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updated the docs
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Davis King
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@ -32,13 +32,19 @@
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<name>Optimization</name>
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<sub>
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<item>derivative</item>
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<item>negate_function</item>
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<item>make_line_search_function</item>
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<item>poly_min_extrap</item>
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<item>line_search</item>
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<item>find_min_quasi_newton</item>
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<item>find_min_conjugate_gradient</item>
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<item>find_min_quasi_newton2</item>
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<item>find_min_conjugate_gradient2</item>
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<item>find_min</item>
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<item>find_min_using_approximate_derivatives</item>
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<item>find_max</item>
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<item>find_max_using_approximate_derivatives</item>
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<item>cg_search_strategy</item>
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<item>bfgs_search_strategy</item>
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<item>lbfgs_search_strategy</item>
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<item>objective_delta_stop_strategy</item>
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</sub>
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</item>
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<item nolink="true">
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@ -129,12 +135,28 @@
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<!-- ************************************************************************* -->
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<component>
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<name>make_line_search_function</name>
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<name>negate_function</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<description>
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This is a function that takes another function as input and returns
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a function object that computes the negation of the input function.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>make_line_search_function</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
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<description>
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This is a function that takes another function f(x) as input and returns
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a function object l(z) = f(start + z*direction).
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a function object l(z) = f(start + z*direction). It is useful for
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turning multi-variable functions into single-variable functions for
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use with the <a href="#line_search">line_search</a> routine.
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</description>
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</component>
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@ -145,7 +167,7 @@
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<component>
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<name>poly_min_extrap</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
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<description>
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This function finds the 3rd degree polynomial that interpolates a
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set of points and returns you the minimum of that polynomial.
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@ -158,7 +180,7 @@
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<component>
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<name>line_search</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
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<description>
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Performs a line search on a given function and returns the input
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that makes the function significantly smaller.
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@ -168,14 +190,24 @@
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<!-- ************************************************************************* -->
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<component>
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<name>find_min_quasi_newton</name>
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<name>cg_search_strategy</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<spec_file link="true">dlib/optimization/optimization_search_strategies_abstract.h</spec_file>
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<description>
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Performs an unconstrained minimization of the potentially nonlinear function f() using the
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BFGS quasi newton method.
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This object represents a strategy for determining which direction
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a <a href="#line_search">line search</a> should be carried out along. This particular object
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is an implementation of the Polak-Ribiere conjugate gradient method
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for determining this direction.
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<p>
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This method uses an amount of memory that is linear in the number
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of variables to be optimized. So it is capable of handling problems
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with a very large number of variables. However, it is generally
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not as good as the L-BFGS algorithm (see the
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<a href="#lbfgs_search_strategy">lbfgs_search_strategy</a> class).
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</p>
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</description>
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<examples>
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<example>optimization_ex.cpp.html</example>
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@ -186,12 +218,21 @@
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<!-- ************************************************************************* -->
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<component>
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<name>find_min_conjugate_gradient</name>
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<name>bfgs_search_strategy</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<spec_file link="true">dlib/optimization/optimization_search_strategies_abstract.h</spec_file>
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<description>
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Performs an unconstrained minimization of the potentially nonlinear function f() using a
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conjugate gradient method.
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This object represents a strategy for determining which direction
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a <a href="#line_search">line search</a> should be carried out along. This particular object
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is an implementation of the BFGS quasi-newton method for determining
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this direction.
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<p>
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This method uses an amount of memory that is quadratic in the number
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of variables to be optimized. It is generally very effective but
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if your problem has a very large number of variables then it isn't
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appropriate. Instead You should try the <a href="#lbfgs_search_strategy">lbfgs_search_strategy</a>.
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</p>
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</description>
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<examples>
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<example>optimization_ex.cpp.html</example>
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@ -202,13 +243,20 @@
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<!-- ************************************************************************* -->
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<component>
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<name>find_min_quasi_newton2</name>
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<name>lbfgs_search_strategy</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<spec_file link="true">dlib/optimization/optimization_search_strategies_abstract.h</spec_file>
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<description>
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Performs an unconstrained minimization of the potentially nonlinear function f() using the
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BFGS quasi newton method. This version doesn't take a gradient function of f()
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but instead numerically approximates the gradient.
