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L’Hôpital rule

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11: 18.41 Tables
Abramowitz and Stegun (1964, Tables 22.4, 22.6, 22.11, and 22.13) tabulates T n ( x ) , U n ( x ) , L n ( x ) , and H n ( x ) for n = 0 ( 1 ) 12 . The ranges of x are 0.2 ( .2 ) 1 for T n ( x ) and U n ( x ) , and 0.5 , 1 , 3 , 5 , 10 for L n ( x ) and H n ( x ) . … For P n ( x ) , L n ( x ) , and H n ( x ) see §3.5(v). …
12: Bibliography
  • G. Allasia and R. Besenghi (1987a) Numerical computation of Tricomi’s psi function by the trapezoidal rule. Computing 39 (3), pp. 271–279.
  • G. Allasia and R. Besenghi (1991) Numerical evaluation of the Kummer function with complex argument by the trapezoidal rule. Rend. Sem. Mat. Univ. Politec. Torino 49 (3), pp. 315–327.
  • G. Allasia and R. Besenghi (1987b) Numerical calculation of incomplete gamma functions by the trapezoidal rule. Numer. Math. 50 (4), pp. 419–428.
  • A. Apelblat (1989) Derivatives and integrals with respect to the order of the Struve functions 𝐇 ν ( x ) and 𝐋 ν ( x ) . J. Math. Anal. Appl. 137 (1), pp. 17–36.
  • T. M. Apostol (1985b) Note on the trivial zeros of Dirichlet L -functions. Proc. Amer. Math. Soc. 94 (1), pp. 29–30.
  • 13: 19.33 Triaxial Ellipsoids
    The external field and the induced magnetization together produce a uniform field inside the ellipsoid with strength H / ( 1 + L c χ ) , where L c is the demagnetizing factor, given in cgs units by
    19.33.7 L c = 2 π a b c 0 d λ ( a 2 + λ ) ( b 2 + λ ) ( c 2 + λ ) 3 = V R D ( a 2 , b 2 , c 2 ) .
    19.33.8 L a + L b + L c = 4 π ,
    where L a and L b are obtained from L c by permutation of a , b , and c . …
    14: 1.2 Elementary Algebra
    This is the row times column rule. …
    1.2.45 𝐯 p = ( i = 1 n | v i | p ) 1 / p , p 1 .
    1.2.48 𝐯 = max ( | v 1 | , | v 2 | , , | v n | ) .
    1.2.50 | 𝐮 , 𝐯 | 𝐮 p 𝐯 q ,
    15: 23.9 Laurent and Other Power Series
    23.9.1 c n = ( 2 n 1 ) w 𝕃 { 0 } w 2 n , n = 2 , 3 , 4 , .
    23.9.2 ( z ) = 1 z 2 + n = 2 c n z 2 n 2 , 0 < | z | < | z 0 | ,
    23.9.3 ζ ( z ) = 1 z n = 2 c n 2 n 1 z 2 n 1 , 0 < | z | < | z 0 | .
    23.9.6 ( ω j + t ) = e j + ( 3 e j 2 5 c 2 ) t 2 + ( 10 c 2 e j + 21 c 3 ) t 4 + ( 7 c 2 e j 2 + 21 c 3 e j + 5 c 2 2 ) t 6 + O ( t 8 ) ,
    23.9.7 σ ( z ) = m , n = 0 a m , n ( 10 c 2 ) m ( 56 c 3 ) n z 4 m + 6 n + 1 ( 4 m + 6 n + 1 ) ! ,
    16: 11.15 Approximations
  • Luke (1975, pp. 416–421) gives Chebyshev-series expansions for 𝐇 n ( x ) , 𝐋 n ( x ) , 0 | x | 8 , and 𝐇 n ( x ) Y n ( x ) , x 8 , for n = 0 , 1 ; 0 x t m 𝐇 0 ( t ) d t , 0 x t m 𝐋 0 ( t ) d t , 0 | x | 8 , m = 0 , 1 and 0 x ( 𝐇 0 ( t ) Y 0 ( t ) ) d t , x t 1 ( 𝐇 0 ( t ) Y 0 ( t ) ) d t , x 8 ; the coefficients are to 20D.

  • MacLeod (1993) gives Chebyshev-series expansions for 𝐋 0 ( x ) , 𝐋 1 ( x ) , 0 x 16 , and I 0 ( x ) 𝐋 0 ( x ) , I 1 ( x ) 𝐋 1 ( x ) , x 16 ; the coefficients are to 20D.

  • 17: 23.7 Quarter Periods
    23.7.1 ( 1 2 ω 1 ) = e 1 + ( e 1 e 3 ) ( e 1 e 2 ) = e 1 + ω 1 2 ( K ( k ) ) 2 k ,
    23.7.2 ( 1 2 ω 2 ) = e 2 i ( e 1 e 2 ) ( e 2 e 3 ) = e 2 i ω 1 2 ( K ( k ) ) 2 k k ,
    23.7.3 ( 1 2 ω 3 ) = e 3 ( e 1 e 3 ) ( e 2 e 3 ) = e 3 ω 1 2 ( K ( k ) ) 2 k ,
    18: 18.8 Differential Equations
    Table 18.8.1: Classical OP’s: differential equations A ( x ) f ′′ ( x ) + B ( x ) f ( x ) + C ( x ) f ( x ) + λ n f ( x ) = 0 .
    # f ( x ) A ( x ) B ( x ) C ( x ) λ n
    8 L n ( α ) ( x ) x α + 1 x 0 n
    9 e 1 2 x 2 x α + 1 2 L n ( α ) ( x 2 ) 1 0 x 2 + ( 1 4 α 2 ) x 2 4 n + 2 α + 2
    10 e 1 2 x x 1 2 α L n ( α ) ( x ) x 1 1 4 x 1 4 α 2 x 1 n + 1 2 ( α + 1 )
    11 e n 1 x x + 1 L n 1 ( 2 + 1 ) ( 2 n 1 x ) 1 0 2 x ( + 1 ) x 2 1 n 2
    19: 23.3 Differential Equations
    23.3.1 g 2 = 60 w 𝕃 { 0 } w 4 ,
    23.3.2 g 3 = 140 w 𝕃 { 0 } w 6 .
    Given g 2 and g 3 there is a unique lattice 𝕃 such that (23.3.1) and (23.3.2) are satisfied. … Conversely, g 2 , g 3 , and the set { e 1 , e 2 , e 3 } are determined uniquely by the lattice 𝕃 independently of the choice of generators. However, given any pair of generators 2 ω 1 , 2 ω 3 of 𝕃 , and with ω 2 defined by (23.2.1), we can identify the e j individually, via …
    20: 3.11 Approximation Techniques
    to the maximum error of the minimax polynomial p n ( x ) is bounded by 1 + L n , where L n is the n th Lebesgue constant for Fourier series; see §1.8(i). Since L 0 = 1 , L n is a monotonically increasing function of n , and (for example) L 1000 = 4.07 , this means that in practice the gain in replacing a truncated Chebyshev-series expansion by the corresponding minimax polynomial approximation is hardly worthwhile. … The Padé approximants can be computed by Wynn’s cross rule. Any five approximants arranged in the Padé table as …