objectives
: Information-Theoretic Objectives¶
This module defines the objective functions that the optimization methods aim to maximize. Use the get_objective
method to retrieve an objective function class by its string name.
-
MI
,SLogMI
, andSchurMI
: These classes compute the Mutual Information (MI) between a set of sensing locations \(X\) and a set of objective locations \(X_{objective}\), using the kernel fuunction \(K\):-
MI
: A naive implementation of MI. -
SLogMI
: Uses a numerically stable implementation of MI based on the log-determinant of the covariance matrix. -
SchurMI
: Computes MI using the Schur complement for improved numerical stability and computational efficiency.
-
-
AOptimal
: Computes the A-optimal design metric, which minimizes \(Tr(K(X, X))\). -
BOptimal
: Computes the B-optimal design metric, which minimizes \(-Tr(K(X, X)^{-1})\). -
DOptimal
: Computes the D-optimal design metric, which minimizes \(|K(X, X)|\).
sgptools.methods.get_objective(objective_name)
¶
Retrieves an objective function class by its string name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
objective_name
|
str
|
The name of the objective function (e.g., 'MI', 'SLogMI'). |
required |
Returns:
Type | Description |
---|---|
Type[Objective]
|
Type[Objective]: The class of the requested objective function. |
Raises:
Type | Description |
---|---|
KeyError
|
If the objective name is not found in the registered OBJECTIVES. |