MS-Tu-E-41
Numerical Linear Algebra Techniques in Massive Data Analysis

Date: August 11
Time: 16:00--18:10
Room: 303B

(Note: Click title to show the abstract.)

Organizer:
Gu, Ming (Univ. of California Berkeley)

Abstract: Effective and efficient treatment of massive data sets has become increasing
important in this age of information explosion. Most machine learning and data analysis algorithms for massive data sets require huge amounts of computational time.
In this minisymposium, we discuss effective algorithms for analyzing massive data sets
by exploiting efficient numerical linear algebra techniques.

This minisymposium is sponsored by the SIAG.


MS-Tu-E-41-1
16:00--16:30
Efficient Algorithms for Solving Kadison-Singer Problems
Gu, Ming (Univ. of California Berkeley)


MS-Tu-E-41-2
16:30--17:00
A Dynamic Approach to Sparse Recovery
Yao, Yuan (Peking Univ.)


MS-Tu-E-41-3
17:00--17:30
Fast randomized iteration for matrix inversion, eigenproblems, and exponentiation
Lim, Lek-Heng (Univ. of Chicago)
Weare, Jonathan (Univ. of Chicago)


CP-Tu-E-41-4
17:30--17:50
The low-rank basis problem for a matrix subspace
Uschmajew, Andre (Univ. of Bonn)
Nakatsukasa, Yuji (Univ. of Tokyo)
Soma, Tasuku (Univ. of Tokyo)


CP-Tu-E-41-5
17:50--18:10
Simultaneous reduction of large sparse matrix pencils
Sidje, Roger (Univ. of Alabama)

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Footnote:
Code: Type-Date-Time-Room No.
Type : IL=Invited Lecture, SL=Special Lectures, MS=Minisymposia, IM=Industrial Minisymposia, CP=Contributed Papers, PP=Posters
Date: Mo=Monday, Tu=Tuesday, We=Wednesday, Th=Thursday, Fr=Friday
Time : A=8:30-9:30, B=10:00-11:00, C=11:10-12:10, BC=10:00-12:10, D=13:30-15:30, E=16:00-18:00, F=19:00-20:00, G=12:10-13:30, H=15:30-16:00
Room No.: TBA