Conjugate duality for fractional programs

A very powerful approach to duality in mathematical programming is the theory of generalised geometric programming. Here we exploit this theory to develop a duality theory for fractional programs. All previous work on duality for such programs uses Lagrangian ideas.     

Duality via halfspaces

This note shows that half spaces play a very special role in the development of duality. In addition to the minimum norm duality, the duality in linear programming, and Wolfe's and Johri's formulations in nonlinear programming can all be derived via half spaces by following an identical five step procedure.     

Sufficient conditions for duality in homogeneous programming

A symmetric duality theory for programming problems with homogeneous objective functions was published in 1961 by Eisenberg and has been used by a number of authors since in establishing duality theorems for specific problems. In this paper, we study a generalization of Eisenberg's problem from the viewpoint of Rockafellar's very general perturbation theory of duality. The extension of Eisenberg's sufficient conditions appears as a special case of a much more general criterion for the existence of optimal vectors and lack of a duality gap. We give examples where Eisenberg's sufficient condition is not satisfied, yet optimal vectors exist, and primal and dual problems have the same value.     

Generalized S-Lemma and strong duality in nonconvex quadratic programming

On the basis of a new topological minimax theorem, a simple and unified approach is developed to Lagrange duality in nonconvex quadratic programming. Diverse generalizations as well as equivalent forms of the S-Lemma, providing a thorough study of duality for single constrained quadratic optimization, are derived along with new strong duality conditions for multiple constrained quadratic optimization. The results allow many quadratic programs to be solved by solving one or just a few SDP’s (semidefinite programs) of about the same size, rather than solving a sequence, often infinite, of SDP’s or linear programs of a very large size as in most existing methods.     

一类带反凸约束的非线性比式和问题的全局优化算法

本文针对一类带有反凸约束的非线性比式和分式规划问题,提出一种求其全局最优解的单纯形分支和对偶定界算法.该算法利用Lagrange对偶理论将其中关键的定界问题转化为一系列易于求解的线性规划问题.收敛性分析和数值算例均表明提出的算法是可行的.     

非常极凸空间

本文使用非常极凸的定义，证明了非常极凸和非常光滑是互为对偶空间且严格介于弱k凸和非常凸之间的空间，最后得到了非常极凸的一些特征．     

The determination of subvarieties of certain congruence-distributive varieties

This note concerns the problem of determining equational bases for the subvarieties of a congruence-distributive variety generated by a finite algebra. It describes a procedure, based on the duality for finite distributive lattices, which converts a local solution to this problem into a global one. For many varieties of distributive-lattice-ordered algebras a local solution of the required form can be obtained from a natural duality in which identities are very conveniently encoded.Dedicated to Bjarni Jonsson on his 70th birthdayPresented by G. McNulty.     

Duality results and proximal solutions of the Huber M-estimator problem

We investigate the interest of solving the Huber M-estimator problem by a proximal approach combined with duality theory. Three different duality schemes are developed. The first one which only deals with estimator determination yields useful information on the geometrical structure of the set of optimal solutions. The second scheme links together estimator determination and outliers detection while the third one only focuses on outliers separation. We show that these three duality schemes can be solved by the partial inverse method, i.e., a special instance of the basic proximal point algorithm, which leads to very simple updating rules. This method which is always globally convergent enjoys nice stability properties and permits parallel computations.     

A convex-like duality scheme for quasi-convex programs

This paper describes a symmetric duality relation for quasi-convex programs. We are able to strengthen previous results and to define necessary and sufficient conditions for the absence of duality gap. In the present scheme one can generate quasi-convex quasi-concave Lagrangians and discuss the correspondence between saddle points of the Lagrangians and the solutions to the dual and primal programs. The present scheme is very similar to Rockafellar's scheme for convex programs and in this sense it may be viewed as a unified approach. Several examples are also given.     

Lagrange Duality in Set Optimization

Based on the complete-lattice approach, a new Lagrangian type duality theory for set-valued optimization problems is presented. In contrast to previous approaches, set-valued versions for the known scalar formulas involving infimum and supremum are obtained. In particular, a strong duality theorem, which includes the existence of the dual solution, is given under very weak assumptions: The ordering cone may have an empty interior or may not be pointed. “Saddle sets” replace the usual notion of saddle points for the Lagrangian, and this concept is proven to be sufficient to show the equivalence between the existence of primal/dual solutions and strong duality on the one hand, and the existence of a saddle set for the Lagrangian on the other hand. Applications to set-valued risk measures are indicated.     

