numerical examples are given to show the efficiency of our proposed method.     

Optimality conditions for vector optimization problems with non-cone constraints in image space

ABSTRACT

In this paper, we employ the image space analysis method to investigate a vector optimization problem with non-cone constraints. First, we use the linear and nonlinear separation techniques to establish Lagrange-type sufficient and necessary optimality conditions of the given problem under convexity assumptions and generalized Slater condition. Moreover, we give some characterizations of generalized Lagrange saddle points in image space without any convexity assumptions. Finally, we derive the vectorial penalization for the vector optimization problem with non-cone constraints by a general way.     

Nonconvex separation theorems and some applications in vector optimization

Separation theorems for an arbitrary set and a not necessarily convex set in a linear topological space are proved and applied to vector optimization. Scalarization results for weakly efficient points and properly efficient points are deduced.     

A hybrid descent method for solving a convex constrained optimization problem with applications

The purpose of this paper is to introduce a hybrid descent algorithm for finding a common point in fixed‐point sets of quasi‐nonexpansive mappings and solution sets of variational inequality problems. In the framework of Hilbert spaces, the strong convergence of the hybrid descent algorithm is established. Numerical experiments for the bandwidth allocation, which demonstrate the effectiveness, performance, and convergence of the proposed algorithm, are provided.     

On the image of a constrained extremum problem and some applications to the existence of a minimum

A new approach to constrained optimization, which has appeared recently under various forms and in several contexts, is presented in a general and unifying setting. This approach is then employed to establish some new conditions for the existence of the minimum of a constrained minimum problem.     

Second-order sequence-based necessary optimality conditions in constrained nonsmooth vector optimization and applications

Several notions of sequential directional derivatives and sequential local approximations are introduced. Under (first-order) Hadamard differentiability assumptions of the data at the point of study, these concepts are utilized to analyze second-order necessary optimality conditions, which rely on given sequences, for local weak solutions in nonsmooth vector optimization problems with constraints. Some applications to minimax programming problems are also derived.     

On constrained optimization with nonconvex regularization

Numerical Algorithms - In many engineering applications, it is necessary to minimize smooth functions plus penalty (or regularization) terms that violate smoothness and convexity. Specific...