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New upper bounds for maximum-entropy sampling

Author: Alan J Hoffman; Jon Lee; Joy Williams
Publisher: Yorktown Heights, N.Y. : IBM T.J. Watson Research Center, [2000]
Series: International Business Machines Corporation.; Research Division.; Research report
Edition/Format:   Book : EnglishView all editions and formats
Database:WorldCat
Summary:
Abstract: "We develop and experiment with new upper bounds for the constrained maximum-entropy sampling problem. Our partition bounds are based on Fischer's inequality. Further new upper bounds combine the use of Fischer's inequality with previously developed bounds. We demonstrate this in detail by using the partitioning idea to strengthen the spectral bounds of Ko, Lee and Queyranne and of Lee. Computational  Read more...
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Document Type: Book
All Authors / Contributors: Alan J Hoffman; Jon Lee; Joy Williams
OCLC Number: 44673120
Notes: Cover title.
"1 March 2000."
Description: 12 p. : ill. ; 28 cm.
Series Title: International Business Machines Corporation.; Research Division.; Research report
Responsibility: Alan Hoffman, Jon Lee, Joy Williams.

Abstract:

Abstract: "We develop and experiment with new upper bounds for the constrained maximum-entropy sampling problem. Our partition bounds are based on Fischer's inequality. Further new upper bounds combine the use of Fischer's inequality with previously developed bounds. We demonstrate this in detail by using the partitioning idea to strengthen the spectral bounds of Ko, Lee and Queyranne and of Lee. Computational evidence suggests that these bounds may be useful in solving problems to optimality in a branch-and-bound framework."

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