| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.minimization |
Provides minimization algorithms.
|
| gov.sandia.cognition.learning.algorithm.minimization.line |
Provides line (scalar) minimization algorithms.
|
| gov.sandia.cognition.learning.algorithm.minimization.matrix |
Provides matrix solving algorithms.
|
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| gov.sandia.cognition.statistics.method |
Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.
|
| Class and Description |
|---|
| AbstractAnytimeFunctionMinimizer
A partial implementation of a minimization algorithm that is iterative,
stoppable, and approximate.
|
| FunctionMinimizer
Interface for unconstrained minimization of nonlinear functions.
|
| FunctionMinimizerConjugateGradient
Conjugate gradient method is a class of algorithms for finding the
unconstrained local minimum of a nonlinear function.
|
| FunctionMinimizerQuasiNewton
This is an abstract implementation of the Quasi-Newton minimization method,
sometimes called "Variable-Metric methods."
This family of minimization algorithms uses first-order gradient information
to find a locally minimum to a scalar function.
|
| Class and Description |
|---|
| AbstractAnytimeFunctionMinimizer
A partial implementation of a minimization algorithm that is iterative,
stoppable, and approximate.
|
| FunctionMinimizer
Interface for unconstrained minimization of nonlinear functions.
|
| Class and Description |
|---|
| FunctionMinimizer
Interface for unconstrained minimization of nonlinear functions.
|
| Class and Description |
|---|
| FunctionMinimizer
Interface for unconstrained minimization of nonlinear functions.
|
| Class and Description |
|---|
| FunctionMinimizer
Interface for unconstrained minimization of nonlinear functions.
|