Control Applications Of Nonlinear Programming And Optimization 1989
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Control Applications of Nonlinear Programming and Optimization 1989
These Proceedings provide valuable information on the exchange of ideas between scientists who apply nonlinear programming and optimization to real world control problems and those who develop new methods, algorithms and software. The papers deal with windshear problems, optimization of aircraft and spacecraft trajectories, optimal control for robots, the optimization of urban traffic control, general mechanical systems, multilevel inventory systems and robust control.
Nonlinear Control Systems Design 1989
In the last two decades, the development of specific methodologies for the control of systems described by nonlinear mathematical models has attracted an ever increasing interest. New breakthroughs have occurred which have aided the design of nonlinear control systems. However there are still limitations which must be understood, some of which were addressed at the IFAC Symposium in Capri. The emphasis was on the methodological developments, although a number of the papers were concerned with the presentation of applications of nonlinear design philosophies to actual control problems in chemical, electrical and mechanical engineering.
Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming
Author: Mohit Tawarmalani
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-04-17
Interest in constrained optimization originated with the simple linear pro gramming model since it was practical and perhaps the only computationally tractable model at the time. Constrained linear optimization models were soon adopted in numerous application areas and are perhaps the most widely used mathematical models in operations research and management science at the time of this writing. Modelers have, however, found the assumption of linearity to be overly restrictive in expressing the real-world phenomena and problems in economics, finance, business, communication, engineering design, computational biology, and other areas that frequently demand the use of nonlinear expressions and discrete variables in optimization models. Both of these extensions of the linear programming model are NP-hard, thus representing very challenging problems. On the brighter side, recent advances in algorithmic and computing technology make it possible to re visit these problems with the hope of solving practically relevant problems in reasonable amounts of computational time. Initial attempts at solving nonlinear programs concentrated on the de velopment of local optimization methods guaranteeing globality under the assumption of convexity. On the other hand, the integer programming liter ature has concentrated on the development of methods that ensure global optima. The aim of this book is to marry the advancements in solving nonlinear and integer programming models and to develop new results in the more general framework of mixed-integer nonlinear programs (MINLPs) with the goal of devising practically efficient global optimization algorithms for MINLPs.