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This object represents a strategy for determining which direction
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a <a href="#line_search">line search</a> should be carried out along. This particular object
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is an implementation of the L-BFGS quasi-newton method for determining
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this direction.
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<p>
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This method uses an amount of memory that is linear in the number
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of variables to be optimized. This makes it an excellent method
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to use when an optimization problem has a large number of variables.
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</p>
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</description>
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<examples>
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<example>optimization_ex.cpp.html</example>
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@ -219,13 +267,15 @@
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<!-- ************************************************************************* -->
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<component>
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<name>find_min_conjugate_gradient2</name>
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<name>objective_delta_stop_strategy</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<spec_file link="true">dlib/optimization/optimization_stop_strategies_abstract.h</spec_file>
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<description>
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Performs an unconstrained minimization of the potentially nonlinear function f() using a
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conjugate gradient method. This version doesn't take a gradient function of f()
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but instead numerically approximates the gradient.
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This object represents a strategy for deciding if an optimization
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algorithm should terminate. This particular object looks at the
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change in the objective function from one iteration to the next and
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bases its decision on how large this change is. If the change
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is below a user given threshold then the search stops.
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</description>
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<examples>
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<example>optimization_ex.cpp.html</example>
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@ -233,6 +283,68 @@
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>find_min</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<description>
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Performs an unconstrained minimization of a nonlinear function using
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some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
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</description>
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<examples>
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<example>optimization_ex.cpp.html</example>
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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>find_min_using_approximate_derivatives</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<description>
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Performs an unconstrained minimization of a nonlinear function using
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some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
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This version doesn't take a gradient function but instead numerically approximates
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the gradient.
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</description>
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<examples>
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<example>optimization_ex.cpp.html</example>
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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>find_max</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<description>
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Performs an unconstrained maximization of a nonlinear function using
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some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>find_max_using_approximate_derivatives</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
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<description>
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Performs an unconstrained maximization of a nonlinear function using
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some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
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This version doesn't take a gradient function but instead numerically approximates
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the gradient.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component checked="true">
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@ -109,7 +109,9 @@
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<a href="dlib/matrix/matrix_utilities_abstract.h.html#trans">transpose</a>,
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<a href="dlib/matrix/matrix_math_functions_abstract.h.html#sin">trig functions</a>, etc...</li>
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<li>Unconstrained non-linear optimization algorithms such as
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<a href="algorithms.html#find_min_conjugate_gradient">conjugate gradient</a> and <a href="algorithms.html#find_min_quasi_newton">quasi newton</a> techniques</li>
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<a href="algorithms.html#cg_search_strategy">conjugate gradient</a>,
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<a href="algorithms.html#bfgs_search_strategy">BFGS</a>, and
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<a href="algorithms.html#lbfgs_search_strategy">L-BFGS</a> techniques</li>
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<li>A <a href="algorithms.html#bigint">big integer</a> object</li>
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<li>A <a href="algorithms.html#rand">random number</a> object</li>
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</ul>
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@ -33,10 +33,15 @@
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<term file="algorithms.html" name="make_line_search_function"/>
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<term file="algorithms.html" name="poly_min_extrap"/>
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<term file="algorithms.html" name="line_search"/>
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<term file="algorithms.html" name="find_min_quasi_newton"/>
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<term file="algorithms.html" name="find_min_conjugate_gradient"/>
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<term file="algorithms.html" name="find_min_quasi_newton2"/>
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<term file="algorithms.html" name="find_min_conjugate_gradient2"/>
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<term file="algorithms.html" name="find_min"/>
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<term file="algorithms.html" name="find_min_using_approximate_derivatives"/>
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<term file="algorithms.html" name="find_max"/>
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<term file="algorithms.html" name="find_max_using_approximate_derivatives"/>
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<term file="algorithms.html" name="objective_delta_stop_strategy"/>
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<term file="algorithms.html" name="negate_function"/>
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<term file="algorithms.html" name="cg_search_strategy"/>
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<term file="algorithms.html" name="bfgs_search_strategy"/>
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<term file="algorithms.html" name="lbfgs_search_strategy"/>
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<term file="bayes.html" name="set_node_value"/>
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<term file="bayes.html" name="node_value"/>
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