There is a dual program linked with every nonlinear program. The dual objective function is called the Lagrangian; it is defined in terms of the original problem. This note presents a characterization of the Lagrangian subgradients under general conditions. The theorem follows from a result of Danskin [1] that can be used (see [2]) to characterize the Lagrangian directional derivatives. These characterizations are theoretically interesting and may be useful in computing optimal dual solutions. Rockafellar's outstanding book, [3], is used as a basic reference; it contains a wealth of background and historical information.
We propose a general dual program for a constrained optimization problem via generalized nonlinear L...
Abstract. The paper deals with vector constrained extremum problems. A separation scheme is recalled...
Subgradient methods (SM) have long been the preferred way to solve the large-scale Nondifferentiable...
In mathematical optimzation, the Lagrangian approach is a general method to find an optimal solution...
In this article, we continue to study the modified subgradient (MSG) algorithm previously suggested ...
In his monograph Conjugate Duality and Optimization, Rockafellar puts forward a "perturbation + dual...
We study convergence properties of a modified subgradient algorithm, applied to the dual problem def...
The subgradient method is both a heavily employed and widely studied algorithm for non-differentiabl...
The subgradient method is both a heavily employed and widely studied algorithm for non-differentiabl...
AbstractWe give some Lagrangian duality theorems for optimization problems, in terms of non-linear s...
For constrained primal infimization problems, we give a characterization of the Lagrangian dual obje...
In this thesis we study a modified subgradient algorithm applied to the dual problem generated by au...
Subgradient methods (SM) have long been the preferred way to solve the large-scale Nondifferentiable...
We combine a Lagrangian approach inspired by convex and quasiconvex dualities with a penalization ap...
Abstract. We propose a general dual program for a constrained optimization problem via gener-alized ...
We propose a general dual program for a constrained optimization problem via generalized nonlinear L...
Abstract. The paper deals with vector constrained extremum problems. A separation scheme is recalled...
Subgradient methods (SM) have long been the preferred way to solve the large-scale Nondifferentiable...
In mathematical optimzation, the Lagrangian approach is a general method to find an optimal solution...
In this article, we continue to study the modified subgradient (MSG) algorithm previously suggested ...
In his monograph Conjugate Duality and Optimization, Rockafellar puts forward a "perturbation + dual...
We study convergence properties of a modified subgradient algorithm, applied to the dual problem def...
The subgradient method is both a heavily employed and widely studied algorithm for non-differentiabl...
The subgradient method is both a heavily employed and widely studied algorithm for non-differentiabl...
AbstractWe give some Lagrangian duality theorems for optimization problems, in terms of non-linear s...
For constrained primal infimization problems, we give a characterization of the Lagrangian dual obje...
In this thesis we study a modified subgradient algorithm applied to the dual problem generated by au...
Subgradient methods (SM) have long been the preferred way to solve the large-scale Nondifferentiable...
We combine a Lagrangian approach inspired by convex and quasiconvex dualities with a penalization ap...
Abstract. We propose a general dual program for a constrained optimization problem via gener-alized ...
We propose a general dual program for a constrained optimization problem via generalized nonlinear L...
Abstract. The paper deals with vector constrained extremum problems. A separation scheme is recalled...
Subgradient methods (SM) have long been the preferred way to solve the large-scale Nondifferentiable...