Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
Written by a leading expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It provides readers with a full treatment of the smoothing technique, which has tremendously extended the abilities of gradient-type methods. Several powerful approaches in structural optimization, including optimization in relative scale and polynomial-time interior-point methods, are also discussed in detail.
Researchers in theoretical optimization as well as professionals working on optimization problems will find this book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Based on the author’s lectures, it can naturally serve as the basis for introductory and advanced courses in convex optimization for students in engineering, economics, computer science and mathematics.| Country | USA |
| Brand | Springer |
| Manufacturer | Springer |
| Binding | Hardcover |
| ItemPartNumber | 52380237 |
| ReleaseDate | 2018-11-20 |
| UnitCount | 1 |
| EANs | 9783319915777 |
