Paul Tseng | |
---|---|
Born |
Hsinchu, Taiwan |
September 21, 1959
Died | Possible drowning |
Residence | United States |
Citizenship | Canada United States |
Fields | Optimization, Mathematics, Network |
Institutions |
University of British Columbia Massachusetts Institute of Technology University of Washington |
Alma mater |
Queen's University Massachusetts Institute of Technology |
Known for |
Large-scale optimization Linear programming Distributed computing Network algorithms |
Large-scale optimization
Paul Tseng was a Taiwanese-born American and Canadian applied mathematician and a professor at the Department of Mathematics at the University of Washington, in Seattle, Washington. Tseng was recognized by his peers to be one of the leading optimization researchers of his generation. Paul Tseng went missing while kayaking in the Yangtze River in the Yunnan province of China and is presumed dead.
Paul Tseng was born September 21, 1959 in Hsinchu, Taiwan. In December 1970, Tseng's family moved to Vancouver, Canada. Tseng received his B.Sc. from Queen's University in 1981 and his Ph.D. from Massachusetts Institute of Technology in 1986. In 1990 Tseng moved to the University of Washington's Department of Mathematics. Tseng has conducted research primarily in continuous optimization and secondarily in discrete optimization and distributed computation.
Tseng made many contributions to mathematical optimization, publishing many articles and helping to develop quality software that has been widely used. He published over 120 papers in optimization and had close collaborations with several colleagues, including Dimitri Bertsekas and Tom Luo.
Tseng's research subjects include:
In his research, Tseng gave a new proof for the sharpest complexity result for path-following interior-point methods for linear programming. Furthermore, together with Tom Luo, he resolved a long-standing open question on the convergence of matrix splitting algorithms for linear complementarity problems and affine variational inequalities. Tseng was the first to establish the convergence of the affine scaling algorithm for linear programming in the presence of degeneracy.