A Smoothing Penalty Method For Mathematical Programs With Equilibrium Constraints


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A Smoothing Penalty Method for Mathematical Programs with Equilibrium Constraints


A Smoothing Penalty Method for Mathematical Programs with Equilibrium Constraints

Author: Jiaping Zhu

language: en

Publisher:

Release Date: 2005


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Mathematical Programs with Equilibrium Constraints


Mathematical Programs with Equilibrium Constraints

Author: Zhi-Quan Luo

language: en

Publisher: Cambridge University Press

Release Date: 1996-11-13


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This book provides a solid foundation and an extensive study for an important class of constrained optimization problems known as Mathematical Programs with Equilibrium Constraints (MPEC), which are extensions of bilevel optimization problems. The book begins with the description of many source problems arising from engineering and economics that are amenable to treatment by the MPEC methodology. Error bounds and parametric analysis are the main tools to establish a theory of exact penalisation, a set of MPEC constraint qualifications and the first-order and second-order optimality conditions. The book also describes several iterative algorithms such as a penalty-based interior point algorithm, an implicit programming algorithm and a piecewise sequential quadratic programming algorithm for MPECs. Results in the book are expected to have significant impacts in such disciplines as engineering design, economics and game equilibria, and transportation planning, within all of which MPEC has a central role to play in the modelling of many practical problems.

Optimization of Complex Systems: Theory, Models, Algorithms and Applications


Optimization of Complex Systems: Theory, Models, Algorithms and Applications

Author: Hoai An Le Thi

language: en

Publisher: Springer

Release Date: 2019-06-15


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This book contains 112 papers selected from about 250 submissions to the 6th World Congress on Global Optimization (WCGO 2019) which takes place on July 8–10, 2019 at University of Lorraine, Metz, France. The book covers both theoretical and algorithmic aspects of Nonconvex Optimization, as well as its applications to modeling and solving decision problems in various domains. It is composed of 10 parts, each of them deals with either the theory and/or methods in a branch of optimization such as Continuous optimization, DC Programming and DCA, Discrete optimization & Network optimization, Multiobjective programming, Optimization under uncertainty, or models and optimization methods in a specific application area including Data science, Economics & Finance, Energy & Water management, Engineering systems, Transportation, Logistics, Resource allocation & Production management. The researchers and practitioners working in Nonconvex Optimization and several application areas can find here many inspiring ideas and useful tools & techniques for their works.