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authorziejd22017-09-28 15:04:40 -0500
committerziejd22017-09-28 15:04:40 -0500
commit8070dc963753142bb86c4ed698d91fd623ed28e7 (patch)
treed0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/scg.htm
parent7cc31810d53176e805532b2789955f4eedbce6bb (diff)
downloadBNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02
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+<html>
+<head>
+<title>
+Netlab Reference Manual scg
+</title>
+</head>
+<body>
+<H1> scg
+</H1>
+<h2>
+Purpose
+</h2>
+Scaled conjugate gradient optimization.
+
+<p><h2>
+Description
+</h2>
+<CODE>[x, options] = scg(f, x, options, gradf)</CODE> uses a scaled conjugate 
+gradients
+algorithm to find a local minimum of the function <CODE>f(x)</CODE> whose
+gradient is given by <CODE>gradf(x)</CODE>.  Here <CODE>x</CODE> is a row vector
+and <CODE>f</CODE> returns a scalar value.
+The point at which <CODE>f</CODE> has a local minimum
+is returned as <CODE>x</CODE>.  The function value at that point is returned
+in <CODE>options(8)</CODE>.
+
+<p><CODE>[x, options, flog, pointlog, scalelog] = scg(f, x, options, gradf)</CODE>
+also returns (optionally) a log of the function values
+after each cycle in <CODE>flog</CODE>, a log
+of the points visited in <CODE>pointlog</CODE>, and a log of the scale values
+in the algorithm in <CODE>scalelog</CODE>.
+
+<p><CODE>scg(f, x, options, gradf, p1, p2, ...)</CODE> allows
+additional arguments to be passed to <CODE>f()</CODE> and <CODE>gradf()</CODE>. 
+  
+The optional parameters have the following interpretations.
+
+<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error 
+values in the return argument <CODE>errlog</CODE>, and the points visited
+in the return argument <CODE>pointslog</CODE>.  If <CODE>options(1)</CODE> is set to 0,
+then only warning messages are displayed.  If <CODE>options(1)</CODE> is -1,
+then nothing is displayed.
+
+<p><CODE>options(2)</CODE> is a measure of the absolute precision required for the value
+of <CODE>x</CODE> at the solution.  If the absolute difference between
+the values of <CODE>x</CODE> between two successive steps is less than
+<CODE>options(2)</CODE>, then this condition is satisfied.
+
+<p><CODE>options(3)</CODE> is a measure of the precision required of the objective
+function at the solution.  If the absolute difference between the
+objective function values between two successive steps is less than
+<CODE>options(3)</CODE>, then this condition is satisfied.
+Both this and the previous condition must be
+satisfied for termination.
+
+<p><CODE>options(9)</CODE> is set to 1 to check the user defined gradient function.
+
+<p><CODE>options(10)</CODE> returns the total number of function evaluations (including
+those in any line searches).
+
+<p><CODE>options(11)</CODE> returns the total number of gradient evaluations.
+
+<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100.
+
+<p><h2>
+Examples
+</h2>
+An example of 
+the use of the additional arguments is the minimization of an error
+function for a neural network:
+<PRE>
+
+w = scg('neterr', w, options, 'netgrad', net, x, t);
+</PRE>
+
+
+<p><h2>
+Algorithm
+</h2>
+The search direction is re-started after every <CODE>nparams</CODE> 
+successful weight updates where <CODE>nparams</CODE> is the total number of 
+parameters in <CODE>x</CODE>. The algorithm is based on that given by Williams
+(1991), with a simplified procedure for updating <CODE>lambda</CODE> when
+<CODE>rho < 0.25</CODE>.
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE><hr>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+
+
+</body>
+</html>
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