Ravindran, A.,  1944- <br/><br/>
Engineering optimization :  methods and applications / 
A. Ravindran, K.M. Ragsdell, G.V. Reklaitis. 
 - 2nd ed. 
 - Hoboken, N.J. :  John Wiley & Sons,  c2006. 
 - xv, 667 p. :  ill., map ;  25 cm. 
<br/><br/> Reklaitis' name appears first on the earlier edition. 
<br/><br/>Includes bibliographical references and indexes. 
<br/><br/> Introduction to Optimization  Requirements for the Application of Optimization Methods  Defining the System Boundaries  Performance Criterion  Independent Variables  System Model  Applications of Optimization in Engineering  Design Applications  Operations and Planning Applications  Analysis and Data Reduction Applications  Classical Mechanics Applications  Taguchi System of Quality Engineering  Structure of Optimization Problems  Functions of a Single Variable  Properties of Single-Variable Functions  Optimality Criteria  Region Elimination Methods  Bounding Phase  Interval Refinement Phase  Comparison of Region Elimination Methods  Polynomial Approximation or Point Estimation Methods  Quadratic Estimation Methods  Successive Quadratic Estimation Method  Methods Requiring Derivatives  Newton-Raphson Method  Bisection Method  Secant Method  Cubic Search Method  Comparison of Methods  Functions of Several Variables  Optimality Criteria  Direct-Search Methods  The S[superscript 2] (Simplex Search) Method  Hooke-Jeeves Pattern Search Method  Powell's Conjugate Direction Method  Gradient-Based Methods  Cauchy's Method  Newton's Method  Modified Newton's Method  Marquardt's Method  Conjugate Gradient Methods  Quasi-Newton Methods  Trust Regions  Gradient-Based Algorithm  Numerical Gradient Approximations  Comparison of Methods and Numerical Results  Linear Programming  Formulation of Linear Programming Models  Graphical Solution of Linear Programs in Two Variables  Linear Program in Standard Form  Handling Inequalities  Handling Unrestricted Variables  Principles of the Simplex Method  Minimization Problems  Unbounded Optimum  Degeneracy and Cycling  Use of Artificial Variables  Two-Phase Simplex Method  Computer Solution of Linear Programs  Computer Codes  Computational Efficiency of the Simplex Method  Sensitivity Analysis in Linear Programming  Applications  Additional Topics in Linear Programming  Duality Theory  Dual Simplex Method  Interior Point Methods  Integer Programming  Goal Programming  Constrained Optimality Criteria  Equality-Constrained Problems  Lagrange Multipliers  Economic Interpretation of Lagrange Multipliers  Kuhn-Tucker Conditions  Kuhn-Tucker Conditions or Kuhn-Tucker Problem  Interpretation of Kuhn-Tucker Conditions  Kuhn-Tucker Theorems  Saddlepoint Conditions  Second-Order Optimality Conditions  Generalized Lagrange Multiplier Method  Generalization of Convex Functions  Transformation Methods  Penalty Concept  Various Penalty Terms  Choice of Penalty Parameter R  Algorithms, Codes, and Other Contributions  Method of Multipliers  Penalty Function  Multiplier Update Rule  Penalty Function Topology  Termination of the Method  MOM Characteristics  Choice of R-Problem Scale  Variable Bounds  Other MOM-Type Codes  Constrained Direct Search  Problem Preparation  Treatment of Equality Constraints  Generation of Feasible Starting Points  Adaptations of Unconstrained Search Methods  Difficulties in Accommodating Constraints  Complex Method  Random-Search Methods  Direct Sampling Procedures  Combined Heuristic Procedures  Linearization Methods for Constrained Problems  Direct Use of Successive Linear Programs  Linearly Constrained Case  General Nonlinear Programming Case  Discussion and Applications  Separable Programming  Single-Variable Functions  Multivariable Separable Functions  Linear Programming Solutions of Separable Problems  Discussion and Applications  Direction Generation Methods Based on Linearization  Method of Feasible Directions  Basic Algorithm  Active Constraint Sets and Jamming  Simplex Extensions for Linearly Constrained Problems  Convex Simplex Method  Reduced Gradient Method  Convergence Acceleration  Generalized Reduced Gradient Method  Implicit Variable Elimination  Basic GRG Algorithm  Extensions of Basic Method  Computational Considerations  Design Application  Problem Statement  General Formulation  Model Reduction and Solution  Quadratic Approximation Methods for Constrained Problems  Direct Quadratic Approximation  Quadratic Approximation of the Lagrangian Function  Variable Metric Methods for Constrained Optimization  Problem Scaling  Constraint Inconsistency  Modification of H[superscript (t)]  