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These algorithms neither use gradient information nor stochastic processes.  
 
These algorithms neither use gradient information nor stochastic processes.  
  
* [[Documentation/Modules/OptAlgSIMPLEX | <code>OptAlgSIMPLEX</code>: Nelder-Mead Simplex Algorithm]]
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* [[Documentation/Modules/OptAlgSIMPLEX | <code>OptAlgSIMPLEX</code>]]: Nelder-Mead Simplex Algorithm
  
 
=== Indirect, Stochastic Algorithms ===
 
=== Indirect, Stochastic Algorithms ===

Version vom 25. Oktober 2015, 12:08 Uhr

Modules contain all the functionality for optimizing and learning. One Modules may contain an optimization algorithm, an artificial neural network, or a problem to optimize.

Here is a list of documented modules in OpenDino. Further modules may exist, but may not yet be documented.

Single Objective Optimization Algorithms

Indirect, Deterministic Algorithms

Indirect algorithms use gradient or higher order derivative information in the optimization.

Not implemented, yet.

Direct, Deterministic Algorithms

These algorithms neither use gradient information nor stochastic processes.

Indirect, Stochastic Algorithms

These algorithms do not use gradient information but require stochastic processes (i.e. random numbers) in their search.

Single and Multi-Objective Optimization Algorithms

Indirect, Stochastic Algorithms

  • Evolutionary Algorithms
    • OptAlgMoCMA: Elitist Evolution Strategy with Covariance Matrix Adaptation
  • Particle Methods

Design of Experiments

Optimization in General

Optimization Problems

Machine Learning

Miscellaneous Modules