This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader's intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum. Because of our focus on continuous problems, we have omitted discussion of important optimization topics such as discrete and stochastic optimization.

This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader's intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum. Because of our focus on continuous problems, we have omitted discussion of important optimization topics such as discrete and stochastic optimization.

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.

This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader's intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum. Because of our focus on continuous problems, we have omitted discussion of important optimization topics such as discrete and stochastic optimization.

This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader's intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum. Because of our focus on continuous problems, we have omitted discussion of important optimization topics such as discrete and stochastic optimization.

1 (p1): 1 Introduction 2 (p1-1): Mathematical Formulation 4 (p1-2): Example:A Transportation Problem 4 (p1-3): Continuous versus Discrete Optimization 6 (p1-4): Constrained and Unconstrained Optimization 6 (p1-5): Global and Local Optimization 7 (p1-6): Stochastic and Deterministic Optimization 7 (p1-7): Optimization Algorithms 8 (p1-8): Convexity 9 (p1-9): Notes and References 10 (p2): 2 Fundamentals of Unconstrained Optimization 13 (p2-1): 2.1 What Is a Solution? 15 (p2-1-1): Recognizing a Local Minimum 18 (p2-1-2): Nonsmooth Problems 19 (p2-2): 2.2 Overview of Algorithms 19 (p2-2-1): Two Strategies:Line Search and Trust Region 21 (p2-2-2): Search Directions for Line Search Methods 26 (p2-2-3): Models for Trust-Region Methods 27 (p2-2-4): Scaling 28 (p2-2-5): Rates of Convergence 29 (p2-2-6): R-Rates of Convergence 30 (p2-3): Notes and References 30 (p2-4): Exercises 34 (p3): 3 Line Search Methods 36 (p3-1): 3.1 Step Length 37 (p3-1-1): The Wolfe Conditions 41 (p3-1-2): The Goldstein Conditions 41 (p3-1-3): Sufficient Decrease and Backtracking 43 (p3-2): 3.2 Convergence of Line Search Methods 46 (p3-3): 3.3 Rate of Convergence 47 (p3-3-1): Convergence Rate of Steepest Descent 49 (p3-3-2): Quasi-Newton Methods 51 (p3-3-3): Newton’s Method 53 (p3-3-4): Coordinate Descent Methods 55 (p3-4): 3.4 Step-Length Selection Algorithms 56 (p3-4-1): Interpolation 58 (p3-4-2): The Initial Step Length 58 (p3-4-3): A Line Search Algorithm for the Wolfe Conditions 61 (p3-5): Notes and References 62 (p3-6): Exercises 64 (p4): 4 Trust-Region Methods 67 (p4-1): Outline of the Algorithm 69 (p4-2): 4.1 The Cauchy Point and Related Algorithms 69 (p4-2-1): The Cauchy Point 70 (p4-2-2): Improving on the Cauchy Point 71 (p4-2-3): The Dogleg Method 74 (p4-2-4): Two-Dimensional Subspace Minimization 75 (p4-2-5): Steihaug’s Approach 77 (p4-3): 4.2 Using Nearly Exact Solutions to the Subproblem 77 (p4-3-1): Characterizing Exact Solutions 78 (p4-3-2): Calculating Nearly Exact Solutions 82 (p4-3-3): The Hard Case 84 (p4-3-4): Proof of Theorem 