mirror of https://github.com/davisking/dlib.git
Moved all the optimization stuff into its own page.
--HG-- rename : docs/docs/algorithms.xml => docs/docs/optimization.xml extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403478
This commit is contained in:
Davis King
parent
becd071a17
commit
cc14e3f9eb
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@ -13,7 +13,7 @@
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This page documents library components that are all basically just implementations of
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mathematical functions or algorithms that don't fit in any of the other pages
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of the dlib documentation. So this includes things like checksums, cryptographic hashes,
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optimization, sorting, etc.
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sorting, etc.
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</p>
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</body>
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@ -30,32 +30,6 @@
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<item>running_stats</item>
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<item>running_covariance</item>
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<item>random_subset_selector</item>
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<item nolink="true">
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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>lagrange_poly_min_extrap</item>
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<item>line_search</item>
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<item>find_min</item>
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<item>find_min_single_variable</item>
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<item>find_min_using_approximate_derivatives</item>
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<item>find_min_bobyqa</item>
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<item>solve_qp_using_smo</item>
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<item>find_max</item>
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<item>find_max_single_variable</item>
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<item>find_max_using_approximate_derivatives</item>
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<item>find_max_bobyqa</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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<item>gradient_norm_stop_strategy</item>
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</sub>
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</item>
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<item nolink="true">
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<name>Quantum Computing</name>
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<sub>
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@ -129,374 +103,7 @@
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<components>
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<!-- ************************************************************************* -->
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<component>
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<name>derivative</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 numerically computes the derivative 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>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). 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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<!-- ************************************************************************* -->
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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_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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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>lagrange_poly_min_extrap</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 function finds the second order polynomial that interpolates a
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set of points and returns you the minimum of that polynomial.
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</description>
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</component>
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<!-- ************************************************************************* -->
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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_line_search_abstract.h</spec_file>
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<description>
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Performs a gradient based line search on a given function and returns the input
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that makes the function significantly smaller.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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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_search_strategies_abstract.h</spec_file>
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<description>
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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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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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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_search_strategies_abstract.h</spec_file>
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<description>
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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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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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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_search_strategies_abstract.h</spec_file>
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<description>
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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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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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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_stop_strategies_abstract.h</spec_file>
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<description>
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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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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>gradient_norm_stop_strategy</name>
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<file>dlib/optimization.h</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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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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norm (i.e. the length) of the current gradient vector and
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stops if it is smaller than a user given threshold.
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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_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_single_variable</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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Performs a bound constrained minimization of a nonlinear function. The
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function must be of a single variable. Derivatives are not required.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>solve_qp_using_smo</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_solve_qp_using_smo_abstract.h</spec_file>
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<description>
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This function solves the following quadratic program:
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<pre>
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Minimize: f(alpha) == 0.5*trans(alpha)*Q*alpha - trans(alpha)*b
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subject to the following constraints:
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sum(alpha) == C
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min(alpha) >= 0
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</pre>
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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_min_bobyqa</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_bobyqa_abstract.h</spec_file>
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<description>
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This function defines the dlib interface to the BOBYQA software developed by M.J.D Powell.
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BOBYQA is a method for optimizing a function in the absence of derivative information.
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Powell described it as a method that seeks the least value of a function of many
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variables, by applying a trust region method that forms quadratic models by
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interpolation. There is usually some freedom in the interpolation conditions,
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which is taken up by minimizing the Frobenius norm of the change to the second
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derivative of the model, beginning with the zero matrix. The values of the variables
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are constrained by upper and lower bounds.
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<p>
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The following paper, published in 2009 by Powell, describes the
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detailed working of the BOBYQA algorithm.
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<blockquote>
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The BOBYQA algorithm for bound constrained optimization
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without derivatives by M.J.D. Powell
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</blockquote>
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</p>
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<p>
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Note that BOBYQA only works on functions of two or more variables. So if you need to perform
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derivative-free optimization on a function of a single variable
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then you should use the <a href="#find_min_single_variable">find_min_single_variable</a>
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function.
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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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<example>model_selection_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_bobyqa</name>
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<file>dlib/optimization.h</file>
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<spec_file link="true">dlib/optimization/optimization_bobyqa_abstract.h</spec_file>
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<description>
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This function is identical to the <a href="#find_min_bobyqa">find_min_bobyqa</a> routine
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except that it negates the objective function before performing optimization.
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Thus this function will attempt to find the maximizer of the objective rather than
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the minimizer.
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<p>
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Note that BOBYQA only works on functions of two or more variables. So if you need to perform
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derivative-free optimization on a function of a single variable
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then you should use the <a href="#find_max_single_variable">find_max_single_variable</a>
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function.
