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| Preface | ||
| 1 | Analysis of Interior-Point Methods for Linear Programming Problems with Variable Upper Bounds | 1 |
| 2 | On the Complexity of the Simplex Method | 25 |
| 3 | The Linear Complementarity Problem | 39 |
| 4 | A Direct Search Optimization Method that Models the Objective and Constraint Functions by Linear Interpolation | 51 |
| 5 | A Truncated SQP Algorithm for Large Scale Nonlinear Programming Problems | 69 |
| 6 | Performance of a Multifrontal Scheme for Partially Separable Optimization | 79 |
| 7 | Towards Second-Order Methods for Structured Nonsmooth Optimization | 97 |
| 8 | Homotopy Methods in Control System Design and Analysis | 111 |
| 9 | How to Properly Relax Delayed Controls | 145 |
| 10 | On Operator Extensions: the Algebraic Theory Approach | 155 |
| 11 | Global Space-time Finite Element Methods for Time-dependent Convection Diffusion Problems | 165 |
| 12 | Eulerian-Lagrangian Localized Adjoint Methods for Variable-Coefficient Advective-Diffusive-Reactive Equations in Groundwater Contaminant Transport | 185 |
| 13 | The Communication Patterns of Nested Preconditionings for Massively Parallel Architectures | 207 |
| 14 | Smoothness and Superconvergence for Approximate Solutions to the One Dimensional Monoenergetic Transport Equation | 219 |
| 15 | Experiments with the Power and Arnoldi Methods for Solving the Two-Group Neutron Diffusion Eigenvalue Problem | 233 |
| 16 | Computational Study of a Free-Boundary Model | 245 |
| 17 | Numerical Approximation to a Class of Weakly Singular Integral Operators | 261 |
| 18 | Directional Second Derivative of the Regularized Function that Smoothes the Min-Max Problem | 273 |
| Price | Condition | Delivery | Seller | Action |
| $99.99 | Digital |
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Suzanne Combs
reviewed Advances In Optimization And Numerical Analysis, Proceedings Of The Sixth Workshop On Optimization And Numerical Analysis, Oaxaca, Mexico on February 22, 2017
Advances In Optimization And Numerical Analysis
If you read this book as first time in order to understand Computational Complexity, then you're probably not going to like it. You need to read Sipser's textbook before you read this book and make sure that you have a strong background in discrete mathematics (if not, then see Rosen's textbook in Discrete Mathematics). This book is recommended by best people in complexity and algorithms such as Scott Aaronson, Sipser, etc. You need to solve as many exercises as you go.
The book has five parts:
Part I: Algorithms
Part II: Logic
Part III: P and NP
Part IV: Inside P
Part V: Beyond NP
The most important parts are in Part I, III.
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