ref: c8b187f9db503465460d8bafa2d7a758bbcf59ca
dir: /module/linalg.m/
# 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); };