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Numerical Algorithms in PL/I.

"Is this the Room of Answers?" -- Gulliver's Travels [the film]

Two of the great names in numerical methods - James Wilkinson of the National Physical Laboratory, U.K., and C. Reinsch of the Mathematical Institute of TH, Munchen, Germany - combined to assemble a masterful suite of numerical procedures in Handbook for Automatic Computation, Volume II, Linear Algebra.

The algorithms cover linear equations, least squares, linear programming, and eigenvalue and eigenvector problems. Note: in some of the following procedures, the original comment documentation and description of the parameters - which were missing - have been restored. As well, the original procedure names have been restored. The routines have not been re-tested.
Most of these routines have active links; the remainder will be added as soon as they have been converted.

IBM's Scientific Subroutine Library. Most if not all of these procedures are available. See a list of their titles. For the procedures, contact the undersigned.


These algorithms were originally published in Journal of Royal Statistical Society (Series C): Applied Statistics. Alan Miller converted them to Fortran 90, and I have converted those to PL/I.
  • M. J. R. Healy's algorithm, AS 6: Given a symmetric matrix a of order n as lower triangle, calculates an upper triangle, u, such that uprime * u = a. a must be positive semi-definite.
  • P. R. Freeman's algorithm, AS 7: Forms as a lower triangle, a generalised inverse of the positive semi-definite symmetric matrix a of order n, stored as lower triangle.
    Tests both AS 6 and AS 7.
  • AS 27: Calculate the upper tail area under Student's t-distribution, by G. A. R. Taylor.
  • AS 60: Calculate the eigenvalues and eigenvectors of a real symmetric matrix, by D. N. Spanks & A. D. Dodd.
  • AS 63: Log of the beta function (includes log of the gamma function, by K. L. Majumder & G. P. BhattaCharjee.
  • AS 66: Evaluates the tail area of the standardised normal curve from x to infinity or from minus infinity to x, by I. D. Hill.
  • AS 91: Evaluates the percentage points of the chi-squared probability distribution function, by D. J. Best & D. E. Roberts.
  • AS 110: Lp-NORM Fit of straight line by extension of Schlossmacher, particularly for 1 <= p <= 2, by V. A. Sposito, W. J. Kennedy, and J. E. Gentle.
  • J. Barnard's algorithm, AS 126: Computes the probability of the normal range given t, the upper limit of integration, and n, the sample size.
  • R. D. Armstrong and M. K. Tung's algorithm, AS 132: Fit Y = ALPHA + BETA.X + error
  • AS 135: Min-Max estimates for a linear multiple regression problem, R. D. Armstrong and D. S. Tung.
  • AS 136: Divides M points in N-dimensional space into K clusters so that the within cluster the sum of squares is minimized, by J. A. Hartigan & M. A. Wong.
  • R. E. Lund's algorithm, AS 152: Cumulative hypergeometric probabilities (Replaces AS 59 and AS 152, and incorporates AS R86.)
  • AS 154: Algorithm for exact maximum likelihood estimation of autoregressive-moving average models by means of Kalman filtering, by G. Gardner, A. C. Harvey, & G. D. A. Phillips.
  • AS 155: Distribution of a linear combination of non-central chi-squared random variables, by R. B. Davies.
  • AS 157: The Runs-Up and Runs-Down Test.
  • AS 177: Exact calculation of Normal Scores.
  • AS 181: Calculates the Shapiro-Wilk W test and its significance level.
  • AS 190: Evaluates the probability from 0 to q for a studentized range having v degrees of freedom and r samples.
  • AS 192: Computes approximate significance points of a Pearson curve with given first four moments, or first three moments and left or right boundary.
  • AS 205: Enumerates all R*C contingency tables with given row totals N(I) and column totals M(J) and calculates the hypergeometric probability of each table.
  • AS 207: Fitting a generalized log-linear model to fully or partially classified frequencies.
  • AS 217: Does the dip calculation for an ordered vector X using the greatest convex minorant and the least concave majorant, skipping through the data using the change points of these distributions.
  • AS 227: Generates all possible N-bit binary codes, and applies a users procedure for each code generated.
  • AS 239: Incomplete Gamma Integral.
  • AS 241: Produces the normal deviate Z corresponding to a given lower tail area of P; Z is accurate to about 1 part in 10**7.
  • AS 245: Logarithm of the gamma function.
  • AS 260: Computes the C.D.F. for the distribution of the square of the multiple correlation coefficient with parameters X, IP, N, and RHO2.
    X is the sample value of R**2.
    IP is the number of predictors, including 1 for the constant if one is being fitted.
    N is the number of cases.
    RHO2 is the population value of the squared multiple correlation coefficient (often set = 0).
  • AS 261: Computes the quantile of the distribution of the square of the sample multiple correlation coefficient for given number of random variables M, sample size SIZE, square of the population multiple correlation coefficient RHO2, and lower tail area P.
  • AS 275: Computes the noncentral chi-square distribution function with positive real degrees of freedom f and nonnegative noncentrality parameter theta.
  • AS 282: High breakdown regression and multivariate estimation, by D. M. Hawkins.
    Calculates the least median of squares regression, minimum volume ellipsoid, and associated statistics.
  • AS 285: Finds the probability that a normally distributed random N-vector with mean 0 and covariance COVAR falls in area enclosed by the external user-defined function F, S. L. Lohr.
  • A. J. Miller's AS 290: Generate a rectangular 2-D grid of variance ratios from which to plot confidence regions for two parameters using Halperin's method. If the model is linear in all parameters other than the two selected, the confidence regions are exact; otherwise they are approximate and the user should test the sensitivity of the confidence regions to variation in the other parameters.
  • AS 295: Alan Miller's & Nam Nguyen's Heuristic algorithm to pick N rows of X out of NCAND to maximize the determinant of X'X, using the Fedorov exchange algorithm. (This is a modified version of the published algorithm.)
  • AS 298: Hybrid minimization routine using simulated annealing, by S. P. Brooks.
  • AS 304: Fisher's non-parametric randomization test for two small independent random samples, by L. E. Richards and J. Byrd.
  • AS 310: Computes the cumulative distribution function of a non-central beta random variable, by R. Chattamvelli and R. Shanmugam.
  • AS 319 Function minimization without derivatives.

R. A. Vowels, 25 February 2006. Updated 7 March 2006, 29 March 2006, 8 April 2006, 15 April 2006, 14 October 2006, 21 December 2006, 29 March 2007, 29 May 2007, 5 June 2007, 29 October 2007, 1 December 2007, 25 April 2008, 23 June 2008, 28 December 2008, 15 March 2009, 14 July 2009, 28 October 2009, 1 December 2009, 3 December 2010.