On duality for square root convex programs

Conjugate function theory is used to develop dual programs for nonseparable convex programs involving the square root function. This function arises naturally in finance when one measures the risk of a portfolio by its variance–covariance matrix, in stochastic programming under chance constraints and in location theory.     

Lagrange-type duality in DC programming

We show a Lagrange-type duality theorem for a DC programming problem, which is a generalization of previous results by J.-E. Martínez-Legaz, M. Volle [5] and Y. Fujiwara, D. Kuroiwa [1] when all constraint functions are real-valued. To the purpose, we decompose the DC programming problem into certain infinite convex programming problems.     

A note about linear inequality systems and duality

The optimization of a linear function on a closed convex set,F, can be stated as a linear semi-infinite program, sinceF is the solution set of (usually) infinite linear inequality systems, the so-called linear representations ofF. The duality properties of these programs are analyzed when the linear representation ofF ranges in some well known classes of linear inequality systems. This paper provides propositions on the duality diagrams of Farkas-Minkowski, canonically closed, compact and closed systems. Converse statements are also given.

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Zusammenfassung Die Optimierung einer linearen Funktion auf einer konvexen abgeschlossenen MengeF kann als semi-infinites lineares Programm aufgefaßt werden, daF als Durchschnitt (unendlich) vieler Halbräume dargestellt werden kann. Es werden Dualitätseigenschaften dieser Programme untersucht, wobei von verschiedenen linearen Darstellungen fürF ausgegangen wird. Die Arbeit enthält Sätze über Dualitätsbeziehungen von Farkas-Minkowski, kanonisch abgeschlossene, kompakte und abgeschlossene Systeme. Es werden auch umgekehrte Beziehungen angegeben.

     

Convex approximations to sparse PCA via Lagrangian duality

We derive a convex relaxation for cardinality constrained Principal Component Analysis (PCA) by using a simple representation of the L₁ unit ball and standard Lagrangian duality. The resulting convex dual bound is an unconstrained minimization of the sum of two nonsmooth convex functions. Applying a partial smoothing technique reduces the objective to the sum of a smooth and nonsmooth convex function for which an efficient first order algorithm can be applied. Numerical experiments demonstrate its potential.     

Fenchel-type duality for matroid valuations

The weighted matroid intersection problem has recently been extended to the valuated matroid intersection problem: Given a pair of valuated matroidsM _i = (V, _i , _i ) (i = 1,2) defined on a common ground setV, find a common baseB _{1 2} that maximizes ₁ (B) + ₂ (B). This paper develops a Fenchel-type duality theory related to this problem with a view to establishing a convexity framework for nonlinear integer programming. A Fenchel-type min max theorem and a discrete separation theorem are given. Furthermore, the subdifferentials of matroid valuations are investigated. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.Part of this paper has been presented at fifth SIAM Conference on Optimization, Victoria, May 1996.This work was done while the author was at Forschungsinstitut für Diskrete Mathematik, Universität Bonn, 1994–1995.     

Wolfe duality and Mond-Weir duality via perturbations

Considering a general optimization problem, we attach to it by means of perturbation theory two dual problems having in the constraints a subdifferential inclusion relation. When the primal problem and the perturbation function are particularized different new dual problems are obtained. In the special case of a constrained optimization problem, the classical Wolfe and Mond-Weir duals, respectively, follow as particularizations of the general duals by using the Lagrange perturbation. Examples to show the differences between the new duals are given and a gate towards other generalized convexities is opened.     

Lagrange duality in canonical DC programming

We study Lagrange duality theorems for canonical DC programming problems. We show two types Lagrange duality results by using a decomposition method to infinite convex programming problems and by using a previous result by Lemaire (1998)  [6]. Also we observe these constraint qualifications for the duality theorems.     

A note on strong duality in convex semidefinite optimization: necessary and sufficient conditions

A strong duality which states that the optimal values of the primal convex problem and its Lagrangian dual problem are equal (i.e. zero duality gap) and the dual problem attains its maximum is a corner stone in convex optimization. In particular it plays a major role in the numerical solution as well as the application of convex semidefinite optimization. The strong duality requires a technical condition known as a constraint qualification (CQ). Several CQs which are sufficient for strong duality have been given in the literature. In this note we present new necessary and sufficient CQs for the strong duality in convex semidefinite optimization. These CQs are shown to be sharper forms of the strong conical hull intersection property (CHIP) of the intersecting sets of constraints which has played a critical role in other areas of convex optimization such as constrained approximation and error bounds. Research was partially supported by the Australian Research Council. The author is grateful to the referees for their helpful comments     

Complete characterizations of stable Farkas’ lemma and cone-convex programming duality

We establish necessary and sufficient conditions for a stable Farkas’ lemma. We then derive necessary and sufficient conditions for a stable duality of a cone-convex optimization problem, where strong duality holds for each linear perturbation of a given convex objective function. As an application, we obtain stable duality results for convex semi-definite programs and convex second-order cone programs. The authors are grateful to the referees for their valuable suggestions and helpful detailed comments which have contributed to the final preparation of the paper. The first author was supported by the Australian Research Council Linkage Program. The second author was supported by the Basic Research Program of KOSEF (Grant No. R01-2006-000-10211-0).     

