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A classical decision theoretic perspective on worst-case analysis

Moshe Sniedovich (2011)

Applications of Mathematics

We examine worst-case analysis from the standpoint of classical Decision Theory. We elucidate how this analysis is expressed in the framework of Wald's famous Maximin paradigm for decision-making under strict uncertainty. We illustrate the subtlety required in modeling this paradigm by showing that information-gap's robustness model is in fact a Maximin model in disguise.

A note on a class of equilibrium problems with equilibrium constraints

Jiří V. Outrata (2004)

Kybernetika

The paper concerns a two-level hierarchical game, where the players on each level behave noncooperatively. In this way one can model eg an oligopolistic market with several large and several small firms. We derive two types of necessary conditions for a solution of this game and discuss briefly the possibilities of its computation.

A note on the relation between strong and M-stationarity for a class of mathematical programs with equilibrium constraints

René Henrion, Jiří Outrata, Thomas Surowiec (2010)

Kybernetika

In this paper, we deal with strong stationarity conditions for mathematical programs with equilibrium constraints (MPEC). The main task in deriving these conditions consists in calculating the Fréchet normal cone to the graph of the solution mapping associated with the underlying generalized equation of the MPEC. We derive an inner approximation to this cone, which is exact under an additional assumption. Even if the latter fails to hold, the inner approximation can be used to check strong stationarity...

An algorithm for multiparametric min max 0-1-integer programming problems relative to the objective function

José Luis Quintero, Alejandro Crema (2006)

RAIRO - Operations Research

The multiparametric min max 0-1-Integer Programming (0-1-IP) problem relative to the objective function is a family of min max 0-1-IP problems which are related by having identical constraint matrix and right-hand-side vector. In this paper we present an algorithm to perform a complete multiparametric analysis relative to the objective function.

An algorithm for multiparametric min max 0-1-integer programming problems relative to the objective function

José Luis Quintero, Alejandro Crema (2005)

RAIRO - Operations Research - Recherche Opérationnelle

The multiparametric min max 0-1-Integer Programming (0-1-IP) problem relative to the objective function is a family of min max 0-1-IP problems which are related by having identical constraint matrix and right-hand-side vector. In this paper we present an algorithm to perform a complete multiparametric analysis relative to the objective function.

Augmented Lagrangian methods for variational inequality problems

Alfredo N. Iusem, Mostafa Nasri (2010)

RAIRO - Operations Research

We introduce augmented Lagrangian methods for solving finite dimensional variational inequality problems whose feasible sets are defined by convex inequalities, generalizing the proximal augmented Lagrangian method for constrained optimization. At each iteration, primal variables are updated by solving an unconstrained variational inequality problem, and then dual variables are updated through a closed formula. A full convergence analysis is provided, allowing for inexact solution of the subproblems. ...

Compression of satellite data.

Roberto Barrio, Antonio Elipe (2002)

Revista Matemática Complutense

In this paper, we present the simple and double compression algorithms with an error control for compressing satellite data corresponding to several revolutions. The compressions are performed by means of approximations in the norm L∞ by finite series of Chebyshev polynomials, with their known properties of fast evaluation, uniform distribution of the error, and validity over large intervals of time. By using the error control here introduced, the number of terms of the series is given automatically...

Computing the greatest 𝐗 -eigenvector of a matrix in max-min algebra

Ján Plavka (2016)

Kybernetika

A vector x is said to be an eigenvector of a square max-min matrix A if A x = x . An eigenvector x of A is called the greatest 𝐗 -eigenvector of A if x 𝐗 = { x ; x ̲ x x ¯ } and y x for each eigenvector y 𝐗 . A max-min matrix A is called strongly 𝐗 -robust if the orbit x , A x , A 2 x , reaches the greatest 𝐗 -eigenvector with any starting vector of 𝐗 . We suggest an O ( n 3 ) algorithm for computing the greatest 𝐗 -eigenvector of A and study the strong 𝐗 -robustness. The necessary and sufficient conditions for strong 𝐗 -robustness are introduced and an efficient...

Covering with rectangular pieces.

Iacob, Paul, Marinescu, Daniela, Luca, Cristina (2003)

Analele Ştiinţifice ale Universităţii “Ovidius" Constanţa. Seria: Matematică

Eigenspace of a circulant max–min matrix

Martin Gavalec, Hana Tomášková (2010)

Kybernetika

The eigenproblem of a circulant matrix in max-min algebra is investigated. Complete characterization of the eigenspace structure of a circulant matrix is given by describing all possible types of eigenvectors in detail.

Existence of solutions to weak nonlinear bilevel problems via MinSup and d.c. problems

Abdelmalek Aboussoror, Abdelatif Mansouri (2008)

RAIRO - Operations Research

In this paper, which is an extension of [4], we first show the existence of solutions to a class of Min Sup problems with linked constraints, which satisfy a certain property. Then, we apply our result to a class of weak nonlinear bilevel problems. Furthermore, for such a class of bilevel problems, we give a relationship with appropriate d.c. problems concerning the existence of solutions.

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