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1 <html>
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2 <head>
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3 <title>
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4 Netlab Reference Manual scg
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5 </title>
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6 </head>
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7 <body>
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8 <H1> scg
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9 </H1>
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10 <h2>
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11 Purpose
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12 </h2>
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13 Scaled conjugate gradient optimization.
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14
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15 <p><h2>
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16 Description
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17 </h2>
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18 <CODE>[x, options] = scg(f, x, options, gradf)</CODE> uses a scaled conjugate
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19 gradients
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20 algorithm to find a local minimum of the function <CODE>f(x)</CODE> whose
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21 gradient is given by <CODE>gradf(x)</CODE>. Here <CODE>x</CODE> is a row vector
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22 and <CODE>f</CODE> returns a scalar value.
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23 The point at which <CODE>f</CODE> has a local minimum
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24 is returned as <CODE>x</CODE>. The function value at that point is returned
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25 in <CODE>options(8)</CODE>.
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26
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27 <p><CODE>[x, options, flog, pointlog, scalelog] = scg(f, x, options, gradf)</CODE>
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28 also returns (optionally) a log of the function values
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29 after each cycle in <CODE>flog</CODE>, a log
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30 of the points visited in <CODE>pointlog</CODE>, and a log of the scale values
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31 in the algorithm in <CODE>scalelog</CODE>.
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32
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33 <p><CODE>scg(f, x, options, gradf, p1, p2, ...)</CODE> allows
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34 additional arguments to be passed to <CODE>f()</CODE> and <CODE>gradf()</CODE>.
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35
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36 The optional parameters have the following interpretations.
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37
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38 <p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error
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39 values in the return argument <CODE>errlog</CODE>, and the points visited
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40 in the return argument <CODE>pointslog</CODE>. If <CODE>options(1)</CODE> is set to 0,
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41 then only warning messages are displayed. If <CODE>options(1)</CODE> is -1,
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42 then nothing is displayed.
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43
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44 <p><CODE>options(2)</CODE> is a measure of the absolute precision required for the value
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45 of <CODE>x</CODE> at the solution. If the absolute difference between
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46 the values of <CODE>x</CODE> between two successive steps is less than
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47 <CODE>options(2)</CODE>, then this condition is satisfied.
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48
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49 <p><CODE>options(3)</CODE> is a measure of the precision required of the objective
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50 function at the solution. If the absolute difference between the
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51 objective function values between two successive steps is less than
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52 <CODE>options(3)</CODE>, then this condition is satisfied.
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53 Both this and the previous condition must be
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54 satisfied for termination.
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55
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56 <p><CODE>options(9)</CODE> is set to 1 to check the user defined gradient function.
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57
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58 <p><CODE>options(10)</CODE> returns the total number of function evaluations (including
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59 those in any line searches).
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60
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61 <p><CODE>options(11)</CODE> returns the total number of gradient evaluations.
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62
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63 <p><CODE>options(14)</CODE> is the maximum number of iterations; default 100.
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64
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65 <p><h2>
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66 Examples
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67 </h2>
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68 An example of
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69 the use of the additional arguments is the minimization of an error
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70 function for a neural network:
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71 <PRE>
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72
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73 w = scg('neterr', w, options, 'netgrad', net, x, t);
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74 </PRE>
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75
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76
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77 <p><h2>
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78 Algorithm
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79 </h2>
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80 The search direction is re-started after every <CODE>nparams</CODE>
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81 successful weight updates where <CODE>nparams</CODE> is the total number of
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82 parameters in <CODE>x</CODE>. The algorithm is based on that given by Williams
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83 (1991), with a simplified procedure for updating <CODE>lambda</CODE> when
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84 <CODE>rho < 0.25</CODE>.
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85
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86 <p><h2>
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87 See Also
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88 </h2>
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89 <CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE><hr>
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90 <b>Pages:</b>
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91 <a href="index.htm">Index</a>
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92 <hr>
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93 <p>Copyright (c) Ian T Nabney (1996-9)
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94
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95
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96 </body>
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97 </html> |
