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- Yinyu Ye

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Mathematics
H-index
74
Citations
36,035
275
World Ranking
84
National Ranking
50

Engineering and Technology
H-index
76
Citations
27,137
282
World Ranking
180
National Ranking
84

2009 - INFORMS John von Neumann Theory Prize

- Mathematical optimization
- Statistics
- Algorithm

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.

- Linear and nonlinear programming (5174 citations)
- Semidefinite Relaxation of Quadratic Optimization Problems (1824 citations)
- Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems (857 citations)

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.

- Mathematical optimization (49.21%)
- Algorithm (21.47%)
- Linear programming (20.16%)

- Mathematical optimization (49.21%)
- Interior point method (20.16%)
- Linear programming (20.16%)

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.

- Variance reduced value iteration and faster algorithms for solving markov decision processes (63 citations)
- Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model (58 citations)
- Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model (23 citations)

- Statistics
- Algorithm
- Mathematical optimization

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.

**(1984)**

6467 Citations

Disciplined Convex Programming

Michael Grant;Stephen Boyd;Yinyu Ye.

**(2006)**

2308 Citations

Semidefinite Relaxation of Quadratic Optimization Problems

Zhi-quan Luo;Wing-kin Ma;Anthony Man-Cho So;Yinyu Ye.

IEEE Signal Processing Magazine **(2010)**

2095 Citations

Interior point algorithms: theory and analysis

Yinyu Ye.

Journal of the Operational Research Society **(1997)**

1168 Citations

Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems

Erick Delage;Yinyu Ye.

Operations Research **(2010)**

1006 Citations

Approximation algorithms for facility location problems

Yinyu Ye;Jiawei Zhang.

**(2004)**

955 Citations

Semidefinite programming for ad hoc wireless sensor network localization

Pratik Biswas;Yinyu Ye.

information processing in sensor networks **(2004)**

776 Citations

Semidefinite programming based algorithms for sensor network localization

Pratik Biswas;Tzu-Chen Lian;Ta-Chung Wang;Yinyu Ye.

ACM Transactions on Sensor Networks **(2006)**

602 Citations

On Adaptive-Step Primal-Dual Interior-Point Algorithms for Linear Programming

Shinji Mizuno;Michael J. Todd;Yinyu Ye.

Mathematics of Operations Research **(1993)**

567 Citations

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)**

479 Citations

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.

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