New strong duality results for convex programs with separable constraints

It is known that convex programming problems with separable inequality constraints do not have duality gaps. However, strong duality may fail for these programs because the dual programs may not attain their maximum. In this paper, we establish conditions characterizing strong duality for convex programs with separable constraints. We also obtain a sub-differential formula characterizing strong duality for convex programs with separable constraints whenever the primal problems attain their minimum. Examples are given to illustrate our results.     

An identity for a quasilinear ODE and its applications to the uniqueness of solutions of BVPs

The following boundary value problem

(1.1)     

The gap function of a convex program

The gap function expresses the duality gap of a convex program as a function of the primal variables only. Differentiability and convexity properties are derived, and a convergent minimization algorithm is given. An example gives a simple one-variable interpretation of weak and strong duality. Application to user-equilibrium traffic assignment yields an appealing alternative optimization problem.     

Generalized concavity and duality with a square root term

Duality results for a class of nondifferentiable mathematical programming problems are given. These results allow for the weakening of the usual convexity conditions required for duality to hold. A pair of symmetric and self dual nondifferentiable programs under weaker convexity conditions are also given. A subgradient symmetric duality is proposed and its limitations discussed. Finally, a pair of nondifferentiable mathematical programs containing arbitrary norms is presented.     

A duality theorem for a homogeneous fractional programming problem

The usual theory of duality for linear fractional programs is extended by replacing the linear functions in the numerator and denominator by arbitrary positively homogeneous convex functions. In the constraints, the positive orthant inR ⁿ is replaced by an arbitrary cone. The resultant duality theorem contains a recent result of Chandra and Gulati as a special case.The authors wish to thank the referee for a number of valuable suggestions, particularly improvements in Theorem 3.4 and Corollary 3.1.     

A SINGULAR OR NONSINGULAR PERTURBATION PROBLEM FOR A SECOND ORDER NONLINEAR DIFFERENTIAL EQUATION WITH TWO FREE ENDPOINTS

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Stable zero duality gaps in convex programming: Complete dual characterisations with applications to semidefinite programs

The zero duality gap that underpins the duality theory is one of the central ingredients in optimisation. In convex programming, it means that the optimal values of a given convex program and its associated dual program are equal. It allows, in particular, the development of efficient numerical schemes. However, the zero duality gap property does not always hold even for finite-dimensional problems and it frequently fails for problems with non-polyhedral constraints such as the ones in semidefinite programming problems. Over the years, various criteria have been developed ensuring zero duality gaps for convex programming problems. In the present work, we take a broader view of the zero duality gap property by allowing it to hold for each choice of linear perturbation of the objective function of the given problem. Globalising the property in this way permits us to obtain complete geometric dual characterisations of a stable zero duality gap in terms of epigraphs and conjugate functions. For convex semidefinite programs, we establish necessary and sufficient dual conditions for stable zero duality gaps, as well as for a universal zero duality gap in the sense that the zero duality gap property holds for each choice of constraint right-hand side and convex objective function. Zero duality gap results for second-order cone programming problems are also given. Our approach makes use of elegant conjugate analysis and Fenchel's duality.     

About a numerical method of successive interpolations for two-point boundary value problems with deviating argument

A new numerical method for two-point boundary value problems with deviating argument is obtained. The method uses the fixed point technique, the trapezoidal quadrature rule, and a Birkhoff interpolation procedure. The convergence of the method is proved without smoothness conditions, the kernel function being only Lipschitzian in each argument. The interpolation procedure is used only on the points where the argument is modified. A stopping criterion of the algorithm is obtained and the accuracy of the method is illustrated on four numerical examples of pantograph type.     

Conjugate duality for vector-maximization problems

In this article we present a conjugate duality for a problem of maximizing a polyhedral concave nondecreasing homogeneous function over a convex feasible set in the nonnegative n-dimensional orthant. Using this duality we obtain a zero-gap duality for a vector-maximization problem.