报告人：高冰(香港科技大学)

报告题目：Gauss Newton method for phaseless recovery

报告摘要：In this talk, we introduce a concrete algorithm for phase retrieval problem, which aims to recover a signal from phaseless measurements. In short, the algorithm, which we refer to as Gauss-Newton method, can be divided into two stages. In the first stage, the algorithm devotes to find a good initial estimation. The second stage of the algorithm is to update the iteration point by Gauss-Newton iteration. Here the initialization method can provide a good initial guess by using optimal number of measurements. For real-valued signals, we proved that a re-sampled version of the algorithm quadratically converges to the global optimal solution with the number of random measurements being nearly minimal.

报告人简介：高冰，香港科技大学博士后。于2017年获得中科院数学与系统科学研究院博士学位，导师许志强研究员。主要从事压缩采样与信号处理方面的研究工作。部分工作发表在了IEEE Trans. On Signal Processing, Journal of Fourier Analysis and Applications, Advance in Applied Mathematics中。

报告时间：2019年3月16日下午4：00-5:00

报告地点：科技楼南楼702

报告人：刘苏卉(武汉工程大学)

报告题目：基于统计学习的含噪声的动态网络结构重构的研究（Statistical Learning-based Research on Reconstructing Time-varying Network Structures with Noise ）

报告摘要:从可测量数据重建复杂网络是理解和控制复杂动态网络系统的基本问题。 然而，由于物理和硬件限制的限制，人们需要仅用少量动态观测来识别网络结构。复杂动力网络结构重建一直是网络科学研究的热点问题之一，近年来已经取得了丰硕的成果。 但在现实世界中，很多网络节点之间高度相关。而现有方法无法解决存在高度相关的节点的网络的结构重建。考虑到耦合是稀疏的，我们可以利用稀疏统计学习方法。问题仅仅是刚开始提出，还需要进行深入的探讨。 噪声亦无处不在，噪声影响网络结构识别。因此有必要讨论新的基于统计学习的噪声扰动重构连续加权网络拓扑的方法。（Reconstructing complex network structures from measurable data is a fundamental problem in understanding and controlling the dynamics of complex network systems. However, because of the limitation by physical and hardware constraints, people need to identify network structures with only a small number of dynamic observations. The reconstruction of complex network structure has always been one of the hot issues in network science research. In recent years, it has achieved fruitful results. But in the real world, many network nodes are highly correlated. Existing methods cannot solve the structural reconstruction of networks with highly correlated nodes. Considering that the couplings are sparse, we can use statistical learning method. The problem has just been raised, but it needs to be discussed in depth. Noise is also ubiquitous, and noise affects network structure identification. Therefore, it is necessary to discuss a new statistical learning-based noise disturbance reconstruction method for continuous weighted network topologies.）

报告人简介：刘苏卉，武汉工程大学副教授。于2017年获得美国爱荷华大学博士学位，导师蔡剑锋教授,随后入职武汉工程大学。主要从事压缩采样与信号处理、复杂网络方面的研究工作。部分工作发表在了International Journal of Circuits, Systems and Signal processing, IEEE International Conference On Acoustics, Speech and Signal processing, Wavelets and Sparsity中。

报告时间：2019年3月16日下午5：00-6:00

报告地点：科技楼南楼702