diff options
Diffstat (limited to 'ml/dlib/dlib/optimization/optimization_least_squares_abstract.h')
| -rw-r--r-- | ml/dlib/dlib/optimization/optimization_least_squares_abstract.h | 112 |
1 files changed, 112 insertions, 0 deletions
diff --git a/ml/dlib/dlib/optimization/optimization_least_squares_abstract.h b/ml/dlib/dlib/optimization/optimization_least_squares_abstract.h new file mode 100644 index 000000000..aaffb2221 --- /dev/null +++ b/ml/dlib/dlib/optimization/optimization_least_squares_abstract.h @@ -0,0 +1,112 @@ +// Copyright (C) 2010 Davis E. King (davis@dlib.net) +// License: Boost Software License See LICENSE.txt for the full license. +#undef DLIB_OPTIMIZATIOn_LEAST_SQUARES_ABSTRACT_ +#ifdef DLIB_OPTIMIZATIOn_LEAST_SQUARES_ABSTRACT_ + +#include "../matrix/matrix_abstract.h" +#include "optimization_trust_region_abstract.h" + +namespace dlib +{ + +// ---------------------------------------------------------------------------------------- + + template < + typename stop_strategy_type, + typename funct_type, + typename funct_der_type, + typename vector_type, + typename T + > + double solve_least_squares ( + stop_strategy_type stop_strategy, + const funct_type& f, + const funct_der_type& der, + const vector_type& list, + T& x, + double radius = 1 + ); + /*! + requires + - stop_strategy == an object that defines a stop strategy such as one of + the objects from dlib/optimization/optimization_stop_strategies_abstract.h + - list == a matrix or something convertible to a matrix via mat() + such as a std::vector. + - is_vector(list) == true + - list.size() > 0 + - is_col_vector(x) == true + - radius > 0 + - for all valid i: + - f(list(i),x) must be a valid expression that evaluates to a floating point value. + - der(list(i),x) must be a valid expression that evaluates to the derivative of f(list(i),x) + with respect to x. This derivative must take the form of a column vector. + ensures + - This function performs an unconstrained minimization of the least squares + function g(x) defined by: + - g(x) = sum over all i: 0.5*pow( f(list(i),x), 2 ) + - This method combines the Levenberg-Marquardt method with a quasi-newton method + for approximating the second order terms of the hessian and is appropriate for + large residual problems (i.e. problems where the f() function isn't driven to 0). + In particular, it uses the method of Dennis, Gay, and Welsch as described in + Numerical Optimization by Nocedal and Wright (second edition). + - Since this is a trust region algorithm, the radius parameter defines the initial + size of the trust region. + - The function is optimized until stop_strategy decides that an acceptable + point has been found or the trust region subproblem fails to make progress. + - #x == the value of x that was found to minimize g() + - returns g(#x). + !*/ + +// ---------------------------------------------------------------------------------------- + + template < + typename stop_strategy_type, + typename funct_type, + typename funct_der_type, + typename vector_type, + typename T + > + double solve_least_squares_lm ( + stop_strategy_type stop_strategy, + const funct_type& f, + const funct_der_type& der, + const vector_type& list, + T& x, + double radius = 1 + ); + /*! + requires + - stop_strategy == an object that defines a stop strategy such as one of + the objects from dlib/optimization/optimization_stop_strategies_abstract.h + - list == a matrix or something convertible to a matrix via mat() + such as a std::vector. + - is_vector(list) == true + - list.size() > 0 + - is_col_vector(x) == true + - radius > 0 + - for all valid i: + - f(list(i),x) must be a valid expression that evaluates to a floating point value. + - der(list(i),x) must be a valid expression that evaluates to the derivative of f(list(i),x) + with respect to x. This derivative must take the form of a column vector. + ensures + - This function performs an unconstrained minimization of the least squares + function g(x) defined by: + - g(x) = sum over all i: 0.5*pow( f(list(i),x), 2 ) + - This method implements a plain Levenberg-Marquardt approach for approximating + the hessian of g(). Therefore, it is most appropriate for small residual problems + (i.e. problems where f() goes to 0 at the solution). + - Since this is a trust region algorithm, the radius parameter defines the initial + size of the trust region. + - The function is optimized until stop_strategy decides that an acceptable + point has been found or the trust region subproblem fails to make progress. + - #x == the value of x that was found to minimize g() + - returns g(#x). + !*/ + +// ---------------------------------------------------------------------------------------- + +} + +#endif // DLIB_OPTIMIZATIOn_LEAST_SQUARES_ABSTRACT_ + + |