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1 @c Copyright (C) 1996, 1997 John W. Eaton |
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2 @c This is part of the Octave manual. |
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3 @c For copying conditions, see the file gpl.texi. |
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4 |
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5 @node Optimization |
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6 @chapter Optimization |
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7 |
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8 Octave comes with support for solving various kinds of optimization |
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9 problems. Specifically Octave can solve problems in Linear Programming, |
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10 Quadratic Programming, Nonlinear Programming, and Linear Least Squares |
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11 Minimization. |
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12 |
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13 @menu |
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14 * Linear Programming:: |
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15 * Quadratic Programming:: |
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16 * Nonlinear Programming:: |
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17 * Linear Least Squares:: |
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18 @end menu |
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19 |
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20 @c @cindex linear programming |
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21 @cindex quadratic programming |
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22 @cindex nonlinear programming |
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23 @cindex optimization |
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24 @cindex LP |
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25 @cindex QP |
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26 @cindex NLP |
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27 |
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28 @node Linear Programming |
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29 @section Linear Programming |
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30 |
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31 Octave can solve Linear Programming problems using the @code{glpk} |
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32 function. That is, Octave can solve |
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33 |
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34 @iftex |
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35 @tex |
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36 $$ |
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37 \min_x c^T x |
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38 $$ |
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39 @end tex |
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40 @end iftex |
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41 @ifnottex |
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42 @example |
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43 min C'*x |
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44 @end example |
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45 @end ifnottex |
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46 subject to the linear constraints |
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47 @iftex |
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48 @tex |
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49 $Ax = b$ where $x \geq 0$. |
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50 @end tex |
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51 @end iftex |
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52 @ifnottex |
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53 @math{A*x = b} where @math{x >= 0}. |
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54 @end ifnottex |
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55 |
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56 @noindent |
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57 The @code{glpk} function also supports variations of this problem. |
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58 |
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59 @DOCSTRING(glpk) |
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60 |
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61 @node Quadratic Programming |
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62 @section Quadratic Programming |
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63 |
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64 Octave can also solve Quadratic Programming problems, this is |
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65 @iftex |
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66 @tex |
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67 $$ |
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68 \min_x {1 \over 2} x^T H x + x^T q |
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69 $$ |
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70 @end tex |
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71 @end iftex |
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72 @ifnottex |
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73 @example |
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74 min 0.5 x'*H*x + x'*q |
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75 @end example |
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76 @end ifnottex |
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77 subject to |
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78 @iftex |
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79 @tex |
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80 $$ |
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81 Ax = b \qquad lb \leq x \leq ub \qquad A_{lb} \leq A_{in} \leq A_{ub} |
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82 $$ |
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83 @end tex |
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84 @end iftex |
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85 @ifnottex |
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86 @example |
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87 A*x = b |
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88 lb <= x <= ub |
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89 A_lb <= A_in*x <= A_ub |
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90 @end example |
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91 @end ifnottex |
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92 |
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93 @DOCSTRING(qp) |
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94 |
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95 @node Nonlinear Programming |
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96 @section Nonlinear Programming |
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97 |
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98 Octave can also perform general nonlinear minimization using a |
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99 successive quadratic programming solver. |
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100 |
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101 @DOCSTRING(sqp) |
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102 |
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103 @node Linear Least Squares |
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104 @section Linear Least Squares |
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105 |
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106 Octave also supports linear least squares minimization. That is, |
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107 Octave can find the parameter @math{b} such the the model |
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108 @iftex |
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109 @tex |
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110 $y = xb$ |
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111 @end tex |
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112 @end iftex |
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113 @ifnottex |
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114 @math{y = x*b} |
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115 @end ifnottex |
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116 fits data @math{(x,y)} as good as possible, assuming zero-mean |
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117 Gaussian noise. If the noise is assumed to be isotropic the problem |
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118 can be solved using the @samp{\} or @samp{/} operators, or the @code{ols} |
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119 function. In the general case where the noise is assumed to be anisotropic |
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120 the @code{gls} is needed. |
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121 |
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122 @DOCSTRING(ols) |
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123 |
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124 @DOCSTRING(gls) |
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125 |
