Asymmetric uniform designs based on mixture discrepancy
被引:16
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作者:
Elsawah, A. M.
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机构:
Cent China Normal Univ, Fac Math & Stat, Wuhan, Peoples R China
Zagazig Univ, Dept Math, Fac Sci, Zagazig, EgyptCent China Normal Univ, Fac Math & Stat, Wuhan, Peoples R China
Elsawah, A. M.
[1
,2
]
Qin, Hong
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机构:
Cent China Normal Univ, Fac Math & Stat, Wuhan, Peoples R ChinaCent China Normal Univ, Fac Math & Stat, Wuhan, Peoples R China
Qin, Hong
[1
]
机构:
[1] Cent China Normal Univ, Fac Math & Stat, Wuhan, Peoples R China
[2] Zagazig Univ, Dept Math, Fac Sci, Zagazig, Egypt
Efficient experimental design is crucial in the study of scientific problems. The uniform design is one of the most widely used approaches. The discrepancies have played an important role in quasi-Monte Carlo methods and uniform design. Zhou et al. [17] proposed a new type of discrepancy, mixture discrepancy (MD), and showed that MD may be a better uniformity measure than other discrepancies. In this paper, we discuss in depth the MD as the uniformity measure for asymmetric mixed two and three levels U-type designs. New analytical expression based on row distance and new lower bound of the MD are given for asymmetric levels designs. Using the new formulation and the new lower bound as the benchmark, we can implement a new version of the fast local search heuristic threshold accepting. By this search heuristic, we can obtain mixed two and three levels U-type designs with low discrepancy.
机构:
Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R ChinaHong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China
Fang, KT
Ma, CX
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机构:Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China
Ma, CX
Winker, P
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机构:Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China
机构:
Macau Univ Sci & Technol, Macau, Peoples R ChinaMacau Univ Sci & Technol, Macau, Peoples R China
Qiang, Sunyuan
Hou, Jiayi
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机构:
Lafayette Coll, Easton, PA USAMacau Univ Sci & Technol, Macau, Peoples R China
Hou, Jiayi
Wan, Jun
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机构:
Macau Univ Sci & Technol, Macau, Peoples R China
Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R ChinaMacau Univ Sci & Technol, Macau, Peoples R China
Wan, Jun
Liang, Yanyan
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机构:
Macau Univ Sci & Technol, Macau, Peoples R ChinaMacau Univ Sci & Technol, Macau, Peoples R China
Liang, Yanyan
Lei, Zhen
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机构:
Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R ChinaMacau Univ Sci & Technol, Macau, Peoples R China
Lei, Zhen
Zhang, Du
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机构:
Macau Univ Sci & Technol, Macau, Peoples R ChinaMacau Univ Sci & Technol, Macau, Peoples R China
Zhang, Du
THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 8,
2023,
: 9498
-
9506