TY  - BOOK
AU  - Ravindran,A.
AU  - Reklaitis,G.V.
AU  - Ragsdell,K.M.
TI  - Engineering optimization: methods and applications 
SN  - 0471558141 (cloth)
AV  - TA342 .R44 2006
U1  - 620/.0042 22
PY  - 2006///
CY  - Hoboken, N.J.
PB  - John Wiley & Sons
KW  - Engineering
KW  - Mathematical models
KW  - Mathematical optimization
N1  - Reklaitis' name appears first on the earlier edition; Includes bibliographical references and indexes; 1; Introduction to Optimization; 1 --; 1.1; Requirements for the Application of Optimization Methods; 2 --; 1.1.1; Defining the System Boundaries; 2 --; 1.1.2; Performance Criterion; 3 --; 1.1.3; Independent Variables; 4 --; 1.1.4; System Model; 5 --; 1.2; Applications of Optimization in Engineering; 6 --; 1.2.1; Design Applications; 8 --; 1.2.2; Operations and Planning Applications; 15 --; 1.2.3; Analysis and Data Reduction Applications; 20 --; 1.2.4; Classical Mechanics Applications; 26 --; 1.2.5; Taguchi System of Quality Engineering; 27 --; 1.3; Structure of Optimization Problems; 28 --; 2; Functions of a Single Variable; 32 --; 2.1; Properties of Single-Variable Functions; 32 --; 2.2; Optimality Criteria; 35 --; 2.3; Region Elimination Methods; 45 --; 2.3.1; Bounding Phase; 46 --; 2.3.2; Interval Refinement Phase; 48 --; 2.3.3; Comparison of Region Elimination Methods; 53 --; 2.4; Polynomial Approximation or Point Estimation Methods; 55 --; 2.4.1; Quadratic Estimation Methods; 56 --; 2.4.2; Successive Quadratic Estimation Method; 58 --; 2.5; Methods Requiring Derivatives; 61 --; 2.5.1; Newton-Raphson Method; 61 --; 2.5.2; Bisection Method; 63 --; 2.5.3; Secant Method; 64 --; 2.5.4; Cubic Search Method; 65 --; 2.6; Comparison of Methods; 69 --; 3; Functions of Several Variables; 78 --; 3.1; Optimality Criteria; 80 --; 3.2; Direct-Search Methods; 84 --; 3.2.1; The S[superscript 2] (Simplex Search) Method; 86 --; 3.2.2; Hooke-Jeeves Pattern Search Method; 92 --; 3.2.3; Powell's Conjugate Direction Method; 97 --; 3.3; Gradient-Based Methods; 108 --; 3.3.1; Cauchy's Method; 109 --; 3.3.2; Newton's Method; 111 --; 3.3.3; Modified Newton's Method; 115 --; 3.3.4; Marquardt's Method; 116 --; 3.3.5; Conjugate Gradient Methods; 117 --; 3.3.6; Quasi-Newton Methods; 123 --; 3.3.7; Trust Regions; 127 --; 3.3.8; Gradient-Based Algorithm; 128 --; 3.3.9; Numerical Gradient Approximations; 129 --; 3.4; Comparison of Methods and Numerical Results; 130 --; 4; Linear Programming; 149 --; 4.1; Formulation of Linear Programming Models; 149 --; 4.2; Graphical Solution of Linear Programs in Two Variables; 154 --; 4.3; Linear Program in Standard Form; 158 --; 4.3.1; Handling Inequalities; 159 --; 4.3.2; Handling Unrestricted Variables; 159 --; 4.4; Principles of the Simplex Method; 161 --; 4.4.1; Minimization Problems; 172 --; 4.4.2; Unbounded Optimum; 173 --; 4.4.3; Degeneracy and Cycling; 174 --; 4.4.4; Use of Artificial Variables; 174 --; 4.4.5; Two-Phase Simplex Method; 176 --; 4.5; Computer Solution of Linear Programs; 177 --; 4.5.1; Computer Codes; 177 --; 4.5.2; Computational