Yinyu Ye
Stanford University
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Publication
Featured researches published by Yinyu Ye.
IEEE Signal Processing Magazine | 2010
Zhi-Quan Luo; Wing-Kin Ma; Anthony Man-Cho So; Yinyu Ye; Shuzhong Zhang
In this article, we have provided general, comprehensive coverage of the SDR technique, from its practical deployments and scope of applicability to key theoretical results. We have also showcased several representative applications, namely MIMO detection, B¿ shimming in MRI, and sensor network localization. Another important application, namely downlink transmit beamforming, is described in [1]. Due to space limitations, we are unable to cover many other beautiful applications of the SDR technique, although we have done our best to illustrate the key intuitive ideas that resulted in those applications. We hope that this introductory article will serve as a good starting point for readers who would like to apply the SDR technique to their applications, and to locate specific references either in applications or theory.
Journal of the Operational Research Society | 1997
Yinyu Ye
Geometry of Convex Inequalities. Computation of Analytic Center. Linear Programming Algorithms. Worst-Case Analysis. Average-Case Analysis. Asymptotic Analysis. Convex Optimization. Nonconvex Optimization. Implementation Issues. Bibliography. Index.
information processing in sensor networks | 2004
Pratik Biswas; Yinyu Ye
We describe an SDP relaxation based method for the position estimation problem in wireless sensor networks. The optimization problem is set up so as to minimize the error in sensor positions to fit distance measures. Observable gauges are developed to check the quality of the point estimation of sensors or to detect erroneous sensors. The performance of this technique is highly satisfactory compared to other techniques. Very few anchor nodes are required to accurately estimate the position of all the unknown nodes in a network. Also the estimation errors are minimal even when the anchor nodes are not suitably placed within the network or the distance measurements are noisy.
ACM Transactions on Sensor Networks | 2006
Pratik Biswas; Tzu Chen Lian; Ta Chung Wang; Yinyu Ye
An SDP relaxation based method is developed to solve the localization problem in sensor networks using incomplete and inaccurate distance information. The problem is set up to find a set of sensor positions such that given distance constraints are satisfied. The nonconvex constraints in the formulation are then relaxed in order to yield a semidefinite program that can be solved efficiently.The basic model is extended in order to account for noisy distance information. In particular, a maximum likelihood based formulation and an interval based formulation are discussed. The SDP solution can then also be used as a starting point for steepest descent based local optimization techniques that can further refine the SDP solution.We also describe the extension of the basic method to develop an iterative distributed SDP method for solving very large scale semidefinite programs that arise out of localization problems for large dense networks and are intractable using centralized methods.The performance evaluation of the technique with regard to estimation accuracy and computation time is also presented by the means of extensive simulations.Our SDP scheme also seems to be applicable to solving other Euclidean geometry problems where points are locally connected.
Mathematics of Operations Research | 1993
Shinji Mizuno; Michael J. Todd; Yinyu Ye
We describe several adaptive-step primal-dual interior point algorithms for linear programming. All have polynomial time complexity while some allow very long steps in favorable circumstances. We provide heuristic reasoning for expecting that the algorithms will perform much better in practice than guaranteed by the worst-case estimates, based on an analysis using a nonrigorous probabilistic assumption.
symposium on discrete algorithms | 2005
Anthony Man-Cho So; Yinyu Ye
AbstractWe analyze the semidefinite programming (SDP) based model and method for the position estimation problem in sensor network localization and other Euclidean distance geometry applications. We use SDP duality and interior-point algorithm theories to prove that the SDP localizes any network or graph that has unique sensor positions to fit given distance measures. Therefore, we show, for the first time, that these networks can be localized in polynomial time. We also give a simple and efficient criterion for checking whether a given instance of the localization problem has a unique realization in
Siam Journal on Optimization | 1999
Steven J. Benson; Yinyu Ye; Xiong Zhang
Mathematical Programming | 2016
Caihua Chen; Bingsheng He; Yinyu Ye; Xiaoming Yuan
\mathcal{R}^2
Lecture Notes in Computer Science | 2002
Mohammad Mahdian; Yinyu Ye; Jiawei Zhang
Mathematical Programming | 1991
Yinyu Ye
using graph rigidity theory. Finally, we introduce a notion called strong localizability and show that the SDP model will identify all strongly localizable sub-networks in the input network.