where is a (possibly infinite) set, f : R ! R is an extended real valued function and g : R ! R. In the above formulation, a feasible point x 2 R is supposed to satisfy the constraints gðx,!Þ 0 for all !2 , and no structural assumptions are made about the set . In some situations it is natural to require that these constraints hold for almost every (a.e.) !2 . That is, the set is equipped with ...

Semi-infinite programming (SIP) problems can be efficiently solved by reduction type methods. Here, we present a new reduction method for SIP, where the multi-local optimization is carried out with a multi-local branch-and-bound method, the reduced (finite) problem is approximately solved by an interior point method, and the global convergence is promoted through a two-dimensional filter line s...

In this work an optimization-based approach to kinetic model reduction was studied with a view to generating reduced-model libaries for reacting-flow simulations. A linear integer formulation of the reaction elimination problem was developed in order to allow the model reduction problem to be solved cheaply and robustly to guaranteed global optimality. When compared with three other conventiona...

The Remez penalty and smoothing algorithm (RPSALG) is a unified framework for penalty and smoothing methods for solving min-max convex semiinfinite programing problems, whose convergence was analyzed in a previous paper of three of the authors. In this paper we consider a partial implementation of RPSALG for solving ordinary convex semi-infinite programming problems. Each iteration of RPSALG in...

This paper provides sufficient conditions for the optimal value function of a given linear semi-infinite programming problem to depend linearly on the size of the perturbations, when these perturbations are directional, involve either the cost coefficients or the right-hand-side function or both, and they are sufficiently small. Two kinds of partitions are considered. The first one concerns the...

Abstract In this paper, for a nonsmooth semi-infinite multiobjective programming with locally Lipschitz data, some weak and strong Karush-KuhnTucker type optimality conditions are derived. The necessary conditions are proposed under a constraint qualification, and the sufficient conditions are explored under assumption of generalized invexity. All results are expressed in terms of Clarke subdif...

In [S. Bütikofer, Math. Methods Oper. Res., 68 (2008), pp. 235–256] a nonsmooth Newton method globalized with the aid of a path search was developed in an abstract framework. We refine the convergence analysis given there and adapt this algorithm to certain finite dimensional optimization problems with C1,1 data. Such problems arise, for example, in semi-infinite programming under a reduction a...

The paper is devoted to the study of general nonsmooth problems of cone-constrained optimization (or conic programming) important for various aspects of optimization theory and applications. Based on advanced constructions and techniques of variational analysis and generalized differentiation, we derive new necessary optimality conditions (in both " exact " and " fuzzy " forms) for nonsmooth co...

In recent years, learning methods are desirable because of their reliability and efficiency in real-world problems. We propose a novel method to find infinitely many kernel combinations for learning problems with the help of infinite and semi-infinite optimization regarding all elements in kernel space. This will provide to study variations of combinations of kernels when considering heterogene...

Finite-dimensional linear programs satisfy strong duality (SD) and have the “dual 8 pricing” (DP) property. The (DP) property ensures that, given a sufficiently small perturbation of 9 the right-hand-side vector, there exists a dual solution that correctly “prices” the perturbation by 10 computing the exact change in the optimal objective function value. These properties may fail in 11 semi-inf...