In the field of nondifferentiable nonconvex optimization, one of the most intensely investigated areas is that of optimization problems involving multivalued mappings in constraints or as the objective function.
Complementarity theory, a relatively new domain in applied mathematics, has deep connections with several aspects of fundamental mathematics and also has many applications in optimization, economics and engineering.
Linear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems-for example, providing logistical support for the U.
Although transportation economists have advocated the tolling of urban streets as a mechanism for controlling congestion and managing travel demands for over 50 years, it is only recently that this idea has become practical.
Large-Scale Nonlinear Optimization reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research.
Most books about global optimization describe the theory of the algorithms, whereas a given implementation's quality never depends exclusively on the theoretical soundness of the algorithms that are implemented.
Robust design-that is, managing design uncertainties such as model uncertainty or parametric uncertainty-is the often unpleasant issue crucial in much multidisciplinary optimal design work.
Search Methodologies is a tutorial survey of the methodologies that are at the confluence of several fields: Computer Science, Mathematics and Operations Research.
Over the last twenty years, Professor Franco Giannessi, a highly respected researcher, has been working on an approach to optimization theory based on image space analysis.
Continuous optimization is the study of problems in which we wish to opti- mize (either maximize or minimize) a continuous function (usually of several variables) often subject to a collection of restrictions on these variables.
During the last decade I have explored the consequences of what I have chosen to call the 'consistent preferences' approach to deductive reasoning in games.
Metaheuristics: Progress as Real Problem Solvers is a peer-reviewed volume of eighteen current, cutting-edge papers by leading researchers in the field.
Global optimization aims at solving the most general problems of deterministic mathematical programming: to find the global optimum of a nonlinear, nonconvex, multivariate function of continuous and/or integer variables subject to constraints which may be themselves nonlinear and nonconvex.
Quadratic programs and affine variational inequalities represent two fundamental, closely-related classes of problems in the t,heories of mathematical programming and variational inequalities, resp- tively.
Many optimization questions arise in economics and finance; an important example of this is the society's choice of the optimum state of the economy (the social choice problem).
Statistical Modeling and Analysis for Complex Data Problems treats some of today's more complex problems and it reflects some of the important research directions in the field.
At present, in order to resolve problems of ecology and to save mineral resources for future population generations, it is quite necessary to know how to maintain nature arrangement in an efficient way.
It is intended that this book be used in senior- to graduate-level semester courses in optimization, as offered in mathematics, engineering, com- puter science and operations research departments.
Peter Kall and Janos Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization.
Dynamic games continue to attract strong interest from researchers interested in modelling competitive as well as conflict situations exhibiting an intertemporel aspect.