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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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<example>model_selection_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>
|
||||
<description>
|
||||
Performs an unconstrained maximization of a nonlinear function using
|
||||
some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
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|
||||
<component>
|
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<name>find_max_single_variable</name>
|
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<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
|
||||
<description>
|
||||
Performs a bound constrained maximization of a nonlinear function. The
|
||||
function must be of a single variable. Derivatives are not required.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_max_using_approximate_derivatives</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
|
||||
<description>
|
||||
Performs an unconstrained maximization of a nonlinear function using
|
||||
some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
|
||||
This version doesn't take a gradient function but instead numerically approximates
|
||||
the gradient.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component checked="true">
|
||||
<name>bigint</name>
|
||||
<file>dlib/bigint.h</file>
|
||||
|
|
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|||
|
|
@ -222,7 +222,7 @@
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</ul><br/>
|
||||
</li>
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<li> <i>Introduction to Derivative-Free Optimization</i> by Conn, Scheinberg, and Vicente
|
||||
<ul> If you want to understand algorithms like <a href="algorithms.html#find_min_bobyqa">BOBYQA</a>
|
||||
<ul> If you want to understand algorithms like <a href="optimization.html#find_min_bobyqa">BOBYQA</a>
|
||||
then this is a good recent book on the subject. Note that a book like <i>Practical Methods of Optimization</i>
|
||||
is almost certainly a prerequisite for reading this book. As an aside, BOBYQA is not discussed in this book but
|
||||
its predecessor, NEWUOA is.
|
||||
|
|
|
|||
|
|
@ -109,11 +109,11 @@
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|||
<a href="dlib/matrix/matrix_utilities_abstract.h.html#trans">transpose</a>,
|
||||
<a href="dlib/matrix/matrix_math_functions_abstract.h.html#sin">trig functions</a>, etc.</li>
|
||||
<li>Unconstrained non-linear optimization algorithms using the
|
||||
<a href="algorithms.html#cg_search_strategy">conjugate gradient</a>,
|
||||
<a href="algorithms.html#bfgs_search_strategy">BFGS</a>, and
|
||||
<a href="algorithms.html#lbfgs_search_strategy">L-BFGS</a> techniques</li>
|
||||
<a href="optimization.html#cg_search_strategy">conjugate gradient</a>,
|
||||
<a href="optimization.html#bfgs_search_strategy">BFGS</a>, and
|
||||
<a href="optimization.html#lbfgs_search_strategy">L-BFGS</a> techniques</li>
|
||||
<li>Box-constrained derivative-free optimization via the
|
||||
<a href="algorithms.html#find_min_bobyqa">BOBYQA</a> algorithm</li>
|
||||
<a href="optimization.html#find_min_bobyqa">BOBYQA</a> algorithm</li>
|
||||
<li>A <a href="algorithms.html#bigint">big integer</a> object</li>
|
||||
<li>A <a href="algorithms.html#rand">random number</a> object</li>
|
||||
</ul>
|
||||
|
|
|
|||
|
|
@ -10,6 +10,11 @@
|
|||
<link>algorithms.html</link>
|
||||
<chm_sub>algorithms.xml</chm_sub>
|
||||
</item>
|
||||
<item>
|
||||
<name>Optimization</name>
|
||||
<link>optimization.html</link>
|
||||
<chm_sub>optimization.xml</chm_sub>
|
||||
</item>
|
||||
<item>
|
||||
<name>Machine Learning</name>
|
||||
<link>ml.html</link>
|
||||
|
|
@ -31,7 +36,7 @@
|
|||
<chm_sub>api.xml</chm_sub>
|
||||
</item>
|
||||
<item>
|
||||
<name>Network</name>
|
||||
<name>Networking</name>
|
||||
<link>network.html</link>
|
||||
<chm_sub>network.xml</chm_sub>
|
||||
</item>
|
||||
|
|
|
|||
|
|
@ -0,0 +1,439 @@
|
|||
<?xml version="1.0" encoding="ISO-8859-1"?>
|
||||
<?xml-stylesheet type="text/xsl" href="stylesheet.xsl"?>
|
||||
|
||||
<doc>
|
||||
<title>Optimization</title>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<body>
|
||||
<br/><br/>
|
||||
|
||||
<p>
|
||||
This page documents library components that attempt to find the
|
||||
minimum or maximum of a user supplied function.