4.3 87 (p4-4): 4.3 Global Convergence 87 (p4-4-1): Reduction Obtained by the Cauchy Point 89 (p4-4-2): Convergence to Stationary Points 93 (p4-4-3): Convergence of Algorithms Based on Nearly Exact Solutions 94 (p4-5): 4.4 Other Enhancements 94 (p4-5-1): Scaling 96 (p4-5-2): Non-Euclidean Trust Regions 97 (p4-6): Notes and References 97 (p4-7): Exercises 100 (p5): 5 Conjugate Gradient Methods 102 (p5-1): 5.1 The Linear Conjugate Gradient Method 102 (p5-1-1): Conjugate Direction Methods 107 (p5-1-2): Basic Properties of the Conjugate Gradient Method 111 (p5-1-3): A Practical Form of the Conjugate Gradient Method 112 (p5-1-4): Rate of Convergence 118 (p5-1-5): Preconditioning 119 (p5-1-6): Practical Preconditioners 120 (p5-2): 5.2 Nonlinear Conjugate Gradient Methods 120 (p5-2-1): The Fletcher-Reeves Method 121 (p5-2-2): The Polak-Ribiere Method 122 (p5-2-3): Quadratic Termination and Restarts 124 (p5-2-4): Numerical Performance 124 (p5-2-5): Behavior of the Fletcher-Reeves Method 127 (p5-2-6): Global Convergence 131 (p5-3): Notes and References 132 (p5-4): Exercises 134 (p6): 6 Practical Newton Methods 136 (p6-1): 6.1 Inexact Newton Steps 139 (p6-2): 6.2 Line Search Newton Methods 139 (p6-2-1): Line Search Newton-CG Method 141 (p6-2-2): Modified Newton’s Method 142 (p6-3): 6.3 Hessian Modifications 143 (p6-3-1): Eigenvalue Modification 144 (p6-3-2): Adding a Multiple of the Identity 145 (p6-3-3): Modified Cholesky Factorization 150 (p6-3-4): Gershgorin Modification 151 (p6-3-5): Modified Symmetric Indefinite Factorization 154 (p6-4): 6.4 Trust-Region Newton Methods 154 (p6-4-1): Newton-Dogleg and Subspace-Minimization Methods 155 (p6-4-2): Accurate Solution of the Trust-Region Problem 156 (p6-4-3): Trust-Region Newton-CG Method 157 (p6-4-4): Preconditioning the Newton-CG Method 159 (p6-4-5): Local Convergence of Trust-Region Newton Methods 162 (p6-5): Notes and References 162 (p6-6): Exercises 164 (p7): 7 Calculating Derivatives 166 (p7-1): 7.1 Finite-Difference Derivative Approximations 166 (p7-1-1): Approximating the Gradient 169 (p7-1-2): Approximating a Sparse Jacobian 173 (p7-1-3): Approximating the Hessian 174 (p7-1-4): Approximating a Sparse Hessian 176 (p7-2): 7.2 Automatic Differentiation 177 (p7-2-1): An Example 178 (p7-2-2): The Forward Mode 179 (p7-2-3): The Reverse Mode 183 (p7-2-4): Vector Functions and Partial Separability 184 (p7-2-5): Calculating Jacobians of Vector Functions 185 (p7-2-6): Calculating Hessians:Forward Mode 187 (p7-2-7): Calculating Hessians:Reverse Mode 188 (p7-2-8): Current Limitations 189 (p7-3): Notes and References 189 (p7-4): Exercises 192 (p8): 8 Quasi-Newton Methods 194 (p8-1): 8.1 The BFGS Method 199 (p8-1-1): Properties of the BFGS Method 200 (p8-1-2): Implementation 202 (p8-2): 8.2 The SR1 Method 205 (p8-2-1): Properties of SR1 Updating 207 (p8-3): 8.3 The Broyden Class 209 (p8-3-1): Properties of the Broyden Class 211 (p8-4): 8.4 Convergence Analysis 211 (p8-4-1): Global Convergence of the BFGS Method 214 (p8-4-2): Superlinear Convergence of BFGS 218 (p8-4-3): Convergence Analysis of the SR1 Method 219 (p8-5): Notes and References 220 (p8-6): Exercises 222 (p9): 9 Large-Scale Quasi-Newton and Partially