scholarly journals Error bounds for nondifferentiable convex inequalities under a strong Slater constraint qualification

1998 ◽  
Vol 83 (1-3) ◽  
pp. 187-194 ◽  
Author(s):  
O. L. Mangasarian
Keyword(s):  
Error Bounds ◽  
Constraint Qualification ◽  
Convex Inequalities ◽  
Slater Constraint Qualification

Smoothing Partially Exact Penalty Function of Biconvex Programming

2020 ◽  
Vol 37 (04) ◽  
pp. 2040018
Author(s):  
Rui Shen ◽  
Zhiqing Meng ◽  
Min Jiang
Keyword(s):  
Error Bounds ◽  
Penalty Function ◽  
Constraint Qualification ◽  
Feasible Solution ◽  
Exact Penalty Function ◽  
Exact Penalty ◽  
Kkt Condition ◽  
Optimum Point ◽  
Kkt Point ◽  
Slater Constraint Qualification

In this paper, a smoothing partial exact penalty function of biconvex programming is studied. First, concepts of partial KKT point, partial optimum point, partial KKT condition, partial Slater constraint qualification and partial exactness are defined for biconvex programming. It is proved that the partial KKT point is equal to the partial optimum point under the condition of partial Slater constraint qualification and the penalty function of biconvex programming is partially exact if partial KKT condition holds. We prove the error bounds properties between smoothing penalty function and penalty function of biconvex programming when the partial KKT condition holds, as well as the error bounds between objective value of a partial optimum point of smoothing penalty function problem and its [Formula: see text]-feasible solution. So, a partial optimum point of the smoothing penalty function optimization problem is an approximately partial optimum point of biconvex programming. Second, based on the smoothing penalty function, two algorithms are presented for finding a partial optimum or approximate [Formula: see text]-feasible solution to an inequality constrained biconvex optimization and their convergence is proved under some conditions. Finally, numerical experiments show that a satisfactory approximate solution can be obtained by the proposed algorithm.

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.

scholarly journals On converse duality for a nondifferentiable program

1982 ◽  
Vol 25 (1) ◽  
pp. 71-80 ◽  
Author(s):  
T.R. Gulati
Keyword(s):  
Simple Proof ◽  
Duality Theorem ◽  
Constraint Qualification ◽  
Dual Problem ◽  
Linear Constraints ◽  
Simple Condition ◽  
Converse Duality ◽  
Slater Constraint Qualification ◽  
Converse Duality Theorem ◽  
Fritz John Conditions

A nonlinear nondifferentiable program with linear constraints is considered and a converse duality theorem is discussed. First we weaken an assumption previously made by Bhatia, and then give a simple proof under this weaker hypothesis, using the Fritz John conditions. Finally, defining a generalized Slater constraint qualification which implies Abadie's constraint qualification, we give a simple condition for the dual problem to satisfy this constraint qualification.

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