SuanShu, a Java numerical and statistical library
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L

L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination
Get a copy of the lower triangular matrix L, such that P %*% A == L %*% U
L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
 
L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Cholesky
Get a copy of the lower triangular matrix L as in L %*% Lt.
L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Doolittle
 
L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LDL
Get a copy of the lower triangular matrix L as in A = L %*% D %*% Lt
L() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LU
 
L() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LUDecomposition
Get a copy of the lower triangular matrix L as in P %*% A == L %*% U
L - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.ForwardSubstitution
the system of linear equations (the homogeneous part)
L - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LU
matrix L as in LUx = PAx = Pb
L() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.hessian.MatthewsDavies
Get a copy of the lower triangular matrix L in the LDL decomposition.
lag(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
Construct a new and independent SimpleMultiVariateTimeSeries by lagging the time series.
lag(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
Construct a new and independent SimpleMultiVariateTimeSeries by lagging the time series.
lag(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
Construct a new and independent SimpleTimeSeries by lagging the time series.
lag(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
Construct a new and independent SimpleTimeSeries by lagging the time series.
lagAdjust - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfFiniteSampleDistribution
indicate whether the distribution is adjusted for lags
lagOrder - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfFiniteSampleDistribution
the lag order used to calculate the test statistics; lagOrder = 0 yields the Dickey-Fuller distribution
lagOrder - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
the lag order
lambda - Variable in class com.numericalmethod.suanshu.matrix.doubles.operation.Householder.Context
the norm of generator with the sign chosen to be the opposite of the first coordinate of generator
lambda - Variable in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
the rate parameter
lambda - Variable in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
the scale parameter
lambdaCol() - Method in interface com.numericalmethod.suanshu.optimization.constrained.linearprogramming.LpSolver
Get the column index for which there is no row that passes the ratio test.
lambdaCol() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.Phase2ByFerrisMangasarianWright
 
lambdaCol() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.StandardSimplex
 
lastValue() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
 
lastValue() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization
Get the ending value of a realization, i.e., the value at the end of the time interval, e.g., ω(T).
lastValue() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk.Realization
 
lastValue() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization
Get the ending value of a realization, i.e., the value at the end of the time interval, e.g., ω(T).
LDL - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
LDL decomposition decomposes a real and symmetric (hence square) matrix A into A = L %*% D %*% Lt L is a lower triangular matrix.
LDL(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LDL
Construct an instance of the LDL decomposition.
LeastPth - Class in com.numericalmethod.suanshu.optimization.minmax
The least p-th minmax algorithm solves the minmax problem in this form: minimize max e(x, ω) x ω∈S e(x, ω) is the error or loss function.
LeastPth(MinMaxProblem) - Constructor for class com.numericalmethod.suanshu.optimization.minmax.LeastPth
Construct an instance of the LeastPth to solve a minmax problem.
Lebesgue - Class in com.numericalmethod.suanshu.analysis.integration.univariate
Lebesgue integration is the general theory of integration of a function with respect to a general measure.
Lebesgue() - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.Lebesgue
Construct an empty Lebesgue integral.
Lebesgue(double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.Lebesgue
Construct a Lebesgue integral.
LEcuyer - Class in com.numericalmethod.suanshu.stats.random.pseudorandom.linear
This implements the random number generator recommended by L'Ecuyer in 1996.
LEcuyer() - Constructor for class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.LEcuyer
Construct a LEcuyer pseudo uniform random generator.
LEcuyer(long, long, long, long, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.LEcuyer
Construct a LEcuyer pseudo uniform random generator and then seed.
leftConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.mean.T
Compute the one sided left confidence interval, [0, a]
leftConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.variance.F
Compute the one sided left confidence interval, [0, a]
leftPreconditioner(Preconditioner) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.Problem
Override the left preconditioner.
leftPreconditioner() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.Problem
Get the left preconditioner.
Lehmer - Class in com.numericalmethod.suanshu.stats.random.pseudorandom.linear
Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in [0, 1].
Lehmer(long, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.Lehmer
Construct a Lehmer (pure) linear congruential generator.
Lehmer(long, long, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.Lehmer
Construct a skipping ahead Lehmer (pure) linear congruential generator.
Lehmer() - Constructor for class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.Lehmer
Construct a Lehmer (pure) linear congruential generator using default values.
length - Variable in class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
length of the sequence
length() - Method in class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
 
