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Table 9. Complexity of sparse array intrinsics in Fortran 90 (for higher-dimensional and parallel cases)

in Parallel Sparse Supports for Array Intrinsic Functions of Fortran 90
by Rong-guey Chang, Tyng-Ruey Chuang 2001
"... In PAGE 33: ... The total cost is the summation of that of the two intermediate reshape operations. Table 7 gives the summary result for the 2-d case, and Table9 gives the summary result for the higher-dimensional case. Notes 1.... ..."
Cited by 3

TABLE 2 Higher-dimensional orthogonal range reporting S(n) Q(n) Source Notes

in Range Searching
by Pankaj K. Agarwal 1997
Cited by 63

Table 8. Complexity of sparse array intrinsics in Fortran 90 (for higher-dimensional and sequential cases)

in Parallel Sparse Supports for Array Intrinsic Functions of Fortran 90
by Rong-guey Chang, Tyng-Ruey Chuang 2001
Cited by 3

TABLE 2 Higher-dimensional orthogonal range reporting S(n) Q(n) Source Notes

in Range Searching
by Pankaj K. Agarwal

Table 4. Assorted timings for higher dimensional simplicial TFTs.

in unknown title
by unknown authors

Table or higher-dimensional array AOV table with x giving specified terms (Default gives all terms, e.g., AOV t is apos;A B AB apos; AOV t)

in Geometric and harmonic means
by Keith Smillie, Frequencies I Range

Table or higher-dimensional array AOV table with x giving specified terms (Default gives all terms, e.g., AOV t is apos;A B AB apos; AOV t )

in J Companion for Statistical Calculations
by Keith Smillie

Table or higher-dimensional array AOV table with x giving specified terms (Default gives all terms, e.g., AOV t is apos;A B AB apos; AOV t)

in Geometric and harmonic means
by Keith Smillie, Frequencies I Range

Table 2 presents the results for the case of a Z which is higher dimensional. The equation that generates the data is given by

in The Partially Linear Regression Model: Monte Carlo Evidence From The Projection Pursuit Regression Approach
by Dingding Li, Thanasis Stengos

Table 2 Another series of comparative results using higher dimensional data sets

in
by K. Blekas, I. E. Lagaris 2006
"... In PAGE 8: ... Observing the number of the EM steps, we can conclude that the preprocessing not only determines the number of clusters properly, but in addition offers nearly optimal cluster solutions. For example, in the case of data set (e) of Table2 , EM took only two steps... ..."
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