edu.stanford.nlp.optimization
Class QNMinimizer.QNInfo
java.lang.Object
edu.stanford.nlp.optimization.QNMinimizer.QNInfo
- Enclosing class:
- QNMinimizer
public class QNMinimizer.QNInfo
- extends Object
Method Summary |
double[] |
applyInitialHessian(double[] x)
|
void |
clear()
|
void |
free()
|
double |
getRho(int ind)
|
double[] |
getS(int ind)
|
double[] |
getY(int ind)
|
void |
setHistory(List<double[]> sList,
List<double[]> yList)
|
int |
size()
|
int |
update(double[] newX,
double[] x,
double[] newGrad,
double[] grad,
double step)
|
int |
update(double[] newS,
double[] newY,
double yy,
double sy,
double sg,
double step)
|
void |
useDiagonalScaling()
|
void |
useScalarScaling()
|
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
d
public double[] d
scaleOpt
public QNMinimizer.eScaling scaleOpt
QNMinimizer.QNInfo
public QNMinimizer.QNInfo(int size)
QNMinimizer.QNInfo
public QNMinimizer.QNInfo()
QNMinimizer.QNInfo
public QNMinimizer.QNInfo(List<double[]> sList,
List<double[]> yList)
size
public int size()
getRho
public double getRho(int ind)
getS
public double[] getS(int ind)
getY
public double[] getY(int ind)
useDiagonalScaling
public void useDiagonalScaling()
useScalarScaling
public void useScalarScaling()
free
public void free()
clear
public void clear()
setHistory
public void setHistory(List<double[]> sList,
List<double[]> yList)
applyInitialHessian
public double[] applyInitialHessian(double[] x)
update
public int update(double[] newX,
double[] x,
double[] newGrad,
double[] grad,
double step)
throws QNMinimizer.SurpriseConvergence
- Throws:
QNMinimizer.SurpriseConvergence
update
public int update(double[] newS,
double[] newY,
double yy,
double sy,
double sg,
double step)
Stanford NLP Group