Numerical Optimization

The Optimization section contains methods to search for the best solution among alternatives.

Mathematically, this means finding extreme values of some objective function given a defined domain. Optimization methods cover a variety of different types of objective functions and different types of domains.

Implementation

INumericalOptimization is a common interface for all numerical optimization algorithms. This interface exposes an event LogMessage that occurs every time the INumericalOptimization wants to send a log message, and the following methods:

Method	Description	Performance
minimum	Finds minimum of constrained multivariable function and returns true in case the optimization algorithm succeeded. Minimize
minimum with initial point provided	Finds minimum of constrained multivariable function and returns true in case the optimization algorithm succeeded. The initial point for the algorithm is passed as a parameter. Minimize(Vector)

Method

Description

Performance

minimum

Finds minimum of constrained multivariable function and returns true in case the optimization algorithm succeeded.

method Minimize

minimum with initial point provided

Finds minimum of constrained multivariable function and returns true in case the optimization algorithm succeeded. The initial point for the algorithm is passed as a parameter.

method Minimize(Vector)

There is also a set of properties exposed:

Property	Description	Performance
tolerance	Algorithm termination tolerance, a positive scalar. Tolerance
iterations done	Number of iterations done. IterationsDone
iterations limit	Maximum number of iterations allowed, a positive integer. IterationsLimit
minimum point	Minimum point of objective function. MinimumPoint
minimum value	Minimum value of objective function. MinimumValue
status	Status of algorithm termination. Status

Reference

FinMath.NumericalOptimization.Constrained

FinMath.NumericalOptimization.Unconstrained