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@ -44,7 +44,7 @@ namespace dlib
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- This function performs an unconstrained minimization of the least squares
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function g(x) defined by:
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- g(x) = sum over all i: 0.5*pow( f(list(i),x), 2 )
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- This method combines the Levenberg–Marquardt method with a quasi-newton method
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- This method combines the Levenberg-Marquardt method with a quasi-newton method
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for approximating the second order terms of the hessian and is appropriate for
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large residual problems (i.e. problems where the f() function isn't driven to 0).
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In particular, it uses the method of Dennis, Gay, and Welsch as described in
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@ -92,7 +92,7 @@ namespace dlib
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- This function performs an unconstrained minimization of the least squares
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function g(x) defined by:
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- g(x) = sum over all i: 0.5*pow( f(list(i),x), 2 )
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- This method implements a plain Levenberg–Marquardt approach for approximating
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- This method implements a plain Levenberg-Marquardt approach for approximating
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the hessian of g(). Therefore, it is most appropriate for small residual problems
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(i.e. problems where f() goes to 0 at the solution).
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- Since this is a trust region algorithm, the radius parameter defines the initial
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