Relation between the constant rank and the relaxed constant rank constraint qualifications

This article discusses a relation between the constant rank constraint qualification (CRCQ) and the recently proposed relaxed constant rank constraint qualification (RCRCQ). We show that a parametric constraint system satisfying the RCRCQ is locally diffeomorphic to a system satisfying the CRCQ. We use this result to extend some existing results for the CRCQ to the RCRCQ, establish a relation between the RCRCQ and the Mangasarian–Fromovitz constraint qualification, and obtain a weakened version of the Aubin property under the RCRCQ.     

Augmented Lagrangian methods under the constant positive linear dependence constraint qualification

Two Augmented Lagrangian algorithms for solving KKT systems are introduced. The algorithms differ in the way in which penalty parameters are updated. Possibly infeasible accumulation points are characterized. It is proved that feasible limit points that satisfy the Constant Positive Linear Dependence constraint qualification are KKT solutions. Boundedness of the penalty parameters is proved under suitable assumptions. Numerical experiments are presented. The authors were supported by PRONEX - CNPq / FAPERJ E-26 / 171.164/2003 - APQ1, FAPESP (Grants 2001/04597-4, 2002/00094-0, 2003/09169-6, 2002/00832-1 and 2005/56773-1) and CNPq.     

A relaxed constant positive linear dependence constraint qualification and applications

In this work we introduce a relaxed version of the constant positive linear dependence constraint qualification (CPLD) that we call RCPLD. This development is inspired by a recent generalization of the constant rank constraint qualification by Minchenko and Stakhovski that was called RCRCQ. We show that RCPLD is enough to ensure the convergence of an augmented Lagrangian algorithm and that it asserts the validity of an error bound. We also provide proofs and counter-examples that show the relations of RCRCQ and RCPLD with other known constraint qualifications. In particular, RCPLD is strictly weaker than CPLD and RCRCQ, while still stronger than Abadie’s constraint qualification. We also verify that the second order necessary optimality condition holds under RCRCQ.     

A new constraint qualification condition

We introduce a new constraint qualification condition in mathematical programming which encompasses the Mangasarian-Fromovitz's condition and the constant rank condition of Janin. Contrarily to the Mangasarian-Fromovitz's condition, our condition is still satisfied when one translates equalities as double inequalities. It relies on the fact that linearization stability is easier to check with equalities than with inequalities.     

A nonsmooth variant of the Mangasarian-Fromovitz constraint qualification

A unified view on constraint qualifications for nonsmooth equality and inequality constrained programs is presented. A fairly general constraint qualification for programs involving B-differential functions is given. Further specification to piecewise differentiable equality constraints and locally Lipschitz continuous inequality constraints yields a nonsmooth version of the Mangasarian-Fromovitz constraint qualification.This work was supported by the Deutsche Forschungsgemeinschaft, DFG-Grant No. Pa 219/5-1.     

A constraint qualification for convex programming

1. IntroductionLet X be the n-dimensional Euclidean space Rad. Denote the transpose of the columnvector y by yT. Suppose that R7 and nit R= are the n-dimensional vector sets with nonnegative and positive components, whose elements are denoted by y 3 0 and y > 0, respectively.Write R for the nonnegative real number set.Consider a nondiffereatiable convex programming problem:We assume that f(x), gi(x),..', g.(x) are finite reaLvalued continuous, convex functions on X, but not necessarily d…     

A simple constraint qualification in convex programming

We introduce and characterize a class of differentiable convex functions for which the Karush—Kuhn—Tucker condition is necessary for optimality. If some constraints do not belong to this class, then the characterization of optimality generally assumes an asymptotic form.We also show that for the functions that belong to this class in multi-objective optimization, Pareto solutions coincide with strong Pareto solutions,. This extends a result, well known for the linear case.Research partly supported by the National Research Council of Canada.     

Examples of constant rank spaces

The maximum dimension of a space of (k+1)×(k4+s-1) complex matrices of rank k is either s or s+1. Only when s divides k is it possible for the maximum to be s+1. This much is known. In this paper we produce for each k, a multiple of s, an (s+l)-dimensional space of (k+1)×(k+s-1) complex matrices whose non-zero members all have rank k. In the notation introduced by Sylvester l(k,k+1,k+s-1)=s+1 whenever s divides k.     

A simple constraint qualification in infinite dimensional programming

A new, simple, constraint qualification for infinite dimensional programs with linear programming type constraints is used to derive the dual program; see Theorem 3.1. Applications include a proof of the explicit solution of the best interpolation problem presented in [8].     

Optimality conditions and the basic constraint qualification for quasiconvex programming

In this paper, we consider optimality conditions and a constraint qualification for quasiconvex programming. For this purpose, we introduce a generator and a new subdifferential for quasiconvex functions by using Penot and Volle’s theorem.     

