Subgradient methods for SVMs have been successful in solving the primal formulation with linear kernels. The approach is extended here to nonlinear kernels, and the assumption of strong convexity of the objective is dropped, allowing an intercept term to be used in the classifier.

A multiobjective nonlinear programming problem is considered. Sufficiency theorems are derived for efficient and properly efficient solutions under generalized (F, ρ)-convexity assumptions. Weak, strong and strict converse duality theorems are established for a general Mond–Weir type dual relating properly efficient solutions of the primal and dual problems.

We consider nonsmooth multiobjective fractional programming problems with inequality and equality constraints. We establish the necessary and sufficient optimality conditions under various generalized invexity assumptions. In addition, we formulate a mixed dual problem corresponding to primal problem, and discuss weak, strong and strict converse duality theorems.

Abstract-A second-order dual to a nonlinear programming problem is formulated. This dual uses the Ritz John necessary optimality conditions instead of the Karush-Kuhn-Tucker necessary optimal&y conditions, and thus, does not require a constraint qualification. Weak, strong, strictconverse, and converse duality theorems between primal and dual problems are established. @ 2001 Elsevier Science Lt...

In this paper, we introduce the concept of a (weak) minimizer of order k for a nonsmooth vector optimization problem over cones. Generalized classes of higher-order cone-nonsmooth (F, ρ)convex functions are introduced and sufficient optimality results are proved involving these classes. Also, a unified dual is associated with the considered primal problem, and weak and strong duality results ar...

In this short paper, we give an upper bound for the number of different basic feasible solutions generated by the dual simplex method with the most negative pivoting rule for LP. The bound is comparable with the bound given by Kitahara and Mizuno (2010) for the primal simplex method. We apply the result to the maximum flow problem and get a strong polynomial bound.

We characterize the optimal solution of a quadratic program over the Stiefel manifold with an objective function in trace formulation. The result is applied to relaxations of HQAP and MTLS. Finally, we show that strong duality holds for the Lagrangian dual, provided some redundant constraints are added to the primal program. © 2005 Elsevier B.V. All rights reserved.

In this paper we present a robust conjugate duality theory for convex programming problems in the face of data uncertainty within the framework of robust optimization, extending the powerful conjugate duality technique. We first establish robust strong duality between an uncertain primal parameterized convex programming model problem and its uncertain conjugate dual by proving strong duality be...

Abstract A transformed primal-dual (TPD) flow is developed for a class of nonlinear smooth saddle point system. The the dual variable contains Schur complement which strongly convex. Exponential stability obtained by showing strong Lyapunov property. Several TPD iterations are derived implicit Euler, explicit implicit-explicit and Gauss-Seidel methods with accelerated overrelaxation flow. Gener...