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<html><head><title>Netlab Reference Manual linemin</title></head><body><H1> linemin</H1><h2>Purpose</h2>One dimensional minimization.<p><h2>Description</h2><CODE>[x, options] = linemin(f, pt, dir, fpt, options)</CODE> uses Brent'salgorithm to find the minimum of the function <CODE>f(x)</CODE> along theline <CODE>dir</CODE> through the point <CODE>pt</CODE>. The function value at thestarting point is <CODE>fpt</CODE>. The point at which <CODE>f</CODE> has a local minimumis returned as <CODE>x</CODE>. The function value at that point is returnedin <CODE>options(8)</CODE>.<p><CODE>linemin(f, pt, dir, fpt, options, p1, p2, ...)</CODE> allows additional arguments to be passed to <CODE>f()</CODE>.<p>The optional parameters have the following interpretations.<p><CODE>options(1)</CODE> is set to 1 to display error values.<p><CODE>options(2)</CODE> is a measure of the absolute precision required for the valueof <CODE>x</CODE> at the solution.<p><CODE>options(3)</CODE> is a measure of the precision required of the objectivefunction at the solution. Both this and the previous condition must besatisfied for termination.<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100.<p><h2>Examples</h2>An example of the use of this function to find the minimum of a function<CODE>f</CODE> in the direction <CODE>sd</CODE> can be found in <CODE>conjgrad</CODE><PRE>x = linemin(f, xold, sd, fold, lineoptions);</PRE><p><h2>Algorithm</h2>Brent's algorithm uses a mixture of quadratic interpolation and goldensection search to find the minimum of a function of a single variable onceit has been bracketed (which is done with <CODE>minbrack</CODE>). This is adaptedto minimize a function along a line.This implementationis based on that in Numerical Recipes.<p><h2>See Also</h2><CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="minbrack.htm">minbrack</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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