This class contains Java versions of a number of the LINPACK
basic linear algebra subroutines (blas):
isamax_f77
daxpy_f77
ddot_f77
dscal_f77
dswap_f77
dnrm2_f77
dcopy_f77
drotg_f77
It also contains utility routines that the translator found useful
while translating the FORTRAN code to Java code.
This class contains Java versions of a number of the LINPACK
basic linear algebra subroutines (blas):
isamax_j
daxpy_j
ddot_j
dscal_j
dswap_j
dnrm2_j
dcopy_j
drotg_j
It also contains utility routines that the translator found useful
while translating the FORTRAN code to Java code.
Currently, this class contains methods to calculate the
cumulative distribution function (CDF) of a
2-parameter Weibull distribution and the inverse of the CDF.
Currently, this class contains methods to calculate the
cumulative distribution function (CDF) of a
3-parameter Weibull distribution and the inverse of the CDF.
The chlhsn_f77 method finds
"THE L(L-TRANSPOSE) [WRITTEN LL+] DECOMPOSITION OF THE PERTURBED
MODEL HESSIAN MATRIX A+MU*I(WHERE MU\0 AND I IS THE IDENTITY MATRIX)
WHICH IS SAFELY POSITIVE DEFINITE.
The choldc_f77 method finds
"THE PERTURBED L(L-TRANSPOSE) [WRITTEN LL+] DECOMPOSITION
OF A+D, WHERE D IS A NON-NEGATIVE DIAGONAL MATRIX ADDED TO A IF
NECESSARY TO ALLOW THE CHOLESKY DECOMPOSITION TO CONTINUE."
Translated by Steve Verrill, April 15, 1998.
This class contains:
a method that obtains the Cholesky
factorization RR´, where R is a lower triangular matrix,
of a symmetric positive definite matrix A.
This method multiplies a constant times a portion of a column
of a matrix and adds the product to the corresponding portion
of another column of the matrix --- a portion of col2 is
replaced by the corresponding portion of a*col1 + col2.
This method multiplies a constant times a portion of a column
of a matrix and adds the product to the corresponding portion
of another column of the matrix --- a portion of col2 is
replaced by the corresponding portion of a*col1 + col2.
This method multiplies a constant times a portion of a column
of a matrix x[ ][ ] and adds the product to the corresponding portion
of a vector y[ ] --- a portion of y[ ] is replaced by the corresponding
portion of ax[ ][j] + y[ ].
This method multiplies a constant times a portion of a column
of a matrix x[ ][ ] and adds the product to the corresponding portion
of a vector y[ ] --- a portion of y[ ] is replaced by the corresponding
portion of ax[ ][j] + y[ ].
This method multiplies a constant times a portion of a vector y[ ]
and adds the product to the corresponding portion
of a column of a matrix x[ ][ ] --- a portion of column j of x[ ][ ]
is replaced by the corresponding
portion of ay[ ] + x[ ][j].
This method multiplies a constant times a portion of a vector y[ ]
and adds the product to the corresponding portion
of a column of a matrix x[ ][ ] --- a portion of column j of x[ ][ ]
is replaced by the corresponding
portion of ay[ ] + x[ ][j].
This method decomposes an p by p symmetric, positive definite
matrix X into a product, R´R, where R is an upper triangular matrix
and R´ is the transpose of R.
This method decomposes an p by p symmetric, positive definite
matrix X into a product, R´R, where R is an upper triangular matrix
and R´ is the transpose of R.
This method "applies the output of DQRDC to compute coordinate
transformations, projections, and least squares solutions."
For details, see the comments in the code.
This method "applies the output of DQRDC to compute coordinate
transformations, projections, and least squares solutions."
For details, see the comments in the code.
The fdjac2 method computes a forward-difference approximation
to the m by n Jacobian matrix associated with a specified
problem of m functions in n variables.
This method searches for a zero of a function f(x) between
the given values b and c until the width of the interval
(b,c) has collapsed to within a tolerance specified by
the stopping criterion, Math.abs(b-c) <= 2.0*(rw*Math.abs(b)+ae).
This method takes a set of sorted data and returns the associated
normal scores (Weisberg-Bingham versions) and the value of the
Weisberg-Bingham version of the Shapiro-Wilk statistic for this
data.
This method takes a set of sorted data and returns the associated
2-parameter Weibull scores (Filliben version) and the value of the
Filliben version of the correlation statistic for this
data.
This method takes a set of sorted data and returns the associated
3-parameter Weibull scores (Filliben version) and the value of the
Filliben version of the correlation statistic for this
data.
This method obtains the inverse of an n by n
symmetric positive definite matrix A.
On entrance:
If factored == false, the lower triangle of a[ ][ ] should
contain the lower triangle of A.
If factored == true, the lower triangle of a[ ][ ] should
contain a lower triangular matrix R such that RR´ = A.
Given an m by n matrix A, an n by n nonsingular diagonal
matrix D, an m-vector b, and a positive number delta,
the problem is to determine a value for the parameter
par such that if x solves the system
The optstp_f77 method determines whether the algorithm should
terminate due to any of the following:
1) problem solved within user tolerance
2) convergence within user tolerance
3) iteration limit reached
4) divergence or too restrictive maximum step (stepmx)
suspected
Translated by Steve Verrill, May 12, 1998.
This method obtains the solution, y, of the equation
Ly = b where L is a known full rank lower triangular
n by n matrix,
and b is a known vector of length n.
This method obtains the solution, x, of the equation
Ux = y where U is a known full rank upper triangular
n by n matrix,
and y is a known vector of length n.