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Description: |
Simplex, interior-point, and memoryless quasi-Newton (QN) optimization
algorithms are each viewed from two contrasting perspectives: the first
facilitates computer implementation but runs counter to intuition, the
second is both insightful and efficiency-revealing. For the memoryless
QN case, the discussion is illustrated by numerical experiments.
Implications for limited-memory QN algorithms are briefly considered.
Area(s):
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Date: |
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| Start Time: |
12:00 |
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Speaker: |
J.L. Nazareth
(Professor Emeritus, Washington State University, Affiliate Professor, University of Washington)
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Place: |
Room 5.5
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URL: |
http://www.math.wsu.edu/faculty/nazareth/
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| Research Groups: |
-Numerical Analysis and Optimization
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See more:
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<Main>
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