|
||||
</p>
|
||||
|
||||
</body>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<menu width="150">
|
||||
<top>
|
||||
<section>
|
||||
<name>Main Routines</name>
|
||||
<item>find_min</item>
|
||||
<item>find_min_single_variable</item>
|
||||
<item>find_min_using_approximate_derivatives</item>
|
||||
<item>find_min_bobyqa</item>
|
||||
<item>solve_qp_using_smo</item>
|
||||
<item>find_max</item>
|
||||
<item>find_max_single_variable</item>
|
||||
<item>find_max_using_approximate_derivatives</item>
|
||||
<item>find_max_bobyqa</item>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<name>Strategies</name>
|
||||
<item>cg_search_strategy</item>
|
||||
<item>bfgs_search_strategy</item>
|
||||
<item>lbfgs_search_strategy</item>
|
||||
<item>objective_delta_stop_strategy</item>
|
||||
<item>gradient_norm_stop_strategy</item>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<name>Helper Routines</name>
|
||||
<item>derivative</item>
|
||||
<item>negate_function</item>
|
||||
<item>make_line_search_function</item>
|
||||
<item>poly_min_extrap</item>
|
||||
<item>lagrange_poly_min_extrap</item>
|
||||
<item>line_search</item>
|
||||
</section>
|
||||
|
||||
</top>
|
||||
</menu>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
<!-- ************************************************************************* -->
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<components>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>derivative</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
|
||||
<description>
|
||||
This is a function that takes another function as input and returns
|
||||
a function object that numerically computes the derivative of the input function.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>negate_function</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
|
||||
<description>
|
||||
This is a function that takes another function as input and returns
|
||||
a function object that computes the negation of the input function.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>make_line_search_function</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
|
||||
<description>
|
||||
This is a function that takes another function f(x) as input and returns
|
||||
a function object l(z) = f(start + z*direction). It is useful for
|
||||
turning multi-variable functions into single-variable functions for
|
||||
use with the <a href="#line_search">line_search</a> routine.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>poly_min_extrap</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
|
||||
<description>
|
||||
This function finds the 3rd degree polynomial that interpolates a
|
||||
set of points and returns you the minimum of that polynomial.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>lagrange_poly_min_extrap</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
|
||||
<description>
|
||||
This function finds the second order polynomial that interpolates a
|
||||
set of points and returns you the minimum of that polynomial.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>line_search</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
|
||||
<description>
|
||||
Performs a gradient based line search on a given function and returns the input
|
||||
that makes the function significantly smaller.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>cg_search_strategy</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_search_strategies_abstract.h</spec_file>
|
||||
<description>
|
||||
This object represents a strategy for determining which direction
|
||||
a <a href="#line_search">line search</a> should be carried out along. This particular object
|
||||
is an implementation of the Polak-Ribiere conjugate gradient method
|
||||
for determining this direction.
|
||||
|
||||
<p>
|
||||
This method uses an amount of memory that is linear in the number
|
||||
of variables to be optimized. So it is capable of handling problems
|
||||
with a very large number of variables. However, it is generally
|
||||
not as good as the L-BFGS algorithm (see the
|
||||
<a href="#lbfgs_search_strategy">lbfgs_search_strategy</a> class).
|
||||
</p>
|
||||
</description>
|
||||
<examples>
|
||||
<example>optimization_ex.cpp.html</example>
|
||||
</examples>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>bfgs_search_strategy</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_search_strategies_abstract.h</spec_file>
|
||||
<description>
|
||||
This object represents a strategy for determining which direction
|
||||
a <a href="#line_search">line search</a> should be carried out along. This particular object
|
||||
is an implementation of the BFGS quasi-newton method for determining
|
||||
this direction.
|
||||
|
||||
<p>
|
||||
This method uses an amount of memory that is quadratic in the number
|
||||
of variables to be optimized. It is generally very effective but
|
||||
if your problem has a very large number of variables then it isn't
|
||||
appropriate. Instead You should try the <a href="#lbfgs_search_strategy">lbfgs_search_strategy</a>.
|
||||
</p>
|
||||
</description>
|
||||
<examples>
|
||||
<example>optimization_ex.cpp.html</example>
|
||||
</examples>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>lbfgs_search_strategy</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_search_strategies_abstract.h</spec_file>
|
||||
<description>
|
||||
This object represents a strategy for determining which direction
|
||||
a <a href="#line_search">line search</a> should be carried out along. This particular object
|
||||
is an implementation of the L-BFGS quasi-newton method for determining
|
||||
this direction.
|
||||
|
||||
<p>
|
||||
This method uses an amount of memory that is linear in the number
|
||||
of variables to be optimized. This makes it an excellent method
|
||||
to use when an optimization problem has a large number of variables.