Separable Optimization 224 (p9-1): 9.1 Limited-Memory BFGS 227 (p9-1-1): Relationship with Conjugate Gradient Methods 229 (p9-2): 9.2 General Limited-Memory Updating 230 (p9-2-1): Compact Representation of BFGS Updating 232 (p9-2-2): SR1 Matrices 232 (p9-2-3): Unrolling the Update 233 (p9-3): 9.3 Sparse Quasi-Newton Updates 235 (p9-4): 9.4 Partially Separable Functions 236 (p9-4-1): A Simple Example 237 (p9-4-2): Internal Variables 240 (p9-5): 9.5 Invariant Subspaces and Partial Separability 242 (p9-5-1): Sparsity vs.Partial Separability 243 (p9-5-2): Group Partial Separability 244 (p9-6): 9.6 Algorithms for Partially Separable Functions 244 (p9-6-1): Exploiting Partial Separability in Newton’s Method 245 (p9-6-2): Quasi-Newton Methods for Partially Separable Functions 247 (p9-7): Notes and References 248 (p9-8): Exercises 250 (p10): 10 Nonlinear Least-Squares Problems 253 (p10-1): 10.1 Background 253 (p10-1-1): Modeling,Regression,Statistics 256 (p10-1-2): Linear Least-Squares Problems 259 (p10-2): 10.2 Algorithms for Nonlinear Least-Squares Problems 259 (p10-2-1): The Gauss-Newton Method 262 (p10-2-2): The Levenberg-Marquardt Method 264 (p10-2-3): Implementation of the Levenberg-Marquardt Method 266 (p10-2-4): Large-Residual Problems 269 (p10-2-5): Large-Scale Problems 271 (p10-3): 10.3 Orthogonal Distance Regression 273 (p10-3-1): Notes and References 274 (p10-3-2): Exercises 276 (p11): 11 Nonlinear Equations 281 (p11-1): 11.1 Local Algorithms 281 (p11-1-1): Newton’s Method for Nonlinear Equations 284 (p11-1-2): Inexact Newton Methods 286 (p11-1-3): Broyden’s Method 290 (p11-1-4): Tensor Methods 292 (p11-2): 11.2 Practical Methods 292 (p11-2-1): Merit Functions 294 (p11-2-2): Line Search Methods 298 (p11-2-3): Trust-Region Methods 304 (p11-3): 11.3 Continuation/Homotopy Methods 304 (p11-3-1): Motivation 306 (p11-3-2): Practical Continuation Methods 310 (p11-4): Notes and References 311 (p11-5): Exercises 314 (p12): 12 Theory of Constrained Optimization 316 (p12-1): Local and Global Solutions 317 (p12-2): Smoothness 319 (p12-3): 12.1 Examples 319 (p12-3-1): A Single Equality Constraint 321 (p12-3-2): A Single Inequality Constraint 324 (p12-3-3): Two Inequality Constraints 327 (p12-4): 12.2 First-Order Optimality Conditions 327 (p12-4-1): Statement of First-Order Necessary Conditions 330 (p12-4-2): Sensitivity 331 (p12-5): 12.3 Derivation of the First-Order Conditions 332 (p12-5-1): Feasible Sequences 336 (p12-5-2): Characterizing Limiting Directions:Constraint Qualifications 339 (p12-5-3): Introducing Lagrange Multipliers 341 (p12-5-4): Proof of Theorem 12.1 342 (p12-6): 12.4 Second-Order Conditions 348 (p12-6-1): Second-Order Conditions and Projected Hessians 350 (p12-6-2): Convex Programs 351 (p12-7): 12.5 Other Constraint Qualifications 354 (p12-8): 12.6 A Geometric Viewpoint 357 (p12-9): Notes and References 358 (p12-10): Exercises 362 (p13): 13 Linear Programming&The Simplex Method 364 (p13-1): Linear Programming 366 (p13-2): 13.1 Optimality and Duality 366 (p13-2-1): Optimality Conditions 367 (p13-2-2): The Dual Problem 370 (p13-3): 13.2 Geometry of the Feasible Set 370 (p13-3-1): Basic Feasible Points 372 (p13-3-2): Vertices of the Feasible