Efficiency of the Simplex Method; 179 --; 4.6; Sensitivity Analysis in Linear Programming; 180 --; 4.7; Applications; 183 --; 4.8; Additional Topics in Linear Programming; 183 --; 4.8.1; Duality Theory; 184 --; 4.8.2; Dual Simplex Method; 188 --; 4.8.3; Interior Point Methods; 189 --; 4.8.4; Integer Programming; 205 --; 4.8.5; Goal Programming; 205 --; 5; Constrained Optimality Criteria; 218 --; 5.1; Equality-Constrained Problems; 218 --; 5.2; Lagrange Multipliers; 219 --; 5.3; Economic Interpretation of Lagrange Multipliers; 224 --; 5.4; Kuhn-Tucker Conditions; 225 --; 5.4.1; Kuhn-Tucker Conditions or Kuhn-Tucker Problem; 226 --; 5.4.2; Interpretation of Kuhn-Tucker Conditions; 228 --; 5.5; Kuhn-Tucker Theorems; 229 --; 5.6; Saddlepoint Conditions; 235 --; 5.7; Second-Order Optimality Conditions; 238 --; 5.8; Generalized Lagrange Multiplier Method; 245 --; 5.9; Generalization of Convex Functions; 249 --; 6; Transformation Methods; 260 --; 6.1; Penalty Concept; 261 --; 6.1.1; Various Penalty Terms; 262 --; 6.1.2; Choice of Penalty Parameter R; 277 --; 6.2; Algorithms, Codes, and Other Contributions; 279 --; 6.3; Method of Multipliers; 282 --; 6.3.1; Penalty Function; 283 --; 6.3.2; Multiplier Update Rule; 283 --; 6.3.3; Penalty Function Topology; 284 --; 6.3.4; Termination of the Method; 285 --; 6.3.5; MOM Characteristics; 286 --; 6.3.6; Choice of R-Problem Scale; 289 --; 6.3.7; Variable Bounds; 289 --; 6.3.8; Other MOM-Type Codes; 293 --; 7; Constrained Direct Search; 305 --; 7.1; Problem Preparation; 306 --; 7.1.1; Treatment of Equality Constraints; 306 --; 7.1.2; Generation of Feasible Starting Points; 309 --; 7.2; Adaptations of Unconstrained Search Methods; 309 --; 7.2.1; Difficulties in Accommodating Constraints; 310 --; 7.2.2; Complex Method; 312 --; 7.3; Random-Search Methods; 322 --; 7.3.1; Direct Sampling Procedures; 322 --; 7.3.2; Combined Heuristic Procedures; 326 --; 8; Linearization Methods for Constrained Problems; 336 --; 8.1; Direct Use of Successive Linear Programs; 337 --; 8.1.1; Linearly Constrained Case; 337 --; 8.1.2; General Nonlinear Programming Case; 346 --; 8.1.3; Discussion and Applications; 355 --; 8.2; Separable Programming; 359 --; 8.2.1; Single-Variable Functions; 359 --; 8.2.2; Multivariable Separable Functions; 362 --; 8.2.3; Linear Programming Solutions of Separable Problems; 364 --; 8.2.4; Discussion and Applications; 368 --; 9; Direction Generation Methods Based on Linearization; 378 --; 9.1; Method of Feasible Directions; 378 --; 9.1.1; Basic Algorithm; 380 --; 9.1.2; Active Constraint Sets and Jamming; 383 --; 9.2; Simplex Extensions for Linearly Constrained Problems; 388 --; 9.2.1; Convex Simplex Method; 389 --; 9.2.2; Reduced Gradient Method; 399 --; 9.2.3; Convergence Acceleration; 403 --; 9.3; Generalized Reduced Gradient Method; 406 --; 9.3.1; Implicit Variable Elimination; 406 --; 9.3.2; Basic GRG Algorithm; 410 --; 9.3.3; Extensions of Basic Method; 419 --; 9.3.4; Computational Considerations; 427 --; 9.4; Design Application; 432 --; 