|
||||
</p>
|
||||
</description>
|
||||
<examples>
|
||||
<example>optimization_ex.cpp.html</example>
|
||||
</examples>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>objective_delta_stop_strategy</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_stop_strategies_abstract.h</spec_file>
|
||||
<description>
|
||||
This object represents a strategy for deciding if an optimization
|
||||
algorithm should terminate. This particular object looks at the
|
||||
change in the objective function from one iteration to the next and
|
||||
bases its decision on how large this change is. If the change
|
||||
is below a user given threshold then the search stops.
|
||||
</description>
|
||||
<examples>
|
||||
<example>optimization_ex.cpp.html</example>
|
||||
</examples>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>gradient_norm_stop_strategy</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_stop_strategies_abstract.h</spec_file>
|
||||
<description>
|
||||
This object represents a strategy for deciding if an optimization
|
||||
algorithm should terminate. This particular object looks at the
|
||||
norm (i.e. the length) of the current gradient vector and
|
||||
stops if it is smaller than a user given threshold.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_min</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
|
||||
<description>
|
||||
Performs an unconstrained minimization of a nonlinear function using
|
||||
some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
|
||||
</description>
|
||||
<examples>
|
||||
<example>optimization_ex.cpp.html</example>
|
||||
</examples>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_min_single_variable</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
|
||||
<description>
|
||||
Performs a bound constrained minimization of a nonlinear function. The
|
||||
function must be of a single variable. Derivatives are not required.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>solve_qp_using_smo</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_solve_qp_using_smo_abstract.h</spec_file>
|
||||
<description>
|
||||
This function solves the following quadratic program:
|
||||
<pre>
|
||||
Minimize: f(alpha) == 0.5*trans(alpha)*Q*alpha - trans(alpha)*b
|
||||
subject to the following constraints:
|
||||
sum(alpha) == C
|
||||
min(alpha) >= 0
|
||||
</pre>
|
||||
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
|
||||
<component>
|
||||
<name>find_min_bobyqa</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_bobyqa_abstract.h</spec_file>
|
||||
<description>
|
||||
This function defines the dlib interface to the BOBYQA software developed by M.J.D Powell.
|
||||
BOBYQA is a method for optimizing a function in the absence of derivative information.
|
||||
Powell described it as a method that seeks the least value of a function of many
|
||||
variables, by applying a trust region method that forms quadratic models by
|
||||
interpolation. There is usually some freedom in the interpolation conditions,
|
||||
which is taken up by minimizing the Frobenius norm of the change to the second
|
||||
derivative of the model, beginning with the zero matrix. The values of the variables
|
||||
are constrained by upper and lower bounds.
|
||||
|
||||
<p>
|
||||
The following paper, published in 2009 by Powell, describes the
|
||||
detailed working of the BOBYQA algorithm.
|
||||
|
||||
<blockquote>
|
||||
The BOBYQA algorithm for bound constrained optimization
|
||||
without derivatives by M.J.D. Powell
|
||||
</blockquote>
|
||||
</p>
|
||||
|
||||
<p>
|
||||
Note that BOBYQA only works on functions of two or more variables. So if you need to perform
|
||||
derivative-free optimization on a function of a single variable
|
||||
then you should use the <a href="#find_min_single_variable">find_min_single_variable</a>
|
||||
function.
|
||||
</p>
|
||||
|
||||
</description>
|
||||
<examples>
|
||||
<example>optimization_ex.cpp.html</example>
|
||||
<example>model_selection_ex.cpp.html</example>
|
||||
</examples>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_max_bobyqa</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_bobyqa_abstract.h</spec_file>
|
||||
<description>
|
||||
This function is identical to the <a href="#find_min_bobyqa">find_min_bobyqa</a> routine
|
||||
except that it negates the objective function before performing optimization.
|
||||
Thus this function will attempt to find the maximizer of the objective rather than
|
||||
the minimizer.
|
||||
<p>
|
||||
Note that BOBYQA only works on functions of two or more variables. So if you need to perform
|
||||
derivative-free optimization on a function of a single variable
|
||||
then you should use the <a href="#find_max_single_variable">find_max_single_variable</a>
|
||||
function.
|
||||
</p>
|
||||
</description>
|
||||
<examples>
|
||||
<example>optimization_ex.cpp.html</example>
|
||||
<example>model_selection_ex.cpp.html</example>
|
||||
</examples>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_min_using_approximate_derivatives</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
|
||||
<description>
|
||||
Performs an unconstrained minimization of a nonlinear function using
|
||||
some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
|
||||
This version doesn't take a gradient function but instead numerically approximates
|
||||
the gradient.
