# The convention used here for storing matrices is the same commonly# used for scientific programming in C, namely linearizing in Fortran order.# Let A be an m by n matrix. We represent this by# a: array of real;# m, n, lda: int;# where the variable lda ("leading dimension of a") is used so that a# succession of matrix problems of varying sizes can be created without# wholesale copying of data. The element of A in the i-th row and j-th column# is stored in a[i+lda*j], where 0<=i<m and 0<=j<n. This 0-origin indexing# is used everywhere, and in particular in permutation vectors.LinAlg: module{ PATH: con "/dis/math/linalg.dis"; Vector: type array of real; Matrix: adt{ m, L, n: int; # rows, column stride, columns a: Vector; # data, stored A[i,j] = a[i+L*j] }; dgefa: fn(a:array of real, lda, n:int, ipvt:array of int): int; dgesl: fn(a:array of real, lda, n:int, ipvt:array of int, b:array of real, job:int); printmat: fn(label:string, a:array of real, lda, m, n:int);};
# The convention used here for storing matrices is the same commonly
# used for scientific programming in C, namely linearizing in Fortran order.
# Let A be an m by n matrix. We represent this by
# a: array of real;
# m, n, lda: int;
# where the variable lda ("leading dimension of a") is used so that a
# succession of matrix problems of varying sizes can be created without
# wholesale copying of data. The element of A in the i-th row and j-th column
# is stored in a[i+lda*j], where 0<=i<m and 0<=j<n. This 0-origin indexing
# is used everywhere, and in particular in permutation vectors.
LinAlg: module{
PATH: con "/dis/math/linalg.dis";
Vector: type array of real;
Matrix: adt{
m, L, n: int; # rows, column stride, columns
a: Vector; # data, stored A[i,j] = a[i+L*j]
};
dgefa: fn(a:array of real, lda, n:int, ipvt:array of int): int;
dgesl: fn(a:array of real, lda, n:int, ipvt:array of int, b:array of real, job:int);
printmat: fn(label:string, a:array of real, lda, m, n:int);
};