Optimality Conditions For Max-Type Nonsmooth Minimization Problems

Optimization ◽  
1997 ◽  
Vol 41 (3) ◽  
pp. 219-236 ◽  
Author(s):  
Simon. Di ◽  
René A. Poliquin
Keyword(s):  
Optimality Conditions ◽  
Minimization Problems ◽  
Nonsmooth Minimization

scholarly journals Strict lower subdifferentiability and applications

1999 ◽  
Vol 40 (3) ◽  
pp. 379-391 ◽  
Author(s):  
H. Xu ◽  
A. M. Rubinov ◽  
B. M. Glover
Keyword(s):  
Global Convergence ◽  
Optimality Conditions ◽  
Convex Subset ◽  
Sufficient Optimality Conditions ◽  
Descent Direction ◽  
Minimization Problems ◽  
Convergence Results ◽  
Lower Subdifferential ◽  
Necessary And Sufficient ◽  
Convex Subdifferential

AbstractWe investigate the strict lower subdifferentiability of a real-valued function on a closed convex subset of Rn. Relations between the strict lower subdifferential, lower subdifferential, and the usual convex subdifferential are established. Furthermore, we present necessary and sufficient optimality conditions for a class of quasiconvex minimization problems in terms of lower and strict lower subdifferentials. Finally, a descent direction method is proposed and global convergence results of the consequent algorithm are obtained.

scholarly journals Smoothing approximations to nonsmooth optimization problems

1995 ◽  
Vol 36 (3) ◽  
pp. 274-285 ◽  
Author(s):  
X.Q. Yang
Keyword(s):  
Optimization Problems ◽  
Directional Derivative ◽  
Optimal Solution ◽  
Necessary Optimality Conditions ◽  
Second Order ◽  
Smooth Approximation ◽  
Minimization Problems ◽  
Second Order Directional Derivative ◽  
Nonsmooth Minimization ◽  
Original Objective

AbstractWe study certain types of composite nonsmooth minimization problems by introducing a general smooth approximation method. Under various conditions we derive bounds on error estimates of the functional values of original objective function at an approximate optimal solution and at the optimal solution. Finally, we obtain second-order necessary optimality conditions for the smooth approximation prob lems using a recently introduced generalized second-order directional derivative.

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