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Time: 4:10-5p.m.
Tuesday
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Date |
Speaker |
Title (Click on the title of a talk for the abstract if available). |
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Jan 22, Tuesday |
(Reserved by Leslie) |
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Jan 24, Thursday |
Yaxiang Yuan, Chinese Academy of Sciences |
Subspace Methods for Large Scale Nonlinear Equations and Nonlinear Least Squares |
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Jan. 29, Tuesday |
Alicia Labra, University of Chile |
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Feb. 5, Tuesday |
(Reserved for interview talk) |
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Feb. 12, Tuesday |
Howard Levine, Iowa State University |
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Feb. 19, Tuesday |
Maria Schonbek, UCSC |
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Feb. 26, Tuesday |
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Mar. 7, Friday |
Hemanshu Kaul, Illinois Institute of Technology |
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Mar. 11, Tuesday |
Peter Olver, University of Minnesota, Minneapolis |
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Mar. 25, Tuesday |
(Reserved by R. Ng) |
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April. 1, Tuesday |
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April. 8, Tuesday |
Roger Lui |
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April. 15, Tuesday |
Ezabel Darcy, University of Iowa |
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Apr. 22, Tuesday |
George Andrews, Penn. State University |
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Abstracts:
January 24, Thursday, Prof. Yaxiang Yuan, Institute of Computational Mathematics, Chinese Academy of Sciences
Title:
Subspace Methods for Large Scale Nonlinear Equations and Nonlinear Least
Squares
Abstract: In this talk, we consider large scale nonlinear systems of equations and nonlinear least square problems. We present subspace methods for solving these two special optimization problems. The subspace methods have the characteristic to force the next iteration in a low dimensional subspace. The main technique is to construct subproblems in low dimensions so that the computation cost in each iteration can be reduced comparing to standard approaches. The subspace approach offers a possible way to handle large scale optimization problems which are now attracting more and more attentions. Actually, quite a few known techniques can be viewed as subspace methods, such as the conjugate gradient method, the limited memory quasi-Newton method, the projected gradient method, and the null space method.