Polytope 374 (p13-4): 13.3 The Simplex Method 374 (p13-4-1): Outline of the Method 377 (p13-4-2): Finite Termination of the Simplex Method 378 (p13-4-3): A Single Step of the Method 379 (p13-5): 13.4 Linear Algebra in the Simplex Method 383 (p13-6): 13.5 Other (Important) Details 383 (p13-6-1): Pricing and Selection of the Entering Index 386 (p13-6-2): Starting the Simplex Method 389 (p13-6-3): Degenerate Steps and Cycling 391 (p13-7): 13.6 Where Does the Simplex Method Fit? 392 (p13-8): Notes and References 393 (p13-9): Exercises 394 (p14): 14 Linear Programming:Interior-Point Methods 396 (p14-1): 14.1 Primal-Dual Methods 396 (p14-1-1): Outline 399 (p14-1-2): The Central Path 401 (p14-1-3): A Primal-Dual Framework 402 (p14-1-4): Path-Following Methods 404 (p14-2): 14.2 A Practical Primal-Dual Algorithm 408 (p14-2-1): Solving the Linear Systems 409 (p14-3): 14.3 Other Primal-Dual Algorithms and Extensions 409 (p14-3-1): Other Path-Following Methods 409 (p14-3-2): Potential-Reduction Methods 410 (p14-3-3): Extensions 411 (p14-4): 14.4 Analysis of Algorithm 14.2 416 (p14-4-1): Notes and References 417 (p14-4-2): Exercises 420 (p14-5): 15 Fundamentals of Algorithms for Nonlinear Constrained Optimization 422 (p14-5-1): Initial Study of a Problem 423 (p14-6): 15.1 Categorizing Optimization Algorithms 426 (p14-7): 15.2 Elimination of Variables 427 (p14-7-1): Simple Elimination for Linear Constraints 430 (p14-7-2): General Reduction Strategies for Linear Constraints 434 (p14-7-3): The Effect of Inequality Constraints 434 (p14-8): 15.3 Measuring Progress:Merit Functions 437 (p14-9): Notes and References 438 (p14-10): Exercises 440 (p15): 16 Quadratic Programming 442 (p15-1): An Example:Portfolio Optimization 443 (p15-2): 16.1 Equality-Constrained Quadratic Programs 444 (p15-2-1): Properties of Equality-Constrained QPs 447 (p15-3): 16.2 Solving the KKT System 448 (p15-3-1): Direct Solution of the KKT System 449 (p15-3-2): Range-Space Method 450 (p15-3-3): Null-Space Method 452 (p15-3-4): A Method Based on Conjugacy 453 (p15-4): 16.3 Inequality-Constrained Problems 454 (p15-4-1): Optimality Conditions for Inequality-Constrained Problems 455 (p15-4-2): Degeneracy 457 (p15-5): 16.4 Active-Set Methods for Convex QP 461 (p15-5-1): Specification of the Active-Set Method for Convex QP 463 (p15-5-2): An Example 465 (p15-5-3): Further Remarks on the Active-Set Method 466 (p15-5-4): Finite Termination of the Convex QP Algorithm 467 (p15-5-5): Updating Factorizations 470 (p15-6): 16.5 Active-Set Methods for Indefinite QP 472 (p15-6-1): Illustration 474 (p15-6-2): Choice of Starting Point 475 (p15-6-3): Failure of the Active-Set Method 475 (p15-6-4): Detecting Indefiniteness Using the LBLT Factorization 476 (p15-7): 16.6 The Gradient-Projection Method 477 (p15-7-1): Cauchy Point Computation 480 (p15-7-2): Subspace Minimization 481 (p15-8): 16.7 Interior-Point Methods 484 (p15-8-1): Extensions and Comparison with Active-Set Methods 484 (p15-9): 16.8 Duality 485 (p15-10): Notes and References 486 (p15-11): Exercises 490 (p16): 17 Penalty,Barrier,and Augmented Lagrangian Methods 492 (p16-1): 17.1 The Quadratic Penalty Method 492 (p16-1-1): Motivation 494 (p16-1-2): Algorithmic