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Yes, you can access Parametric Optimization and Related Topics by Jürgen Guddat,Hubertus Th. Jongen,Bernd Kummer,František Nožička in PDF and/or ePUB format. We have over one million books available in our catalogue for you to explore.

Information

Publisher

De Gruyter

Year

1987

eBook ISBN

9783112720936

Edition

Topic

Mathematics

Index

Mathematics

PREFACE
Contents
Regularity theorems In sensitivity with nonsmooth data
Nonlinear parametric Integer programming
Stability of polynomial optimization problems
On the existence of Lipschitz-continuous and differentiable selections for multifunctions
On some connections between parametric and stochastic programming
Methods of parametrlzation in the analysis of improper mathematical programming problems
Continuous selections for semi-finite optimization
Holder continuity of solutions of perturbed optimization problems under Mangasarian-Fromowitz constraint qualification
Parametric optimization: Pivoting and predictor-corrector continuation, a survey
Parametric optimization In certain lattice-ordered groups: Variable objective function
Local aspects of a method for solving membrane-eigenvalue problems by parametric semi-infinite programming
Parametric optimization: The Kuhn-Tucker set
A note on Branln's method for finding the critical points of smooth functions
Lipschltz continuity of inflma and optimal solutions in parametric optimization: The polyhedral case
Linearly and nonlinearly perturbed optimization problems
An algorithm for one-parametric optimization problems based on an active index set strategy
Smooth homotopies for mathematical programming
Strong unicity in nonlinear parametric optimization
Obtaining convergence rates for approximations In stochastic programming
Stochastic optimization and stochastic processes: The epigraphical approach
On duality and stability of parametrized optimization problems and related topics
The application of parametric optimization and imbedding to the foundation and realization of ageneralized primal decomposition approach
On approximations and stability in stochastic programming

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