|
||||
</description>
|
||||
<examples>
|
||||
<example>optimization_ex.cpp.html</example>
|
||||
</examples>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_max</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
|
||||
<description>
|
||||
Performs an unconstrained maximization of a nonlinear function using
|
||||
some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_max_single_variable</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_line_search_abstract.h</spec_file>
|
||||
<description>
|
||||
Performs a bound constrained maximization of a nonlinear function. The
|
||||
function must be of a single variable. Derivatives are not required.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_max_using_approximate_derivatives</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
|
||||
<description>
|
||||
Performs an unconstrained maximization of a nonlinear function using
|
||||
some search strategy (e.g. <a href="#bfgs_search_strategy">bfgs_search_strategy</a>).
|
||||
This version doesn't take a gradient function but instead numerically approximates
|
||||
the gradient.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
</components>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
|
||||
</doc>
|
||||
|
||||
|
|
@ -32,27 +32,27 @@
|
|||
<term file="dlib/optimization/optimization_line_search_abstract.h.html" name="optimize_single_variable_failure"/>
|
||||
<term file="dlib/optimization/optimization_bobyqa_abstract.h.html" name="bobyqa_failure"/>
|
||||
|
||||
<term file="algorithms.html" name="derivative"/>
|
||||
<term file="algorithms.html" name="make_line_search_function"/>
|
||||
<term file="algorithms.html" name="poly_min_extrap"/>
|
||||
<term file="algorithms.html" name="lagrange_poly_min_extrap"/>
|
||||
<term file="algorithms.html" name="line_search"/>
|
||||
<term file="algorithms.html" name="find_min"/>
|
||||
<term file="algorithms.html" name="find_min_single_variable"/>
|
||||
<term file="algorithms.html" name="find_min_using_approximate_derivatives"/>
|
||||
<term file="algorithms.html" name="find_min_bobyqa"/>
|
||||
<term file="algorithms.html" name="solve_qp_using_smo"/>
|
||||
<term link="algorithms.html#find_min_bobyqa" name="BOBYQA"/>
|
||||
<term file="algorithms.html" name="find_max"/>
|
||||
<term file="algorithms.html" name="find_max_single_variable"/>
|
||||
<term file="algorithms.html" name="find_max_using_approximate_derivatives"/>
|
||||
<term file="algorithms.html" name="find_max_bobyqa"/>
|
||||
<term file="algorithms.html" name="objective_delta_stop_strategy"/>
|
||||
<term file="algorithms.html" name="gradient_norm_stop_strategy"/>
|
||||
<term file="algorithms.html" name="negate_function"/>
|
||||
<term file="algorithms.html" name="cg_search_strategy"/>
|
||||
<term file="algorithms.html" name="bfgs_search_strategy"/>
|
||||
<term file="algorithms.html" name="lbfgs_search_strategy"/>
|
||||
<term file="optimization.html" name="derivative"/>
|
||||
<term file="optimization.html" name="make_line_search_function"/>
|
||||
<term file="optimization.html" name="poly_min_extrap"/>
|
||||
<term file="optimization.html" name="lagrange_poly_min_extrap"/>
|
||||
<term file="optimization.html" name="line_search"/>
|
||||
<term file="optimization.html" name="find_min"/>
|
||||
<term file="optimization.html" name="find_min_single_variable"/>
|
||||
<term file="optimization.html" name="find_min_using_approximate_derivatives"/>
|
||||
<term file="optimization.html" name="find_min_bobyqa"/>
|
||||
<term file="optimization.html" name="solve_qp_using_smo"/>
|
||||
<term link="optimization.html#find_min_bobyqa" name="BOBYQA"/>
|
||||
<term file="optimization.html" name="find_max"/>
|
||||
<term file="optimization.html" name="find_max_single_variable"/>
|
||||
<term file="optimization.html" name="find_max_using_approximate_derivatives"/>
|
||||
<term file="optimization.html" name="find_max_bobyqa"/>
|
||||
<term file="optimization.html" name="objective_delta_stop_strategy"/>
|
||||
<term file="optimization.html" name="gradient_norm_stop_strategy"/>
|
||||
<term file="optimization.html" name="negate_function"/>
|
||||
<term file="optimization.html" name="cg_search_strategy"/>
|
||||
<term file="optimization.html" name="bfgs_search_strategy"/>
|
||||
<term file="optimization.html" name="lbfgs_search_strategy"/>
|
||||
|
||||
<term file="bayes.html" name="set_node_value"/>
|
||||
<term file="bayes.html" name="node_value"/>
|
||||
|
|
|
|||
Loading…
Reference in New Issue