2009 - INFORMS John von Neumann Theory Prize
Yinyu Ye mainly investigates Mathematical optimization, Algorithm, Interior point method, Linear programming and Semidefinite programming. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Wireless sensor network and Second-order cone programming, Convex optimization. He mostly deals with Optimization problem in his studies of Algorithm.
His studies in Linear programming integrate themes in fields like Function and Generalization. He has researched Semidefinite programming in several fields, including Semidefinite embedding, Gradient descent, Graph theory, Relaxation and Euclidean distance. His work deals with themes such as Statistics, Method of steepest descent, Conic section and Calculus, which intersect with Nonlinear programming.
His primary areas of investigation include Mathematical optimization, Algorithm, Linear programming, Interior point method and Combinatorics. His Mathematical optimization study often links to related topics such as Convex optimization. Much of his study explores Linear programming relationship to Function.
His Interior point method study integrates concerns from other disciplines, such as Point, Numerical analysis and Applied mathematics. His research in Combinatorics focuses on subjects like Discrete mathematics, which are connected to Polynomial. His Semidefinite programming research includes themes of Wireless sensor network, Graph, Semidefinite embedding and Relaxation.
His primary areas of study are Mathematical optimization, Interior point method, Linear programming, Discrete mathematics and Markov decision process. As part of his studies on Mathematical optimization, Yinyu Ye often connects relevant subjects like Estimator. His Interior point method study incorporates themes from Point and Feasible region.
Yinyu Ye combines subjects such as Penalty method, Nonlinear programming, Binary logarithm, Distribution and Online algorithm with his study of Linear programming. His work carried out in the field of Markov decision process brings together such families of science as Range, Sample and Reinforcement learning. The various areas that Yinyu Ye examines in his Optimization problem study include Computational complexity theory, Feature selection and Rendering.
His scientific interests lie mostly in Mathematical optimization, Convergence, Coordinate descent, Upper and lower bounds and Markov decision process. His Mathematical optimization study frequently links to other fields, such as Nonlinear programming. His Convergence research focuses on subjects like Random permutation, which are linked to Rate of convergence, Convex optimization, Quadratic programming and Iterated function.
His biological study spans a wide range of topics, including Crowdsourcing, Logarithm, Regret and Combinatorics. His study focuses on the intersection of Feasible region and fields such as Penalty method with connections in the field of Linear programming. His work is dedicated to discovering how Interior point method, Matrix are connected with Algorithm and other disciplines.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Linear and nonlinear programming
David G. Luenberger;Yinyu Ye.
Disciplined Convex Programming
Michael Grant;Stephen Boyd;Yinyu Ye.
Semidefinite Relaxation of Quadratic Optimization Problems
Zhi-quan Luo;Wing-kin Ma;Anthony Man-Cho So;Yinyu Ye.
IEEE Signal Processing Magazine (2010)
Interior point algorithms: theory and analysis
Journal of the Operational Research Society (1997)
Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems
Erick Delage;Yinyu Ye.
Operations Research (2010)
Approximation algorithms for facility location problems
Yinyu Ye;Jiawei Zhang.
Semidefinite programming for ad hoc wireless sensor network localization
Pratik Biswas;Yinyu Ye.
information processing in sensor networks (2004)
Semidefinite programming based algorithms for sensor network localization
Pratik Biswas;Tzu-Chen Lian;Ta-Chung Wang;Yinyu Ye.
ACM Transactions on Sensor Networks (2006)
On Adaptive-Step Primal-Dual Interior-Point Algorithms for Linear Programming
Shinji Mizuno;Michael J. Todd;Yinyu Ye.
Mathematics of Operations Research (1993)
Semidefinite Programming Approaches for Sensor Network Localization With Noisy Distance Measurements
P. Biswas;Tzu-Chen Liang;Kim-Chuan Toh;Y. Ye.
IEEE Transactions on Automation Science and Engineering (2006)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: