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Journal ArticleDOI

Penalty and Smoothing Methods for Convex Semi-Infinite Programming

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TLDR
This paper introduces a unified framework concerning Remez-type algorithms and integral methods coupled with penalty and smoothing methods that subsumes well-known classical algorithms, but also provides some new methods with interesting properties.
Abstract
In this paper we consider min-max convex semi-infinite programming. To solve these problems we introduce a unified framework concerning Remez-type algorithms and integral methods coupled with penalty and smoothing methods. This framework subsumes well-known classical algorithms, but also provides some new methods with interesting properties. Convergence of the primal and dual sequences are proved under minimal assumptions.

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Citations
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Journal ArticleDOI

A New Exchange Method for Convex Semi-Infinite Programming

TL;DR: A new dropping-rule is introduced in the proposed exchange algorithm, which only keeps those active constraints with positive Lagrange multipliers and exploits the idea of looking for $\eta$-infeasible indices of the lower level problem as the adding-rule in the algorithm.
Journal ArticleDOI

Recent contributions to linear semi-infinite optimization: an update

TL;DR: The state-of-the-art in the theory of deterministic and uncertain linear semi-infinite optimization is reviewed, some numerical approaches to this type of problems are presented, and a selection of recent applications are described.
Journal ArticleDOI

Stationarity and Regularity of Infinite Collections of Sets

TL;DR: Stationarity criteria developed in the article are applied to proving intersection rules for Fréchet normals to infinite intersections of sets in Asplund spaces.
Journal ArticleDOI

Recent contributions to linear semi-infinite optimization

TL;DR: The state-of-the-art in the theory of deterministic and uncertain linear semi-infinite optimization is reviewed, some numerical approaches to this type of problems are presented, and a selection of recent applications are described.
References
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Book

Asymptotic cones and functions in optimization and variational inequalities

TL;DR: Convex analysis and set valued maps: A Review and a Review of Set Valued Maps and Set-Valued Maps: Existence and Stability in Optimization Problems, Minimum Monotone Maps and Variational Inequalities as discussed by the authors.
Journal ArticleDOI

Newton's method for convex programming and Tchebycheff approximation

TL;DR: The rationale of Newton's method is exploited here in order to develop effective algorithms for solving the following general problem: given a convex continuous function F defined on a closed convex subset K of E,~, obtain a point x of K such that F(x)_<_F(y) for all y in K.
Book ChapterDOI

Numerical Methods for Semi-Infinite Programming: A Survey

TL;DR: This paper provides a review of numerical methods for the solution of smooth semi-infinite programming problems and presents fundamental and partly new results on level sets, discretization, and local reduction.
Journal ArticleDOI

A new computational algorithm for functional inequality constrained optimization problems

TL;DR: A computational algorithm is devised for solving a class of functional inequality constrained optimization problems, based on a penalty function, for which a numerical example is solved.
Journal ArticleDOI

A central cutting plane algorithm for the convex programming problem

TL;DR: An algorithm is developed for solving the convex programming problem by constructing a cutting plane through the center of a polyhedral approximation to the optimum, which generates a sequence of primal feasible points whose limit points satisfy the Kuhn—Tucker conditions of the problem.
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