Package | Description |
---|---|
com.imsl.math |
Mathematical functions and algorithms.
|
com.imsl.stat |
Statistical methods.
|
com.imsl.stat.distributions |
Probability distributions and parameter estimation.
|
com.imsl.test.example.math |
Math examples.
|
Modifier and Type | Method and Description |
---|---|
void |
FeynmanKac.computeCoefficients(int numLeftBounds,
int numRightBounds,
FeynmanKac.Boundaries pdeBounds,
double[] xGrid,
double[] tGrid)
Determines the coefficients of the Hermite quintic splines that represent
an approximate solution for the Feynman-Kac PDE.
|
double |
SuperLU.getConditionNumber()
Returns the estimate of the reciprocal condition number of the matrix
A.
|
double |
ComplexSuperLU.getConditionNumber()
Returns the estimate of the reciprocal condition number of the matrix
A.
|
double |
SuperLU.getReciprocalPivotGrowthFactor()
Returns the reciprocal pivot growth factor.
|
double |
ComplexSuperLU.getReciprocalPivotGrowthFactor()
Returns the reciprocal pivot growth factor.
|
void |
LinearProgramming.solve()
Deprecated.
Solves the program using the revised simplex algorithm.
|
Complex[] |
ComplexSuperLU.solve(Complex[] b)
Computation of the solution vector for the system
\( Ax = b\).
|
static Complex[] |
ComplexLU.solve(Complex[][] a,
Complex[] b)
Solve Ax = b for x using the LU factorization of A.
|
double[] |
QR.solve(double[] b)
Returns the solution to the least-squares problem Ax = b.
|
double[] |
GenMinRes.solve(double[] b)
Generate an approximate solution to \(Ax=b\) using the Generalized
Residual Method.
|
double[] |
SuperLU.solve(double[] b)
Computation of the solution vector for the system \( Ax = b\).
|
double[] |
ConjugateGradient.solve(double[] b)
Solves a real symmetric positive or negative definite system \(Ax=b\) using
a conjugate gradient method with or without preconditioning.
|
static double[] |
LU.solve(double[][] a,
double[] b)
Solve Ax = b for x using the LU factorization of A.
|
double[] |
QR.solve(double[] b,
double tol)
Returns the solution to the least-squares problem Ax = b using
an input tolerance.
|
Complex[] |
ComplexSuperLU.solveConjugateTranspose(Complex[] b)
Computation of the solution vector for the system
\( A^Hx = b\).
|
Complex[] |
ComplexSuperLU.solveTranspose(Complex[] b)
Computation of the solution vector for the system
\( A^Tx = b\).
|
double[] |
SuperLU.solveTranspose(double[] b)
Computation of the solution vector for the system \( A^Tx = b\).
|
Constructor and Description |
---|
Cholesky(double[][] a)
Create the Cholesky factorization of a symmetric positive definite
matrix of type
double . |
ComplexLU(Complex[][] a)
Creates the LU factorization of a square matrix of type
Complex . |
CsShape(double[] xData,
double[] yData)
Construct a cubic spline interpolant which is consistent
with the concavity of the data.
|
LU(double[][] a)
Creates the LU factorization of a square matrix of type
double . |
Modifier and Type | Method and Description |
---|---|
void |
AutoARIMA.compute(int maxlag)
Estimates potential missing values, detects and determines outliers and
simultaneously fits an optimum model from a set of different \(
\text{ARIMA}(p,0,0)\times(0,d,0)_s\) models to the outlier free
time series.
|
void |
ARMAOutlierIdentification.compute(int[] model)
Detects and determines outliers and simultaneously estimates the model
parameters for the given time series.
|
void |
AutoARIMA.compute(int[] arOrders,
int[] maOrders)
Estimates potential missing values, detects and determines outliers and
simultaneously fits an optimum model from a set of different \(
\text{ARIMA}(p,0,q)\times(0,d,0)_s\) models to the outlier free
time series.
|
void |
AutoARIMA.compute(int p,
int q,
int s,
int d)
Estimates potential missing values, detects and determines outliers and
simultaneously fits an \(\text{ARIMA}(p,0,q)\times(0,d,0)_s
\) model to the outlier free time series.
|
double[][] |
FactorAnalysis.getFactorScoreCoefficients(FactorAnalysis.ScoreMethod method)
Computes the matrix of factor score coefficients.
|
Modifier and Type | Method and Description |
---|---|
double[] |
MaximumLikelihoodEstimation.getStandardErrors()
Returns the approximate standard errors of the maximum likelihood
estimates.
|
double[][] |
MaximumLikelihoodEstimation.getVarCov()
Returns the approximate variance-covariance matrix of the maximum
likelihood estimates.
|
Modifier and Type | Method and Description |
---|---|
static void |
QREx1.main(String[] args) |
static void |
LUEx1.main(String[] args) |
static void |
ComplexEx1.main(String[] args) |
static void |
ComplexLUEx1.main(String[] args) |
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