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| Preface | ||
| 1 | Algorithm Complexity: Two Simple Examples | 1 |
| 2 | Solving General Linear Functional Equations: An Application to Algorithm Complexity | 21 |
| 3 | Program Testing: A Problem | 45 |
| 4 | Optimal Program Testing | 65 |
| 5 | Optimal Choice of a Penalty Function: Simplest Case of Algorithm Design | 87 |
| 6 | Solving General Linear Differential Equations with Constant Coefficients: An Application to Constrained Optimization | 109 |
| 7 | Simulated Annealing: "Smooth" (Local) Discrete Optimization | 131 |
| 8 | Genetic Algorithms: "Non-Smooth" Discrete Optimization | 153 |
| 9 | Risc Computer Architecture and Internet Growth: Two Applications of Extrapolation | 175 |
| 10 | Systems of Differential Equations and Their Use in Computer-Related Extrapolation Problems | 197 |
| 11 | Network Congestion: An Example of Non-Linear Extrapolation | 225 |
| 12 | Neural Networks: A General Form of Non-Linear Extrapolation | 251 |
| 13 | Expert Systems and the Basics of Fuzzy Logic | 275 |
| 14 | Intelligent and Fuzzy Control | 297 |
| 15 | Randomness, Chaos, and Fractals | 323 |
| A | Simulated Annealing Revisited | 351 |
| B | Software Cost Estimation | 355 |
| C | Electronic Engineering: How to Describe PN-Junctions | 359 |
| D | Log-Normal Distribution Justified: An Application to Computational Statistics | 363 |
| E | Optimal Robust Statistical Methods | 375 |
| F | How to Avoid Paralysis of Neural Networks | 383 |
| G | Estimating Computer Prices | 385 |
| H | Allocating Bandwidth on Computer Networks | 391 |
| I | Algorithm Complexity Revisited | 397 |
| J | How Can a Robot Avoid Obstacles: Case Study of Real-Time Optimization | 401 |
| K | Discounting in Robot Control: A Case Study of Dynamic Optimization | 405 |
| Index | 407 |
| Price | Condition | Delivery | Seller | Action |
| $299.00 | Digital |
| WonderClub |
Oliver Hearsey
reviewed Applications of Continuous Mathematics to Computer Science on January 31, 2021
Applications of Continuous Mathematics to Computer Science
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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