Framework 495 (p16-1-3): Convergence of the Quadratic Penalty Function 500 (p16-2): 17.2 The Logarithmic Barrier Method 500 (p16-2-1): Properties of Logarithmic Barrier Functions 505 (p16-2-2): Algorithms Based on the Log-Barrier Function 507 (p16-2-3): Properties of the Log-Barrier Function and Framework 17.2 509 (p16-2-4): Handling Equality Constraints 510 (p16-2-5): Relationship to Primal-Dual Methods 512 (p16-3): 17.3 Exact Penalty Functions 513 (p16-4): 17.4 Augmented Lagrangian Method 513 (p16-4-1): Motivation and Algorithm Framework 516 (p16-4-2): Extension to Inequality Constraints 518 (p16-4-3): Properties of the Augmented Lagrangian 521 (p16-4-4): Practical Implementation 523 (p16-5): 17.5 Sequential Linearly Constrained Methods 525 (p16-6): Notes and References 526 (p16-7): Exercises 528 (p17): 18 Sequential Quadratic Programming 530 (p17-1): 18.1 Local SQP Method 531 (p17-1-1): SQP Framework 533 (p17-1-2): Inequality Constraints 534 (p17-1-3): IQP vs.EQP 534 (p17-2): 18.2 Preview of Practical SQP Methods 536 (p17-3): 18.3 Step Computation 536 (p17-3-1): Equality Constraints 538 (p17-3-2): Inequality Constraints 539 (p17-4): 18.4 The Hessian of the Quadratic Model 540 (p17-4-1): Full Quasi-Newton Approximations 541 (p17-4-2): Hessian of Augmented Lagrangian 542 (p17-4-3): Reduced-Hessian Approximations 544 (p17-5): 18.5 Merit Functions and Descent 547 (p17-6): 18.6 A Line Search SQP Method 548 (p17-7): 18.7 Reduced-Hessian SQP Methods 549 (p17-7-1): Some Properties of Reduced-Hessian Methods 550 (p17-7-2): Update Criteria for Reduced-Hessian Updating 551 (p17-7-3): Changes of Bases 552 (p17-7-4): A Practical Reduced-Hessian Method 553 (p17-8): 18.8 Trust-Region SQP Methods 555 (p17-8-1): Approach I:Shifting the Constraints 556 (p17-8-2): Approach II:Two Elliptical Constraints 557 (p17-8-3): Approach III:Sl 1 QP (Sequential l 1 Quadratic Programming) 560 (p17-9): 18.9 A Practical Trust-Region SQP Algorithm 563 (p17-10): 18.10 Rate of Convergence 565 (p17-10-1): Convergence Rate of Reduced-Hessian Methods 567 (p17-11): 18.11 The Maratos Effect 570 (p17-11-1): Second-Order Correction 571 (p17-11-2): Watchdog (Nonmonotone) Strategy 573 (p17-12): Notes and References 574 (p17-13): Exercises 576 (p18): A Background Material 577 (p18-1): A.1 Elements of Analysis,Geometry,Topology 577 (p18-1-1): Topology of the Euclidean Space Rn 580 (p18-1-2): Continuity and Limits 581 (p18-1-3): Derivatives 583 (p18-1-4): Directional Derivatives 584 (p18-1-5): Mean Value Theorem 585 (p18-1-6): Implicit Function Theorem 586 (p18-1-7): Geometry of Feasible Sets 591 (p18-1-8): Order Notation 592 (p18-1-9): Root-Finding for Scalar Equations 593 (p18-2): A.2 Elements of Linear Algebra 593 (p18-2-1): Vectors and Matrices 594 (p18-2-2): Norms 597 (p18-2-3): Subspaces 598 (p18-2-4): Eigenvalues,Eigenvectors,and the Singular-Value Decomposition 599 (p18-2-5): Determinant and Trace 600 (p18-2-6): Matrix Factorizations:Cholesky,LU,QR 605 (p18-2-7): Sherman-Morrison-Woodbury Formula 605 (p18-2-8): Interlacing Eigenvalue Theorem 606 (p18-2-9): Error Analysis and Floating-Point Arithmetic 608 (p18-2-10): Conditioning and Stability 611 (p19): References 625 (p20): Index

