Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!uflorida!gatech!myke
From: myke@gatech.edu (Mike Rynolds)
Newsgroups: comp.ai.neural-nets
Subject: guassian elimination on sparce matricies (used as an associative mem)
Summary: AX = B where A and B are sparce matricies
Keywords: matrix associative memory
Message-ID: <18331@gatech.edu>
Date: 4 Apr 89 16:38:29 GMT
Reply-To: myke@gatech.UUCP (Mike Rynolds)
Distribution: na
Organization: School of Information and Computer Science, Georgia Tech, Atlanta
Lines: 14

If A represents a series of input state vectors and B is a corresponding list
of output state vectors, then in the equation AX = B, X is a neural net which
can be trained simply by setting it equal to A-1 * B. Since A and B consists of
1's and 0's, and mostly 0's, large matricies can be made managable if they are
sparce. 
	I have only been able to find gaussian elimination alg.'s on sparce
systems of linear equations of the form Ax = b, where x and b are vectors.
Can anyone direct me to where I can find a gaussian elimination alg on sparce
systems of the form AX = B?
-- 
Mike Rynolds
School of Information & Computer Science, Georgia Tech, Atlanta GA 30332
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