9.4.1; Problem Statement; 433 --; 9.4.2; General Formulation; 434 --; 9.4.3; Model Reduction and Solution; 437 --; 10; Quadratic Approximation Methods for Constrained Problems; 450 --; 10.1; Direct Quadratic Approximation; 451 --; 10.2; Quadratic Approximation of the Lagrangian Function; 456 --; 10.3; Variable Metric Methods for Constrained Optimization; 464 --; 10.4.1; Problem Scaling; 470 --; 10.4.2; Constraint Inconsistency; 470 --; 10.4.3; Modification of H[superscript (t)]; 471 --; 10.4.4; Comparison of GRG with CVM; 471 --; 11; Structured Problems and Algorithms; 481 --; 11.1; Integer Programming; 481 --; 11.1.1; Formulation of Integer Programming Models; 482 --; 11.1.2; Solution of Integer Programming Problems; 484 --; 11.1.3; Guidelines on Problem Formulation and Solution; 492 --; 11.2; Quadratic Programming; 494 --; 11.2.1; Applications of Quadratic Programming; 494 --; 11.2.2; Kuhn-Tucker Conditions; 498 --; 11.3; Complementary Pivot Problems; 499 --; 11.4; Goal Programming; 507 --; 12; Comparison of Constrained Optimization Methods; 530 --; 12.1; Software Availability; 530 --; 12.2; A Comparison Philosophy; 531 --; 12.3; Brief History of Classical Comparative Experiments; 533 --; 12.3.1; Preliminary and Final Results; 535 --; 13; Strategies for Optimization Studies; 542 --; 13.1; Model Formulation; 543 --; 13.1.1; Levels of Modeling; 544 --; 13.1.2; Types of Models; 548 --; 13.2; Problem Implementation; 552 --; 13.2.1; Model Assembly; 553 --; 13.2.2; Preparation for Solution; 554 --; 13.2.3; Execution Strategies; 580 --; 13.3; Solution Evaluation; 588 --; 13.3.1; Solution Validation; 589 --; 13.3.2; Sensitivity Analysis; 590 --; 14; Engineering Case Studies; 603 --; 14.1; Optimal Location of Coal-Blending Plants by Mixed-Integer Programming; 603 --; 14.1.1; Problem Description; 604 --; 14.1.2; Model Formulation; 604 --; 14.1.3; Results; 609 --; 14.2; Optimization of an Ethylene Glycol-Ethylene Oxide Process; 610 --; 14.2.1; Problem Description; 610 --; 14.2.2; Model Formulation; 612 --; 14.2.3; Problem Preparation; 618 --; 14.2.4; Discussion of Optimization Runs; 618 --; 14.3; Optimal Design of a Compressed Air Energy Storage System; 621 --; 14.3.1; Problem Description; 621 --; 14.3.2; Model Formulation; 622 --; 14.3.3; Numerical Results; 627 --; Appendix A; Review of Linear Algebra; 633 --; A.1; Set Theory; 633 --; A.2; Vectors; 633 --; A.3; Matrices; 634 --; A.3.1; Matrix Operations; 635 --; A.3.2; Determinant of a Square Matrix; 637 --; A.3.3; Inverse of a Matrix; 637 --; A.3.4; Condition of a Matrix; 639 --; A.3.5; Sparse Matrix; 639 --; A.4; Quadratic Forms; 640 --; A.4.1; Principal Minor; 641 --; A.4.2; Completing the Square; 642 --; A.5; Convex Sets; 646 --; Appendix B; Convex and Concave Functions; 648 --; Appendix C; Gauss-Jordan Elimination Scheme; 651
UR  - http://catdir.loc.gov/catdir/enhancements/fy0653/2005044611-d.html
UR  - http://catdir.loc.gov/catdir/enhancements/fy0653/2005044611-t.html
UR  - http://catdir.loc.gov/catdir/enhancements/fy0826/2005044611-b.html
ER  -