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

Quantum mechanics, shortly after invention, obtained applications in different area of human knowledge. Perhaps, the most attractive feature of quantum mechanics is its applications in such diverse area as, astrophysics, nuclear physics, atomic and molecular spectroscopy, solid state physics and nanotechnology, crystallography, chemistry, biotechnology, information theory, electronic engineering... This book is the result of an international attempt written by invited authors from over the world to response daily growing needs in this area. We do not believe that this book can cover all area of application of quantum mechanics but wish to be a good reference for graduate students and researchers.

This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader’s intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum. Because of our focus on continuous problems, we have omitted discussion of important optimization topics such as discrete and stochastic optimization.

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

"Numerical Optimization" presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. Drawing on their experiences in teaching, research, and consulting, the authors have produced a textbook that will be of interest to students and practitioners alike. Each chapter begins with the basic concepts and builds up gradually to the best techniques currently available. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. Above all, the authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. MMOR, Mathematical Methods of Operations Research, 2001 observes: "The book looks very suitable to be used in an graduate-level course in optimization for students in mathematics, operations research, engineering, and others. Moreover, it seems to be very helpful to do some self-studies in optimization, to complete own knowledge and can be a source of new ideas...I recommend this excellent book to everyone who is interested in optimization problems."

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

"Numerical Optimization" presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. Drawing on their experiences in teaching, research, and consulting, the authors have produced a textbook that will be of interest to students and practitioners alike. Each chapter begins with the basic concepts and builds up gradually to the best techniques currently available. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. Above all, the authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. MMOR, Mathematical Methods of Operations Research, 2001 observes: "The book looks very suitable to be used in an graduate-level course in optimization for students in mathematics, operations research, engineering, and others. Moreover, it seems to be very helpful to do some self-studies in optimization, to complete own knowledge and can be a source of new ideas...I recommend this excellent book to everyone who is interested in optimization problems."

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader's intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum. Because of our focus on continuous problems, we have omitted discussion of important optimization topics such as discrete and stochastic optimization.

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

本书作者斯蒂芬·J. 维尔吉利奥非常注重团队合作,这种合作既能消除儿童因营养过剩而导致的肥胖,又能帮助他们树立积极健康的生活方式。这本书展示了如何从事体育教育、课堂教师、学校志愿者等工作,通过引导大家以团队合作的方法来帮助学校满足健康的目标。书中还提供包括儿童肥胖症的新研究、统计和干预方法,新的体育活动指南,体育课程安排思路,新的健身游戏,如何教育自闭症儿童的方法,以及针对学龄期儿童的瑜伽练习。

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side

Developments in optimization theory, including emphasis on large problems and on interior-point methods for linear programming, have begun to appear in production software. Here is a reference tool that includes discussions of these areas and names software packages that incorporate the results of theoretical research. After an introduction to the major problem areas in optimization and an outline of the algorithms used to solve them, a data sheet is presented for each of the 75 software packages and libraries in the authors' survey. These include information on the capabilities of the packages, how to obtain them, and addresses for further information. Standard optimization paradigms are addressed -- linear, quadratic, and nonlinear programming; network optimization; unconstrained and bound-constrained optimization; least-squares problems; nonlinear equations; and integer programming. The most practical algorithms for the major fields of numerical optimization are outlined, and the software packages in which they are implemented are described. This format will aid current and potential users of optimization software in classifying the optimization problem to be solved, determining appropriate algorithms, and obtaining the software that implements those algorithms. Readers need only a basic knowledge of vector calculus and linear algebra to understand this book.

Developments in optimization theory, including emphasis on large problems and on interior-point methods for linear programming, have begun to appear in production software. Here is a reference tool that includes discussions of these areas and names software packages that incorporate the results of theoretical research. After an introduction to the major problem areas in optimization and an outline of the algorithms used to solve them, a data sheet is presented for each of the 75 software packages and libraries in the authors' survey. These include information on the capabilities of the packages, how to obtain them, and addresses for further information. Standard optimization paradigms are addressed -- linear, quadratic, and nonlinear programming; network optimization; unconstrained and bound-constrained optimization; least-squares problems; nonlinear equations; and integer programming. The most practical algorithms for the major fields of numerical optimization are outlined, and the software packages in which they are implemented are described. This format will aid current and potential users of optimization software in classifying the optimization problem to be solved, determining appropriate algorithms, and obtaining the software that implements those algorithms. Readers need only a basic knowledge of vector calculus and linear algebra to understand this book.

instructor's solution manual (official) for the book "Numerical Optimization, Second Edition" (2006), obtained right through Springer.com

Developments in optimization theory, including emphasis on large problems and on interior-point methods for linear programming, have begun to appear in production software. Here is a reference tool that includes discussions of these areas and names software packages that incorporate the results of theoretical research. After an introduction to the major problem areas in optimization and an outline of the algorithms used to solve them, a data sheet is presented for each of the 75 software packages and libraries in the authors' survey. These include information on the capabilities of the packages, how to obtain them, and addresses for further information. Standard optimization paradigms are addressed -- linear, quadratic, and nonlinear programming; network optimization; unconstrained and bound-constrained optimization; least-squares problems; nonlinear equations; and integer programming. The most practical algorithms for the major fields of numerical optimization are outlined, and the software packages in which they are implemented are described. This format will aid current and potential users of optimization software in classifying the optimization problem to be solved, determining appropriate algorithms, and obtaining the software that implements those algorithms. Readers need only a basic knowledge of vector calculus and linear algebra to understand this book.

