Overview

This text emphasizes linear programming (LP) techniques to solve realistic applied problems. Coverage of the theoretical aspects is optional. Modelling of practical LP problems is stressed throughout, with sections on LP Models at the end of most chapters. Ideas from linear algebra are developed where needed. Thorough, useful coverage of the simplex method, duality, integer programming and implicit enumeration, column generation techniques, sparse matrix techniques, and Karmarkar's method is provided. Special techniques for handling large-scale problems efficiently are discussed in detail.

Full Product Details

Author:   James Calvert ,  William Voxman
Publisher:   Cengage Learning, Inc
Imprint:   Brooks/Cole
Dimensions:   Width: 18.00cm , Height: 2.90cm , Length: 24.20cm
Weight:   1.085kg
ISBN:  

9780155510272

ISBN 10:   0155510274
Pages:   655
Publication Date:   13 January 1989
Audience:   College/higher education ,  General/trade ,  Tertiary & Higher Education ,  General
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

Contents: K.H. Pribram, Foreword. Part I:Issues. L. Prigogine, Mind and Matter: Beyond the Cartesian Dualism. P.J. Werbos, Self-Organization: Reexamining the Basics. R. Shaw, E. Kadar, J. Kinsella-Shaw, Modelling Systems With Intentional Dynamics: A Lesson From Quantum Mechanics. J. Gyr, Psychophysics: The Self-Referent Holonomic Observer-Observed Relation. B. MacLennan, Continuous Computation and the Emergence of the Discrete. S. Hagan, M. Jibu, K. Yasue, Consciousness and Anesthesia: An Hypothesis Involving Biophoton Emission in the Microtubular Cytoskeleton of the Brain. D. Stassinopoulos, P. Bak, Self-Organization and Pavlov's Dogs: A Simple Model of the Brain. C. Game, Non-Equilibrium Thermodynamics and the Brain. Part II:Spike Trains and Networks. P. Cariani, As If Time Really Mattered: Temporal Strategies for Neural Coding of Sensory Information. M. Xie, K. Pribram, J. King, Are Neural Spike Trains Deterministically Chaotic or Stochastic Processes? P. Lansky, J-P. Rospars, Stochastic Model of Intensity Coding in Olfactory Neurons. M. Levine, Signal and Variability in Spike Trains. J. Segundo, J-F. Vibert, K. Pakdaman, M. Stiber, O.D. Martinez, Noise and the Neurosciences: A Long History, A Recent Revival and Some Theory. R. Bulsara, Towards Simplicity: Noise and Cooperation in the Perfect Integrator. A.R. Bulsara, S.B. Lowen, C.D. Rees, Cooperative Behavior in the Periodically Modulated Wiener Process. A. Longtin, A. Bulsara, D. Pierson, F. Moss, Bistability and the Dynamics of Periodically Forced Sensory Neurons. N.H. Farhat, M. Eldefrawy, S-Y. Lin, A Bifurcation Model of Neuronal of Spike Train Patterns: A Nonlinear Dynamic Systems Approach. H. Szu, J. Garcia, L. Zadeh, C.C. Hsu, J. DeWitte, Jr., G. Moon, D. Gobovic, M. Zaghloul, Neural Network Models for Chaotic-Fuzzy Information Processing. Part III:Image Processing. E.M. Sokolov, Vector Coding in Neuronal Nets: Color Vision. V. Glezer, The Perception of Visual Form. S. Neuenschwander, F. Varela, Visually-triggered Neuronal Oscillations in the Pigeon: An Autocorrelation Study of Tectal Activity. S. Bressler, Dynamic Self-Organization in the Brain as Observed by Transient Cortical Coherence. H. Erwin, The Application of Katchalsky Network Models to Radar Pattern Classification. J. King, M. Xie, B. Zheng, K. Pribram, Spectral Density Maps of Receptive Fields in the Rat's Somatosensory Cortex. Part IV:The Brain as Controller. V. Glezer, Perception. Double Dichotomy of Visual Brain. M. Goodale, Visual Pathways Supporting Perception and Action in the Primate Cerebral Cortex. C. Fowler, Auditory Objects: The Role of Motor Activity in Auditory Perception and Speech Perception. M. Clynes, Entities and Brain Organization: Logogenesis of Meaningful Time-Forms. R.B. Bolster, Role of the Frontal and Temporal Lobes in Scanning Visual Features. H. Crawford, Brain Systems Involved in Attention and Disattention (Hypnotic Analgesia) to Pain. P. Werbos, The Brain as a Neurocontroller: New Hypotheses and New Experimental Possibilities. K.H. Pribram, Afterword.

Reviews

1. LINEAR PROGRAMMING: AN OVERVIEW AND SAMPLE PROBLEMS. Product-mix Problems. Transportation and Assignment Problems. General Comments Regarding LP Models. Dynamic Planning. 2. GEOMETRIC AND ALGEBRAIC PRELIMINARIES. Half-spaces, Hyperplanes, and Convex Sets. Feasible Sets and Geometric Solutions of LP Problems. Proof of the Corner Point Theorem. Algebraic Preliminaries. Slack Variables and Constraints in Canonical Form. 3. THE SIMPLEX METHOD. Selection of Entering and Departing Basic Variables. Simplex Tableaus. Degeneracy, Cycling, and Convergence. The Two-Phase Method. The Two-Phase Method: Some Complications. The Big-M Method. LP Models. 4. THE SIMPLEX METHOD IN A MATRIX CONTEXT. Matrices. Elementary Matrices, Matrix Inverses, and Pivoting. Matrix Representation of the Simplex Method: Part I. Matrix Representation of the Simplex Method: Part II. LP Models. 5. THE REVISED SIMPLEX METHOD. A Faster Algorithm for Computer Solution. Preliminary Results for Use in Complexity Analysis. Space and Time Complexity of the Simplex Method. LP Models. 6. DUALITY. An Economic Interpretation of Duality. Primal and Dual Problems: A Special Case. Duality: The General Case. Duality Proofs. The Dual Simplex Model. LP Models. 7. SENSITIVITY ANALYSIS. What is Sensitivity Analysis? Changes in the Resource Column. Changes in Cost Coefficients. Using Duality in Sensitivity Analysis. Introduction of a New Variable. LP Models. 8. AN INTRODUCTION TO INTEGER PROGRAMMING. A Sampling of IP Problems. A Branch and Bound Method. Implicit Enumeration: Part I. Implicit Enumeration: Part II. Geometry Cutting Plane Method. LP Models. 9. THE TRANSPORTATION PROBLEM. Choosing the Entering Basic Variable. Choosing the Departing Basic Variable. Finding an Initial Basic Feasible Solution. 10. LARGE-SCALE PROBLEMS. LP Problems with Bounded Constraints. A Decomposition Procedure: The Master Problem. Solving the Master Problem. Final Words.

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