Zhouchen Lin is a leading expert in the fields of machine learning and computer vision. He is currently a Professor at the Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University. He served as an area chair for several prestigious conferences, including CVPR, ICCV, ICML, NIPS, AAAI and IJCAI. He is an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and the International Journal of Computer Vision. He is a Fellow of IAPR and IEEE.Huan Li received his Ph.D. degree in machine learning from Peking University in 2019. He is currently an Assistant Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His current research interests include optimization and machine learning.Cong Fang received his Ph.D. degree from Peking University in 2019. He is currently a Postdoctoral Researcher at Princeton University. His research interests include machine learning and optimization.

• Chapter 1. Introduction.- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization.- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization.- Chapter 4. Accelerated Algorithms for Nonconvex Optimization.- Chapter 5. Accelerated Stochastic Algorithms.- Chapter 6. Accelerated Paralleling Algorithms.- Chapter 7. Conclusions.-

Zhouchen Lin is a leading expert in the fields of machine learning and computer vision. He is currently a Professor at the Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University. He served as an area chair for several prestigious conferences, including CVPR, ICCV, ICML, NIPS, AAAI and IJCAI. He is an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and the International Journal of Computer Vision. He is a Fellow of IAPR and IEEE.Huan Li received his Ph.D. degree in machine learning from Peking University in 2019. He is currently an Assistant Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His current research interests include optimization and machine learning.Cong Fang received his Ph.D. degree from Peking University in 2019. He is currently a Postdoctoral Researcher at Princeton University. His research interests include machine learning and optimization.

• Chapter 1. Introduction.- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization.- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization.- Chapter 4. Accelerated Algorithms for Nonconvex Optimization.- Chapter 5. Accelerated Stochastic Algorithms.- Chapter 6. Accelerated Paralleling Algorithms.- Chapter 7. Conclusions.-

Zhouchen Lin is a leading expert in the fields of machine learning and computer vision. He is currently a Professor at the Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University. He served as an area chair for several prestigious conferences, including CVPR, ICCV, ICML, NIPS, AAAI and IJCAI. He is an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and the International Journal of Computer Vision. He is a Fellow of IAPR and IEEE.Huan Li received his Ph.D. degree in machine learning from Peking University in 2019. He is currently an Assistant Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His current research interests include optimization and machine learning.Cong Fang received his Ph.D. degree from Peking University in 2019. He is currently a Postdoctoral Researcher at Princeton University. His research interests include machine learning and optimization.

• Chapter 1. Introduction.- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization.- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization.- Chapter 4. Accelerated Algorithms for Nonconvex Optimization.- Chapter 5. Accelerated Stochastic Algorithms.- Chapter 6. Accelerated Paralleling Algorithms.- Chapter 7. Conclusions.-