From 46439007cf417cbd9ac8049bb4122c890097a0fa Mon Sep 17 00:00:00 2001
From: "Charles.Forsyth" <devnull@localhost>
Date: Fri, 22 Dec 2006 20:52:35 +0000
Subject: 20060303-partial

---
 module/linalg.m | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)
 create mode 100644 module/linalg.m

(limited to 'module/linalg.m')

diff --git a/module/linalg.m b/module/linalg.m
new file mode 100644
index 00000000..ccc3288d
--- /dev/null
+++ b/module/linalg.m
@@ -0,0 +1,24 @@
+# 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);
+};
-- 
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