Zhong-Zhi Bai

Bio: Zhong-Zhi Bai is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Iterative method & System of linear equations. The author has an hindex of 49, co-authored 160 publications receiving 9600 citations. Previous affiliations of Zhong-Zhi Bai include Fudan University & Southern Federal University.

A class of asynchronous parallel multisplitting blockwise relaxation methods

Abstract: By the principle of using sufficiently the delayed information and based on the technique of successively accelerated overrelaxation (AOR), we set up a class of asynchronous multisplitting blockwise relaxation methods for solving the large sparse blocked system of linear equations, which comes from the discretizations of many differential equations These new methods are efficient blockwise variants of the asynchronous parallel matrix multisplitting relaxed iterations discussed by Bai et al (Parallel Computing 21 (1995) 565–582), and they are very smart for implementations on the MIMD multiprocessor systems Under reasonable restrictions on the relaxation parameters as well as the multiple splittings, we establish the convergence theories of this class of new methods when the coefficient matrices of the blocked systems of linear equations are block H -matrices of different types A lot of numerical experiments show that our new methods are applicable and efficient, and have better numerical behaviours than their pointwise alternatives investigated by Bai et al

Alternating splitting waveform relaxation method and its successive overrelaxation acceleration

Abstract: For the large sparse implicit linear initial value problem, we present a block successive overrelaxation scheme for the alternating direction implicit waveform relaxation method to further accelerate its convergence speed, and discuss the convergence property of the resulting iteration method in detail. Numerical implementations about several non-Hermitian implicit linear initial value problems show that the alternating direction implicit waveform relaxation method is very effective, and the block successive overrelaxation technique really accelerates its convergence speed.

Numerical solution of saddle point problems

Abstract: Large linear systems of saddle point type arise in a wide variety of applications throughout computational science and engineering. Due to their indefiniteness and often poor spectral properties, such linear systems represent a significant challenge for solver developers. In recent years there has been a surge of interest in saddle point problems, and numerous solution techniques have been proposed for this type of system. The aim of this paper is to present and discuss a large selection of solution methods for linear systems in saddle point form, with an emphasis on iterative methods for large and sparse problems.

Attack Detection and Identification in Cyber-Physical Systems

Abstract: Cyber-physical systems are ubiquitous in power systems, transportation networks, industrial control processes, and critical infrastructures. These systems need to operate reliably in the face of unforeseen failures and external malicious attacks. In this paper: (i) we propose a mathematical framework for cyber-physical systems, attacks, and monitors; (ii) we characterize fundamental monitoring limitations from system-theoretic and graph-theoretic perspectives; and (ii) we design centralized and distributed attack detection and identification monitors. Finally, we validate our findings through compelling examples.

Attack Detection and Identification in Cyber-Physical Systems -- Part I: Models and Fundamental Limitations

Abstract: Cyber-physical systems integrate computation, communication, and physical capabilities to interact with the physical world and humans. Besides failures of components, cyber-physical systems are prone to malignant attacks, and specific analysis tools as well as monitoring mechanisms need to be developed to enforce system security and reliability. This paper proposes a unified framework to analyze the resilience of cyber-physical systems against attacks cast by an omniscient adversary. We model cyber-physical systems as linear descriptor systems, and attacks as exogenous unknown inputs. Despite its simplicity, our model captures various real-world cyber-physical systems, and it includes and generalizes many prototypical attacks, including stealth, (dynamic) false-data injection and replay attacks. First, we characterize fundamental limitations of static, dynamic, and active monitors for attack detection and identification. Second, we provide constructive algebraic conditions to cast undetectable and unidentifiable attacks. Third, by using the system interconnection structure, we describe graph-theoretic conditions for the existence of undetectable and unidentifiable attacks. Finally, we validate our findings through some illustrative examples with different cyber-physical systems, such as a municipal water supply network and two electrical power grids.