Mathematics for Future Computing and Communications
For 80 years, mathematics has driven fundamental innovation in computing and communications. This timely book provides a panorama of some recent ideas in mathematics and how they will drive continued innovation in computing, communications and AI in the coming years. It provides a unique insight into how the new techniques that are being developed can be used to provide theoretical foundations for technological progress, just as mathematics was used in earlier times by Turing, von Neumann, Shannon and others. Edited by leading researchers in the field, chapters cover the application of new mathematics in computer architecture, software verification, quantum computing, compressed sensing, networking, Bayesian inference, machine learning, reinforcement learning and many other areas.
- Demonstrates the impact of new mathematics across a wide spectrum of research challenges in computing, communications and AI
- Discusses cutting-edge academic research and its applications in industry
- Looks forward with a chapter on prospects for future breakthroughs
Reviews & endorsements
' In a field in which depth is often privileged, the broad spectrum of topics offered in this book makes it a welcome addition for nonspecialist readers with some background who are eager to approach the use of mathematics in computing and communications … Recommended.' L. Benedicenti, Choice
Product details
December 2021Hardback
9781316513583
396 pages
257 × 176 × 21 mm
0.9kg
Available
Table of Contents
- Preface Liao Heng
- Part I. Computing: Introduction to Part I
- 1. Mathematics, models and architectures Bill McColl
- 2. Mathematics and software verification Chen Haibo and Gao Xin
- 3. Mathematics for quantum computing Kong Yunchuan
- 4. Mathematics for AI: categories, toposes, types Daniel Bennequin and Jean-Claude Belfiore
- Part II. Communications: Introduction to Part II
- 5. Mathematics and compressed sensing Zhang Rui and Long Zichao
- 6. Mathematics, information theory, and statistical physics Mérouane Debbah
- 7. Mathematics of data networking Li Zongpeng, Miao Lihua and Tang Siyu
- 8. Mathematics and network science Sun Jie
- Part III. Artificial Intelligence: Introduction to Part III
- 9. Mathematics, information and learning Tong Wen and Ge Yiqun
- 10. Mathematics and Bayesian inference Guo Kaiyang, Lv Wenlong and Zhang Jianfeng
- 11. Mathematics, optimization and machine learning Jiu Shangling
- 12. Mathematics of reinforcement learning Wu Shuang and Wang Jun
- Part IV. Future:
- 13. Mathematics and prospects for future breakthroughs Dang Wenshuan
- Editors and contributing authors.