length() - Method in interface com.numericalmethod.suanshu.analysis.sequence.Sequence
Get the number of computed terms in the sequence.
length - Variable in class com.numericalmethod.suanshu.stats.timeseries.TimeSeries.Iterator
the length of the time series
length - Variable in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
length of this vector
length - Variable in class com.numericalmethod.suanshu.vector.doubles.dense.operation.Projection
the length of v projected on each dimension {wi}
Levene - Class in com.numericalmethod.suanshu.stats.test.variance
The Levene test tests for the equality of variance of groups.
Levene(double...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.Levene
Perform the Levene test to test for homeogeneity of variance across groups.
Levene(Levene.Type, double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.Levene
Perform the Levene test to test for homeogeneity of variance across groups.
Levene.Type - Enum in com.numericalmethod.suanshu.stats.test.variance
the implementations available when computing the absolute deviations
leverage - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
leverage; the bigger the leverage for an observation, the bigger influence on the prediction
License - Class in com.numericalmethod.suanshu.license
This is the license management system.
LICENSE_FILE_PROPERTY - Static variable in class com.numericalmethod.suanshu.license.License
The system property name for setting license file.
Lilliefors - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
Lilliefors test tests the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance.
Lilliefors(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
Perform the Lilliefors test to test for the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance.
LilSparseMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
The list of lists (LIL) format for sparse matrix.
LilSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LilSparseMatrix
Create an instance of LIL sparse matrix with the matrix dimension.
LilSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LilSparseMatrix
Create an instance of LIL sparse matrix with non-zero values.
LilSparseMatrix(int, int, List<SparseElement>) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LilSparseMatrix
Create an instance of LIL sparse matrix with a list of non-zero SparseElements.
LilSparseMatrix(LilSparseMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LilSparseMatrix
Copy constructor.
Linear - Class in com.numericalmethod.suanshu.analysis.function.polynomial.root
This is a linear equation solver.
Linear() - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.root.Linear
 
LinearCongruentialGenerator - Interface in com.numericalmethod.suanshu.stats.random.pseudorandom.linear
A linear congruential generator (LCG) produces a sequence of pseudo-random numbers based on a linear recurrence relation.
LinearKalmanFilter - Class in com.numericalmethod.suanshu.stats.dlm
 
LinearKalmanFilter(Dlm) - Constructor for class com.numericalmethod.suanshu.stats.dlm.LinearKalmanFilter
 
LinearRepresentation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma
This class computes the linear representation of an Autoregressive Moving Average (ARMA) model.
LinearRepresentation(ArmaModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.LinearRepresentation
Construct the linear representation of an ARMA model.
LinearRepresentation(ArmaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.LinearRepresentation
Construct the linear representation of an ARMA model up to the default number of lags LinearRepresentation.DEFAULT_NUMBER_OF_LAGS.
LinearRepresentation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma
This class computes the linear representation of an Autoregressive Moving Average (ARMA) model.
LinearRepresentation(ArmaModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.LinearRepresentation
Construct the linear representation of an ARMA model.
LinearRepresentation(ArmaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.LinearRepresentation
Construct the linear representation of an ARMA model.
linearSpan(double...) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.VectorSpace
Deprecated. Not supported yet.
linesearch - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
the line search method to use
link() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Family
Get the link function of this distribution.
link() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.QuasiFamily
 
LinkFunction - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
This interface represents a link function g(x) in the Generalized Linear Model (GLM).
LjungBox - Class in com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
The Ljung–Box test (named for Greta M.
LjungBox(double[], int, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.LjungBox
 
LmProblem - Class in com.numericalmethod.suanshu.stats.regression.linear
This class represents a linear regression or a linear model (LM) problem.
LmProblem(Vector, Matrix, boolean, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LmProblem
Construct a linear regression problem.
LmProblem(Vector, Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LmProblem
Construct a linear regression problem, assuming a constant term (the intercept).
LmProblem(Vector, Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LmProblem
Construct a linear regression problem, assuming equal weights to all observations.
LmProblem(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LmProblem
Construct a linear regression problem, assuming a constant term (the intercept) equal weights to all observations
LmProblem(LmProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LmProblem
Copy constructor.
log(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
Compute log(x).
log(BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
Compute log(x) up to a scale.
log(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
Natural logarithm of a complex number (a + bi).
Log - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
This class represents the link function: g(x) = log(x)
Log() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Log
 
LogBeta - Class in com.numericalmethod.suanshu.analysis.function.special
This class represents the log of Beta function log(B(x, y)).
LogBeta() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.LogBeta
 
LogGamma - Class in com.numericalmethod.suanshu.analysis.function.special
This computes an approximation to the log Gamma function, log(Γ(z)), for positive real numbers.
LogGamma() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.LogGamma
Construct an instance to compute for log Gamma function with default settings.
LogGamma(LogGamma.Method, double, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.LogGamma
Construct an instance to compute for log Gamma function.
LogGamma.Method - Enum in com.numericalmethod.suanshu.analysis.function.special
the methods available to compute log(Γ(z))
Logistic - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
A logistic regression (sometimes called the logistic model or logit model) is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve.
Logistic(LmProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.logistic.Logistic
Construct a Logistic instance.
LogisticProblem - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
This class represents a logistic regression problem.
LogisticProblem(LmProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticProblem
 