Very Convex Banach Spaces

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Constrained Information Theoretic Characterizations in Consumer Purchase Behaviour

The analysis called SANDDABS which has had a long history of use in consumer purchase behaviour (including brand switching) at MRCA and elsewhere, is given a constrained information theoretic characterization. This is made possible by recent developments in optimization and duality which are precise and general. That is, these developments generalize the classical relations between information theory and statistics as previously established in very precise forms by Khinchin-Kullback-Leibler (and others). Contacts with geometric programming in an extended form along with other recent developments in optimization and duality are also supplied and a way is thereby opened for unifying many apparently separate approaches to market analysis.     

Surrogate duality in a branch-and-bound procedure for integer programming

The existence of efficient techniques such as subgradient search for solving Lagrangean duals has led to some very successful applications of Lagrangean duality in solving specially structured discrete problems. While surrogate duals have been theoretically shown to provide stronger bounds, the complexity of surrogate dual multiplier search has discouraged their employment in solving integer programs. We have recently suggested a new strategy for computing surrogate dual values that allows us to directly use established Lagrangean search methods for exploring surrogate dual multipliers. This paper considers the problem of incorporating surrogate duality within a branch-and-bound procedure for solving integer programming problems. Computational experience with randomly generated multiconstraint knapsack problems is also reported.     

Sandwich theorem for quasiconvex functions and its applications

In convex programming, sandwich theorem is very important because it is equivalent to Fenchel duality theorem. In this paper, we investigate a sandwich theorem for quasiconvex functions. Also, we consider some applications for quasiconvex programming.     

Abstract convexity:examples and applications

This paper presents a survey of some results from and applications of abstract convexity based on the notions of Minkowski duality, supremal generators, subdifferentials and conjugations. The paper contains very many examples, which are its essential part     

A simplicial branch and duality bound algorithm for the sum of convex–convex ratios problem

This article presents a simplicial branch and duality bound algorithm for globally solving the sum of convex–convex ratios problem with nonconvex feasible region. To our knowledge, little progress has been made for globally solving this problem so far. The algorithm uses a branch and bound scheme where the Lagrange duality theory is used to obtain the lower bounds. As a result, the lower-bounding subproblems during the algorithm search are all ordinary linear programs that can be solved very efficiently. It has been proved that the algorithm possesses global convergence. Finally, the numerical experiments are given to show the feasibility of the proposed algorithm.     

Every topological category is convenient for Gelfand duality

In this paper we generalize our work on Gelfand dualities in cartesian closed topological categories [42] to categories which are only monoidally closed. Using heavily enriched category theory we show that under very mild conditions on the base category function algebra functor and spectral space functor exist, forming a pair of adjoint functors and establishing a duality between function algebras and spectral spaces. Using recent results in connection with semitopological functors, we show that every (E,M)-topological category is endowed with at least oneconvenient monoidal structure admitting a generalized Gelfand duality. So it turns out that there is no need for a cartesian closed structure on a topological category in order to study generalized Gelfand-Naimark dualities.     

A duality theorem for generalized local cohomology

We prove a duality theorem for graded algebras over a field that implies several known duality results: graded local duality, versions of Serre duality for local cohomology and of Suzuki duality for generalized local cohomology, and Herzog-Rahimi bigraded duality.

     

Unified Duality Theory for Constrained Extremum Problems. Part I: Image Space Analysis

This paper is concerned with a unified duality theory for a constrained extremum problem. Following along with the image space analysis, a unified duality scheme for a constrained extremum problem is proposed by virtue of the class of regular weak separation functions in the image space. Some equivalent characterizations of the zero duality property are obtained under an appropriate assumption. Moreover, some necessary and sufficient conditions for the zero duality property are also established in terms of the perturbation function. In the accompanying paper, the Lagrange-type duality, Wolfe duality and Mond–Weir duality will be discussed as special duality schemes in a unified interpretation. Simultaneously, three practical classes of regular weak separation functions will be also considered.     

On Reducing a Resource Allocation Problem to a Single One-Dimensional Minimization of a Differentiable Convex Function

By very elementary arguments on Lagrangian duality it is shown that the classical resource allocation problem can be reduced to a single one-dimensional minimization of a differentiable convex function. An optimality condition is given that can be used for testing optimality of some proposed heuristic solutions.