Duality theorems for convex programming without constraint qualification

Duality without constraint qualification in nonsmooth optimization

We are concerned with a nonsmooth multiobjective optimization problem with inequality constraints. In order to obtain our main results, we give the definitions of the generalized convex functions based on the generalized directional derivative. Under the above generalized convexity assumptions, sufficient and necessary conditions for optimality are given without the need of a constraint qualifi...

Mixed Duality for Nonsmooth Multiobjective Fractional Programming without a Constraint Qualification

On Sequential Optimality Conditions without Constraint Qualifications for Nonlinear Programming with Nonsmooth Convex Objective Functions

Sequential optimality conditions provide adequate theoretical tools to justify stopping criteria for nonlinear programming solvers. Here, nonsmooth approximate gradient projection and complementary approximate Karush-Kuhn-Tucker conditions are presented. These sequential optimality conditions are satisfied by local minimizers of optimization problems independently of the fulfillment of constrai...

New Constraint Qualification and Conjugate Duality for Composed Convex Optimization Problems

We present a new constraint qualification which guarantees strong duality between a cone-constrained convex optimization problem and its Fenchel-Lagrange dual. This result is applied to a convex optimization problem having, for a given nonempty convex cone K , as objective function a K-convex function postcomposed with a K-increasing convex function. For this so-called composed convex optimizat...

Duality and Convex Programming

This chapter surveys key concepts in convex duality theory and their application to the analysis and numerical solution of problem archetypes in imaging.