1 (p0-1): 目录译者序前言本书的源起引言:所有事物背后的一面 17 (p0-2): 第1章 学校老师跟相扑运动员之间有何共同之处 53 (p0-3): 第2章 三K党跟房地产经纪人之间有何共同之处 87 (p0-4): 第3章 为什么毒品贩子仍然跟自己的妈妈住在一起 117 (p0-5): 第4章 犯罪分子都到哪里去了 147 (p0-6): 第5章 怎么才能成为完美的父母 181 (p0-7): 第6章 名字对孩子的未来有影响吗 *A Rogue Economist Explores the Hidden Side of Everything* Which is more dangerous, a gun or a swimming pool? What do schoolteachers and sumo wrestlers have in common? Why do drug dealers still live with their moms? How much do parents really matter? How did the legalization of abortion affect the rate of violent crime? These may not sound like typical questions for an economist to ask. But Steven D. Levitt is not a typical economist. He is a much-heralded scholar who studies the riddles of everyday life—from cheating and crime to sports and child-rearing—and whose conclusions turn the conventional wisdom on its head. Freakonomics is a ground-breaking collaboration between Levitt and Stephen J. Dubner, an award-winning author and journalist. They usually begin with a mountain of data and a simple, unasked question. Some of these questions concern life-and-death issues; others have an admittedly freakish quality. Thus the new field of study contained in this book: Freakonomics. Through forceful storytelling and wry insight, Levitt and Dubner show that economics is, at root, the study of incentives—how people get what they want, or need, especially when other people want or need the same thing. In Freakonomics, they explore the hidden side of ... well, everything. The inner workings of a crack gang. The truth about real-estate agents. The myths of campaign finance. The telltale marks of a cheating schoolteacher. The secrets of the Ku Klux Klan. What unites all these stories is a belief that the modern world, despite a great deal of complexity and downright deceit, is not impenetrable, is not unknowable, and—if the right questions are asked—is even more intriguing than we think. All it takes is a new way of...

Developments in optimization theory, including emphasis on large problems and on interior-point methods for linear programming, have begun to appear in production software. Here is a reference tool that includes discussions of these areas and names software packages that incorporate the results of theoretical research. After an introduction to the major problem areas in optimization and an outline of the algorithms used to solve them, a data sheet is presented for each of the 75 software packages and libraries in the authors' survey. These include information on the capabilities of the packages, how to obtain them, and addresses for further information. Standard optimization paradigms are addressed -- linear, quadratic, and nonlinear programming; network optimization; unconstrained and bound-constrained optimization; least-squares problems; nonlinear equations; and integer programming. The most practical algorithms for the major fields of numerical optimization are outlined, and the software packages in which they are implemented are described. This format will aid current and potential users of optimization software in classifying the optimization problem to be solved, determining appropriate algorithms, and obtaining the software that implements those algorithms. Readers need only a basic knowledge of vector calculus and linear algebra to understand this book.

Se aborda la problemática que no tienen al animal humano como eje gravitacional sino al fenómeno que se deriva de ser ese \'primate inquisitivo. La intención es ofrecer un amplio panorama de reflexiones que permitan acotar temas y optar por abordajes desde una gran gama de marcos teóricos y técnicas, a fin de dar respuestas verosímiles a pequeños pero importantes cuestionamientos, sobre la inquietud antropológica.