Generalizations of Slater's constraint qualification for infinite convex programs

In this paper we study constraint qualifications and duality results for infinite convex programs (P) = inf{f(x): g(x) – S, x C}, whereg = (g ₁,g ₂) andS = S ₁ ×S ₂,S _i are convex cones,i = 1, 2,C is a convex subset of a vector spaceX, andf andg _i are, respectively, convex andS _i -convex,i = 1, 2. In particular, we consider the special case whenS ₂ is in afinite dimensional space,g ₂ is affine andS ₂ is polyhedral. We show that a recently introduced simple constraint qualification, and the so-called quasi relative interior constraint qualification both extend to (P), from the special case thatg = g ₂ is affine andS = S ₂ is polyhedral in a finite dimensional space (the so-called partially finite program). This provides generalized Slater type conditions for (P) which are much weaker than the standard Slater condition. We exhibit the relationship between these two constraint qualifications and show how to replace the affine assumption ong ₂ and the finite dimensionality assumption onS ₂, by a local compactness assumption. We then introduce the notion of strong quasi relative interior to get parallel results for more general infinite dimensional programs without the local compactness assumption. Our basic tool reduces to guaranteeing the closure of the sum of two closed convex cones.     

On the Guignard constraint qualification for mathematical programs with equilibrium constraints

We recapitulate the well-known fact that most of the standard constraint qualifications are violated for mathematical programs with equilibrium constraints (MPECs). We go on to show that the Abadie constraint qualification is only satisfied in fairly restrictive circumstances. In order to avoid this problem, we fall back on the Guignard constraint qualification (GCQ). We examine its general properties and clarify the position it occupies in the context of MPECs. We show that strong stationarity is a necessary optimality condition under GCQ. Also, we present several sufficient conditions for GCQ, showing that it is usually satisfied for MPECs.     

Characterization of optimality in convex programming without a constraint qualification

Necessary and sufficient conditions of optimality are given for convex programming problems with no constraint qualification. The optimality conditions are stated in terms of consistency or inconsistency of a family of systems of linear inequalities and cone relations.This research was supported by Project No. NR-047-021, ONR Contract No. N00014-67-A-0126-0009 with the Center for Cybernetics Studies, The University of Texas; by NSF Grant No. ENG-76-10260 at Northwestern University; and by the National Research Council of Canada.     

An alternative formulation for a new closed cone constraint qualification

We give an alternative formulation for the so-called closed cone constraint qualification (CCCQ) related to a convex optimization problem in Banach spaces recently introduced in the literature. This new formulation allows to prove in a simple way that (CCCQ) is weaker than some generalized interior-point constraint qualifications given in the past. By means of some insights from the theory of conjugate duality we also show that strong duality still holds under some weaker hypotheses than the ones considered so far in the literature.     

Mixed duality without a constraint qualification for minimax fractional programming

Without the need of a constraint qualification, we establish the necessary and sufficient optimality conditions for minimax fractional programming. Using these optimality conditions, we construct a mixed dual model which unifies the Mond–Weir dual, Wolfe dual and a parameter dual models. Several duality theorems are established. Consequently, this article partly solves the problem posed by Lai et al. [H.C. Lai, J.C. Liu and K. Tanaka (1999). Duality without a constraint qualification for minimax fractional programming. Journal of Optimization Theory and Applications, 101, 109–125.].     

Coercive inequalities for Pseudodifferential operators of constant rank

Summary Operators are studied whose symbol satisfies a condition of constant rank and a priori estimates developed which hold essentially on the complement of an approximate null space of the operator. A limit absorption result is then established using a technique first described by Agmon. Entrata in Redazione il 1è ottobre 1975. Research supported by the Science Research Council under contract B/SR/97915.     

On the solution of systems of equations with constant rank derivatives

The famous for its simplicity and clarity Newton–Kantorovich hypothesis of Newton’s method has been used for a long time as the sufficient convergence condition for solving nonlinear equations. Recently, in the elegant study by Hu et al. (J Comput Appl Math 219:110–122, 2008), a Kantorovich-type convergence analysis for the Gauss–Newton method (GNM) was given improving earlier results by Häubler (Numer Math 48:119–125, 1986), and extending some results by Argyros (Adv Nonlinear Var Inequal 8:93–99, 2005, 2007) to hold for systems of equations with constant rank derivatives. In this study, we use our new idea of recurrent functions to extend the applicability of (GNM) by replacing existing conditions by weaker ones. Finally, we provide numerical examples to solve equations in cases not covered before (Häubler, Numer Math 48:119–125, 1986; Hu et al., J Comput Appl Math 219:110–122, 2008; Kontorovich and Akilov 2004).