A self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Exercises are included in each chapter, and additional information is provided in two appendices and an accompanying Web site. Only a basic knowledge of linear algebra and calculus is required.
In this chapter, we consider some extensions of linear programming to other
mathematical programming problems. We first look at nonlinear problems,
deriving optimality conditions based solely on calculus arguments and convexity
properties ...
This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory. It considers select areas of discrete mathematics linking combinatorics and the theory of the simplest types of artificial neural networks. Neural networks have emerged as a key technology in many fields of application, and an understanding of the theories concerning what such systems can and cannot do is essential. Some classical results are presented with accessible proofs, together with some more recent perspectives, such as those obtained by considering decision lists. In addition, probabilistic models of neural network learning are discussed. Graph theory, some partially ordered set theory, computational complexity, and discrete probability are among the mathematical topics involved. Pointers to further reading and an extensive bibliography make this book a good starting point for research in discrete mathematics and neural networks.
This proceedings is designed for computer scientists, engineers and mathematicians interested in the use, design and analysis of algorithms, with special emphasis on questions of efficiency.
[3] P. J. CARSTENSEN, The complexity of some problems in parametric, linear,
and combinatorial programming, PhD thesis, Department of Mathematics,
University of Michigan, Ann Arbor, Mich., 1983. [4] E. COHEN AND N. MEGIDDO,
...
The annual Workshop on Algorithm Engineering and Experiments (ALENEX) provides a forum for the presentation of original research in all aspects of algorithm engineering, including the implementation and experimental evaluation of algorithms and data structures. The workshop was sponsored by SIAM, the Society for Industrial and Applied Mathematics, and SIGACT, the ACM Special Interest Group on Algorithms and Computation Theory. The aim of ANALCO is to provide a forum for the presentation of original research in the analysis of algorithms and associated combinatorial structures.
The ALENEX workshop provides a forum for the presentation of original research in the implementation and experimental evaluation of algorithms and data structures. This volume collects extended versions of the 12 papers that were selected for presentation.
This book introduces mathematicians to real applications from physiology. Using mathematics to analyze physiological systems, the authors focus on models reflecting current research in cardiovascular and pulmonary physiology. In particular, they present models describing blood flow in the heart and the cardiovascular system, as well as the transport of oxygen and carbon dioxide through the respiratory system and a model for baroreceptor regulation.
... widely used in the modeling of biology and medicine (J acquez, 1985) and can
describe both physical flows like the gas flow over the lung membrane and more
abstract flows such as a substance changing into another by chemical reaction.