Comparison of GRG with CVM  Structured Problems and Algorithms  Integer Programming  Formulation of Integer Programming Models  Solution of Integer Programming Problems  Guidelines on Problem Formulation and Solution  Quadratic Programming  Applications of Quadratic Programming  Kuhn-Tucker Conditions  Complementary Pivot Problems  Goal Programming  Comparison of Constrained Optimization Methods  Software Availability  A Comparison Philosophy  Brief History of Classical Comparative Experiments  Preliminary and Final Results  Strategies for Optimization Studies  Model Formulation  Levels of Modeling  Types of Models  Problem Implementation  Model Assembly  Preparation for Solution  Execution Strategies  Solution Evaluation  Solution Validation  Sensitivity Analysis  Engineering Case Studies  Optimal Location of Coal-Blending Plants by Mixed-Integer Programming  Problem Description  Model Formulation  Results  Optimization of an Ethylene Glycol-Ethylene Oxide Process  Problem Description  Model Formulation  Problem Preparation  Discussion of Optimization Runs  Optimal Design of a Compressed Air Energy Storage System  Problem Description  Model Formulation  Numerical Results  Review of Linear Algebra  Set Theory  Vectors  Matrices  Matrix Operations  Determinant of a Square Matrix  Inverse of a Matrix  Condition of a Matrix  Sparse Matrix  Quadratic Forms  Principal Minor  Completing the Square  Convex Sets  Convex and Concave Functions  Gauss-Jordan Elimination Scheme  1  1 --  1.1  2 --  1.1.1  2 --  1.1.2  3 --  1.1.3  4 --  1.1.4  5 --  1.2  6 --  1.2.1  8 --  1.2.2  15 --  1.2.3  20 --  1.2.4  26 --  1.2.5  27 --  1.3  28 --  2  32 --  2.1  32 --  2.2  35 --  2.3  45 --  2.3.1  46 --  2.3.2  48 --  2.3.3  53 --  2.4  55 --  2.4.1  56 --  2.4.2  58 --  2.5  61 --  2.5.1  61 --  2.5.2  63 --  2.5.3  64 --  2.5.4  65 --  2.6  69 --  3  78 --  3.1  80 --  3.2  84 --  3.2.1  86 --  3.2.2  92 --  3.2.3  97 --  3.3  108 --  3.3.1  109 --  3.3.2  111 --  3.3.3  115 --  3.3.4  116 --  3.3.5  117 --  3.3.6  123 --  3.3.7  127 --  3.3.8  128 --  3.3.9  129 --  3.4  130 --  4  149 --  4.1  149 --  4.2  154 --  4.3  158 --  4.3.1  159 --  4.3.2  159 --  4.4  161 --  4.4.1  172 --  4.4.2  173 --  4.4.3  174 --  4.4.4  174 --  4.4.5  176 --  4.5  177 --  4.5.1  177 --  4.5.2  179 --  4.6  180 --  4.7  183 --  4.8  183 --  4.8.1  184 --  4.8.2  188 --  4.8.3  189 --  4.8.4  205 --  4.8.5  205 --  5  218 --  5.1  218 --  5.2  219 --  5.3  224 --  5.4  225 --  5.4.1  226 --  5.4.2  228 --  5.5  229 --  5.6  235 --  5.7  238 --  5.8  245 --  5.9  249 --  6  260 --  6.1  261 --  6.1.1  262 --  6.1.2  277 --  6.2  279 --  6.3  282 --  6.3.1  283 --  6.3.2  283 --  6.3.3  284 --  6.3.4  285 --  6.3.5  286 --  6.3.6  289 --  6.3.7  289 --  6.3.8  293 --  7  305 --  7.1  306 --  7.1.1  306 --  7.1.2  309 --  7.2  309 --  7.2.1  310 --  7.2.2  312 --  7.3  322 --  7.3.1  322 --  7.3.2  326 --  8  336 --  8.1  337 --  8.1.1  337 --  8.1.2  346 --  8.1.3  355 --  8.2  359 --  8.2.1  359 --  8.2.2  362 --  8.2.3  364 --  8.2.4  368 --  9  378 --  9.1  378 --  9.1.1  380 --  9.1.2  383 --  9.2  388 --  9.2.1  389 --  9.2.2  399 --  9.2.3  403 --  9.3  406 --  9.3.1  406 --  9.3.2  410 --  9.3.3  419 --  9.3.4  427 --  9.4  432 --  9.4.1  433 --  9.4.2  434 --  9.4.3  437 --  10  450 --  10.1  451 --  10.2  456 --  10.3  464 --  10.4.1  470 --  10.4.2  470 --  10.4.3  471 --  10.4.4  471 --  11  481 --  11.1  481 --  11.1.1  482 --  11.1.2  484 --  11.1.3  492 --  11.2  494 --  11.2.1  494 --  11.2.2  498 --  11.3  499 --  11.4  507 --  12  530 --  12.1  530 --  12.2  531 --  12.3  533 --  12.3.1  535 --  13  542 --  13.1  543 --  13.1.1  544 --  13.1.2  548 --  13.2  552 --  13.2.1  553 --  13.2.2  554 --  13.2.3  580 --  13.3  588 --  13.3.1  589 --  13.3.2  590 --  14  603 --  14.1  603 --  14.1.1  604 --  14.1.2  604 --  14.1.3  609 --  14.2  610 --  14.2.1  610 --  14.2.2  612 --  14.2.3  618 --  14.2.4  618 --  14.3  621 --  14.3.1  621 --  14.3.2  622 --  14.3.3  627 --  Appendix A  633 --  A.1  633 --  A.2  633 --  A.3  634 --  A.3.1  635 --  A.3.2  637 --  A.3.3  637 --  A.3.4  639 --  A.3.5  639 --  A.4  640 --  A.4.1  641 --  A.4.2  642 --  A.5  646 --  Appendix B  648 --  Appendix C  651. 
<br/><br/><label>ISBN: </label>0471558141 (cloth) 9780471558149 
<br/><br/><label>LCCN: </label>2005044611 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Engineering--Mathematical models.<br/>Mathematical optimization.
<br/><br/><label>LC Class. No.: </label>TA342  / .R44 2006 
<br/><br/><label>Dewey Class. No.: </label>620/.0042