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<center><font color=blue size=5>'''Optimization of FuncDesigner models by OpenOpt'''</font></center>

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Let me remember you - all 1st derivatives for nonlinear problems group are obtaining via [[AD|Automatic differentiation]], not to be confused with finite-difference derivatives approximation.
For the list of problems and solvers available in OpenOpt framework see OpenOpt problems tree: {{#icon: Tree.png|Problems Tree|50|Problems}} 

== LLSP example ==
{{FD_LLSP}}


== LP example ==

{{FD_LP_1}}
See also another one FD LP [http://trac.openopt.org/openopt/browser/PythonPackages/FuncDesigner/FuncDesigner/examples/lp2.py example]


== SLE example ==
{{FD_SLE}}


== NLP example ==

{{FD_NLP_1}}


== NSP example ==

{{FD_NSP_1}}


== NLSP example ==

{{FD_NLSP_1}}


== MILP example ==

{{FD_MILP}}


== GLP example ==
Currently OpenOpt has no [[GLP]] solvers connected that are capable of using derivatives, hence [[FuncDesigner]] [[AD]] features don't benefit you, but for puny problems, where calculations speed isn't of big importance, you can use FuncDesigner for to make your code more clear and short (without code/decode all variables into/from a single optimization vector), also, handling of fixed/unfixed optimization variables is more convenient.
{{FD_GLP}}



== Optimization with some fixed variables ==

{{FD_OWFV}}