Characterization of optimality in convex programming without a constraint qualification

1976 ◽  
Vol 20 (4) ◽  
pp. 417-437 ◽  
Author(s):  
A. Ben-Tal ◽  
A. Ben-Israel ◽  
S. Zlobec
Keyword(s):  
Convex Programming ◽  
Constraint Qualification ◽  
Characterization Of Optimality

scholarly journals Duality theorems for convex programming without constraint qualification

1984 ◽  
Vol 36 (2) ◽  
pp. 253-266 ◽  
Author(s):  
P. Kanniappan
Keyword(s):  
Convex Function ◽  
Convex Programming ◽  
Constraint Qualification ◽  
Constraint Qualifications ◽  
Convex Programming Problem ◽  
Duality Theorems ◽  
Finite Dimensional ◽  
Lower Semi ◽  
Continuous Convex Function

AbstractInvoking a recent characterization of Optimality for a convex programming problem with finite dimensional range without any constraint qualification given by Borwein and Wolkowicz, we establish duality theorems. These duality theorems subsume numerous earlier duality results with constraint qualifications. We apply our duality theorems in the case of the objective function being the sum of a positively homogeneous, lower-semi-continuous, convex function and a subdifferentiable convex function. We also study specific problems of the above type in this setting.

On A characterization of optimality in convex programming

1976 ◽  
Vol 11 (1) ◽  
pp. 81-88 ◽  
Author(s):  
A. Ben-Israel ◽  
A. Ben-Tal
Keyword(s):  
Convex Programming ◽  
Characterization Of Optimality

Characterization of the solution for the problems of strong and weak convex programming

2021 ◽  
Vol 212 (6) ◽  
Author(s):  
Sergey Ivanovitch Dudov ◽  
Mikhail Anatol'evich Osiptsev
Keyword(s):  
Convex Programming

scholarly journals What is invexity?

1986 ◽  
Vol 28 (1) ◽  
pp. 1-9 ◽  
Author(s):  
A. Ben-Israel ◽  
B. Mond
Keyword(s):  
Mathematical Programming ◽  
Convex Functions ◽  
Constraint Qualification ◽  
Saddle Point Problem ◽  
Point Problem ◽  
Slater Constraint Qualification ◽  
The Relationship ◽  
Unconstrained Problems ◽  
Class Of Functions

AbstractRecently it was shown that many results in Mathematical Programming involving convex functions actually hold for a wider class of functions, called invex. Here a simple characterization of invexity is given for both constrained and unconstrained problems. The relationship between invexity and other generalizations of convexity is illustrated. Finally, it is shown that invexity can be substituted for convexity in the saddle point problem and in the Slater constraint qualification.

A simple constraint qualification in convex programming

1993 ◽  
Vol 61 (1-3) ◽  
pp. 385-397 ◽  
Author(s):  
X. Zhou ◽  
F. Sharifi Mokhtarian ◽  
S. Zlobec
Keyword(s):  
Convex Programming ◽  
Constraint Qualification

scholarly journals A comparison of constraint qualifications in infinite-dimensional convex programming revisited

1999 ◽  
Vol 40 (3) ◽  
pp. 353-378 ◽  
Author(s):  
C. Zặlinescu
Keyword(s):  
Convex Programming ◽  
Constraint Qualification ◽  
Constraint Qualifications ◽  
Strong Duality ◽  
Sufficient Condition ◽  
Subdifferential Calculus ◽  
Infinite Dimensional ◽  
Qualification Conditions ◽  
The One

In 1990 Gowda and Teboulle published the paper [16], making a comparison of several conditions ensuring the Fenchel-Rockafellar duality formulainf{f(x) + g(Ax) | x ∈ X} = max{−f*(A*y*) − g*(− y*) | y* ∈ Y*}.Probably the first comparison of different constraint qualification conditions was made by Hiriart-Urruty [17] in connection with ε-subdifferential calculus. Among them appears, as the basic sufficient condition, the formula for the conjugate of the corresponding function; such functions are: f1 + f2, g o A, max{fl,…, fn}, etc. In fact strong duality formulae (like the one above) and good formulae for conjugates are equivalent and they can be used to obtain formulae for ε-subdifferentials, using a technique developed in [17] and extensively used in [46].

scholarly journals Lagrange multiplier characterizations of constrained best approximation with nonsmooth nonconvex constraints

Filomat ◽  
2020 ◽  
Vol 34 (14) ◽  
pp. 4669-4684
Author(s):  
H. Mohebi
Keyword(s):  
Lagrange Multiplier ◽  
Best Approximation ◽  
Constraint Qualification ◽  
Sufficient Conditions ◽  
Dual Cone ◽  
The Best Approximation ◽  
Constraint Set ◽  
Nonconvex Constraint ◽  
Constrained Best Approximation

In this paper, we consider the constraint set K := {x ? Rn : gj(x)? 0,? j = 1,2,...,m} of inequalities with nonsmooth nonconvex constraint functions gj : Rn ? R (j = 1,2,...,m).We show that under Abadie?s constraint qualification the ?perturbation property? of the best approximation to any x in Rn from a convex set ?K := C ? K is characterized by the strong conical hull intersection property (strong CHIP) of C and K, where C is an arbitrary non-empty closed convex subset of Rn: By using the idea of tangential subdifferential and a non-smooth version of Abadie?s constraint qualification, we do this by first proving a dual cone characterization of the constraint set K. Moreover, we present sufficient conditions for which the strong CHIP property holds. In particular, when the set ?K is closed and convex, we show that the Lagrange multiplier characterizations of constrained best approximation holds under a non-smooth version of Abadie?s constraint qualification. The obtained results extend many corresponding results in the context of constrained best approximation. Several examples are provided to clarify the results.

A note on optimal inspection policy for stochastically deteriorating series systems

1991 ◽  
Vol 28 (4) ◽  
pp. 934-939 ◽  
Author(s):  
Yuping Qiu
Keyword(s):  
Necessary Conditions ◽  
Inspection Policy ◽  
Limiting Case ◽  
Series Systems ◽  
Special Case ◽  
Characterization Of Optimality

A counterexample is given to demonstrate that a previously proposed characterization of optimal inspection policy for series systems is not correct in the discounted case. A set of valid necessary conditions is provided which leads to the characterization of optimality for a special case. The limiting case of these necessary conditions is also examined.

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