Logit - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
This class represents the link function: mu g(x) = log(--------) 1 - mu
Logit() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Logit
 
logLikelihood() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.Fitting
 
logLikelihood() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
 
logLikelihood(double[], int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.Garch
the log-likelihood function for a set of observations The log-likelihood takes θ as the inputs.
LONDON - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
London
longValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
Deprecated. Not supported yet.
longValue() - Method in class com.numericalmethod.suanshu.number.Real
 
longValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
 
LoopBody - Interface in com.numericalmethod.suanshu.parallel
The implementation of this interface contains the code inside a for-loop construct.
lowerBidiagonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
Check if a matrix is lower bidiagonal.
lowerTriangular(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
Check if a matrix is lower triangular.
LowerTriangularMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle
This class implements the lower triangular matrix, which has 0 entries whenever column index > row index.
LowerTriangularMatrix(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
Construct a lower triangular matrix of dimension dim * dim.
LowerTriangularMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
Construct a lower triangular matrix from a 2D double[][] array.
LowerTriangularMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
Construct a lower triangular matrix from a matrix.
LowerTriangularMatrix(LowerTriangularMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
Copy constructor performing a deep copy.
LpBoundedSolution - Class in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard
This class represents a solution to a bounded Linear Programming (LP) problem.
LpBoundedSolution(LpSolver) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.LpBoundedSolution
Construct the solution for a bounded LP problem.
LpProblem - Interface in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
This defines an abstract Linear Programming (LP) problem.
LpProblem.DimensionNotMatched - Exception in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
 
LpProblem.DimensionNotMatched(String) - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.LpProblem.DimensionNotMatched
 
LpProblem.EmptyCostVector - Exception in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
 
LpProblem.EmptyCostVector() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.LpProblem.EmptyCostVector
 
LpProblem.Infeasible - Exception in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
 
LpProblem.Infeasible() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.LpProblem.Infeasible
 
LpProblem.LpException - Exception in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
 
LpProblem.LpException() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.LpProblem.LpException
 
LpProblem.LpRuntimeException - Exception in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
 
LpProblem.LpRuntimeException() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.LpProblem.LpRuntimeException
 
LpProblem.LpRuntimeException(String) - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.LpProblem.LpRuntimeException
 
LpProblem.NoConstraint - Exception in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
 
LpProblem.NoConstraint() - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.LpProblem.NoConstraint
 
LpProblem.Unbounded - Exception in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
 
LpProblem.Unbounded(int) - Constructor for exception com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.LpProblem.Unbounded
 
LpSolution - Interface in com.numericalmethod.suanshu.optimization.constrained.linearprogramming
The interface represents the common part of various Linear Programming (LP) solutions, e.g., bounded optimal solutions, multiple solutions, unbounded LP.
LpSolver - Interface in com.numericalmethod.suanshu.optimization.constrained.linearprogramming
This interface represents the various stages in solving a Linear Programming (LP) problem.
LpUnboundedSolution - Class in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard
This class represents a solution to an unbounded Linear Programming (LP) problem.
LpUnboundedSolution(LpSolver) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.LpUnboundedSolution
Construct the solution for an unbounded LP problem.
lr - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Hessenberg.Deflation
H33, upper quasi-triangular, in Algorithm 7.5.2 has dimension (n-lr) x (n-lr).
Lt() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Cholesky
Get a copy of the transpose of the lower triangular matrix L as in A = L %*% Lt.
Lt() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LDL
Get a copy of the transpose of the lower triangular matrix L as in A = L %*% Lt The transpose is upper triangular.
Lt() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.hessian.MatthewsDavies
Get the transpose of the lower triangular matrix L in the LDL decomposition.
LU - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
LU decomposition of a matrix.
LU(Matrix, LU.Method, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LU
Construct an instance of the LU decomposition that uses Gaussian Elimination.
LU(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LU
Construct an instance of the LU decomposition.
LU - Class in com.numericalmethod.suanshu.matrix.doubles.linearsystem
Use the LU decomposition to solve Ax = b where A is square and det(A) !
LU(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LU
Construct an LU instance to solve for different Vector b's.
LU.Method - Enum in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
the methods available to do the LU decomposition
LUDecomposition - Interface in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
All LU decomposition algorithms implement this interface.

SuanShu, a Java numerical and statistical library
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