3 (p1): 第一部分 Visual J++应用程序 3 (p1-2): 第1章 普通控制台应用程序 3 (p1-3): 1．1 Hello World！项目 17 (p1-4): 1．2 让我们澄清Java性能不佳的谣传 23 (p1-5): 1．3 普通控制台应用程序的功能 40 (p1-6): 1．4 小结 41 (p1-7): 第2章 WFC介绍：I/O软件包 41 (p1-8): 2．1 Windows基础类 43 (p1-9): 2．2 I/O软件包 65 (p1-10): 2．3 小结 66 (p1-11): 3．1 Debug类 66 (p1-12): 第3章 WFC实用工具软件包 79 (p1-13): 3．2 容器（Container） 94 (p1-14): 3．3 小结 97 (p2): 第二部分 视窗化应用程序 97 (p2-2): 第4章 普通的视窗化应用程序 97 (p2-3): 4．1 为什么要花两次的时间来解决一个同样的问题 98 (p2-4): 4．2 问题 100 (p2-5): 4．3 抽象窗口工具包简介 112 (p2-6): 4．4 AWT的解决方案 125 (p2-7): 4．5 配置类型 126 (p2-8): 4．6 小结 127 (p2-9): 5．1 设计WindowedApp应用程序 127 (p2-10): 第5章 Microsoft Windows应用程序 134 (p2-11): 5．2 为WindowedApp程序添加操作 136 (p2-12): 5．3 完整的WindowedApp应用程序 158 (p2-13): 5．4 小结 159 (p2-14): 第6章 简单输入控件 159 (p2-15): 6．1 按钮 173 (p2-16): 6．2 列表框 194 (p2-17): 6．3 RICHEDIT控件 210 (p2-18): 6．4 小结 211 (p2-19): 第7章 菜单与对话框 211 (p2-20): 7．1 “文件”菜单 229 (p2-21): 7．2 “编辑”菜单 237 (p2-22): 7．3 添加编辑器的其余部分 243 (p2-23): 7．4 小结：设计完成的编辑器 245 (p2-24): 第8章 列表显示控件和树型显示控件 245 (p2-25): 8．1 ListView控件 261 (p2-26): 8．2 TreeView控件 271 (p2-27): 8．3 使用FileMgr将它们连接起来 293 (p2-28): 8．4 小结 294 (p2-29): 第9章 Graphics类 294 (p2-30): 9．1 屏幕上随手绘画 311 (p2-31): 9．2 文字绘画 325 (p2-32): 9．3 绘制更加复杂的结构 337 (p2-33): 9．4 GRAPHICS对象和WFC组件综合应用 358 (p2-34): 9．5 小结 359 (p2-35): 第10章 WFC应用程序包 359 (p2-36): 10．1 Time和Timer类 371 (p2-37): 10．2 多线程技术 410 (p2-38): 10．3 程序注册表 417 (p2-39): 10．4 小结 418 (p2-40): 第11章 J/Direct 419 (p2-41): 11．1 使用J/Direct CALL BUILDER 432 (p2-42): 11．2 J/DIRECT 揭秘 446 (p2-43): 11．3 访问自己的动态链接库 454 (p2-44): 11．4 使用J/Direct完成空闲事件处理 467 (p2-45): 11．5 小结 471 (p3): 第三部分 特殊论题 471 (p3-2): 第12章 数据库访问 471 (p3-3): 12．1 SQL语言 474 (p3-4): 12．2 ACTIVEX数据对象 475 (p3-5): 12．3 手工编写ADO应用程序 492 (p3-6): 12．4 应用程序向导 500 (p3-7): 12．5 使用数据工具箱控件 511 (p3-8): 12．6 通过局域网访问数据库 511 (p3-9): 12．7 小结 512 (p3-10): 第13章 创建控件 549 (p3-11): 13．2 自定义ActiveX控件 556 (p3-12): 13．3 小结 557 (p3-13): 第14章 小程序 557 (p3-14): 14．1 Applet基础 581 (p3-15): 14．2 Applet向导 588 (p3-16): 14．3 APPLET输入/输出 603 (p3-17): 14．4 小结 604 (p3-18): 第15章 Applet动画制作技术 604 (p3-19): 15．1 多线程的Applets 618 (p3-20): 15．2 动画制作 640 (p3-21): 15．3 小结 641 (p3-22): 第16章 WFC动态HTML软件包 641 (p3-23): 16．1 动态HTML标准 644 (p3-24): 16．2 客户端的DHTML 666 (p3-25): 16．3 服务器端的DTHML 675 (p3-26): 16．4 小结 676 (p3-27): 附录Java语法 676 (p3-28): Java表达式 681 (p3-29): 流程控制 684 (p3-30): 类信息 696 (p3-31): 其他与类相关的概念 700 (p3-32): 事件处理