Difference between revisions of "Optimizer"
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| − | The SUMO Toolbox contains a whole hierarchy of optimizers that can be used by several components (for instance the [[SampleSelector# | + | The SUMO Toolbox contains a whole hierarchy of optimizers that can be used by several components (for instance the [[Config:SampleSelector#expectedImprovement|EGO algorithm]] or the [[Config:AdaptiveModelBuilder#krigingoptim|OptimizerModelBuilder]]). |
| − | A list of available optimizers is found in <code>src/matlab/tools/ | + | A list of available optimizers is found in <code>src/matlab/tools/Optimization/optimizers</code>. Note that some optimizers need some external code available in the extension pack (or the Matlab Optimization toolbox). |
| − | + | A couple of '''example configurations''' are found [[Config:Optimizer|here]]. These tags can be used in supported components as stated above. When a particular option is not given a sane default will be used. Full information about the several options can be found in the optimizer source code, implementation and corresponding papers. | |
| + | |||
| + | === Add a custom optimizer === | ||
| + | In order to implement a new optimization algorithm one should only create one new class in <code>src/matlab/tools/Optimization/optimizers</code>. The class should be derived from the base class Optimizer and MUST implement at least the following functions: | ||
| + | * A constructor accepting one parameter (the configuration xml tag) OR a varargin | ||
| + | * [this, x, fval] = optimize(this, arg) | ||
| + | ** Accepts a function handle OR a SUMO model to optimize. x and fval are the resulting optimum(s) and associated function value(s). | ||
| + | In addition the following methods COULD be implemented if appropriate: | ||
| + | * size = getPopulationSize(this) | ||
| + | ** When implementing a population-based optimization algorithm | ||
| + | * this = setInputConstraints( this, con ) | ||
| + | ** When the algorithm supports constraints | ||
| + | |||
| + | It is advised to copy a simple existing optimizer and work from there. For example implementations, please see the provided optimizers. For instance, DifferentialEvolution is a good starting point. The optimizer can be used like: | ||
| + | <source lang="xml"> | ||
| + | <Optimizer type="CLASSNAME"> | ||
| + | <Option key="option1" value="1" /> | ||
| + | <Option key="option2" value="2" /> | ||
| + | ... | ||
| + | </Optimizer> | ||
| + | </source> | ||
Latest revision as of 17:33, 30 January 2012
The SUMO Toolbox contains a whole hierarchy of optimizers that can be used by several components (for instance the EGO algorithm or the OptimizerModelBuilder).
A list of available optimizers is found in src/matlab/tools/Optimization/optimizers. Note that some optimizers need some external code available in the extension pack (or the Matlab Optimization toolbox).
A couple of example configurations are found here. These tags can be used in supported components as stated above. When a particular option is not given a sane default will be used. Full information about the several options can be found in the optimizer source code, implementation and corresponding papers.
Add a custom optimizer
In order to implement a new optimization algorithm one should only create one new class in src/matlab/tools/Optimization/optimizers. The class should be derived from the base class Optimizer and MUST implement at least the following functions:
- A constructor accepting one parameter (the configuration xml tag) OR a varargin
- [this, x, fval] = optimize(this, arg)
- Accepts a function handle OR a SUMO model to optimize. x and fval are the resulting optimum(s) and associated function value(s).
In addition the following methods COULD be implemented if appropriate:
- size = getPopulationSize(this)
- When implementing a population-based optimization algorithm
- this = setInputConstraints( this, con )
- When the algorithm supports constraints
It is advised to copy a simple existing optimizer and work from there. For example implementations, please see the provided optimizers. For instance, DifferentialEvolution is a good starting point. The optimizer can be used like:
<Optimizer type="CLASSNAME">
<Option key="option1" value="1" />
<Option key="option2" value="2" />
...
</Optimizer>
