public class LowerSPDDenseMatrix extends LowerSymmDenseMatrix
LowerSymmDenseMatrix. This
class does not enforce the SPD property, but serves as a tag so that more
efficient algorithms can be used in the solvers.Matrix.NormnumColumns, numRows| Constructor and Description |
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LowerSPDDenseMatrix(int n)
Constructor for LowerSPDDenseMatrix
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LowerSPDDenseMatrix(Matrix A)
Constructor for LowerSPDDenseMatrix
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LowerSPDDenseMatrix(Matrix A,
boolean deep)
Constructor for LowerSPDDenseMatrix
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| Modifier and Type | Method and Description |
|---|---|
LowerSPDDenseMatrix |
copy()
Creates a deep copy of the matrix
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double[] |
getData()
Returns the matrix contents.
|
Matrix |
multAdd(double alpha,
Matrix B,
Matrix C)
C = alpha*A*B + C |
Vector |
multAdd(double alpha,
Vector x,
Vector y)
y = alpha*A*x + y |
Matrix |
rank1(double alpha,
Matrix C)
A = alpha*C*CT + A. |
Matrix |
rank1(double alpha,
Vector x,
Vector y)
A = alpha*x*yT + A. |
Matrix |
rank2(double alpha,
Matrix B,
Matrix C)
A = alpha*B*CT + alpha*C*BT + A. |
Matrix |
rank2(double alpha,
Vector x,
Vector y)
A = alpha*x*yT + alpha*y*xT + A. |
Matrix |
set(Matrix B)
A=B. |
Matrix |
solve(Matrix B,
Matrix X)
X = A\B. |
Vector |
solve(Vector b,
Vector x)
x = A\b. |
String |
toString() |
Matrix |
transAmultAdd(double alpha,
Matrix B,
Matrix C)
C = alpha*AT*B + C |
Vector |
transMultAdd(double alpha,
Vector x,
Vector y)
y = alpha*AT*x + y |
Matrix |
transpose()
Transposes the matrix in-place.
|
Matrix |
transRank1(double alpha,
Matrix C)
A = alpha*CT*C + A The matrices must be square
and of the same size |
Matrix |
transRank2(double alpha,
Matrix B,
Matrix C)
A = alpha*BT*C + alpha*CT*B + A. |
Matrix |
transSolve(Matrix B,
Matrix X)
X = AT\B. |
Vector |
transSolve(Vector b,
Vector x)
x = AT\b. |
Matrix |
zero()
Zeros all the entries in the matrix, while preserving any underlying
structure.
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add, get, setadd, add, check, checkMultAdd, checkMultAdd, checkRank1, checkRank1, checkRank2, checkRank2, checkSize, checkSolve, checkSolve, checkTransABmultAdd, checkTransAmultAdd, checkTransBmultAdd, checkTransMultAdd, checkTranspose, checkTranspose, checkTransRank1, checkTransRank2, isSquare, iterator, max, max, mult, mult, mult, mult, multAdd, multAdd, norm, norm1, normF, normInf, numColumns, numRows, rank1, rank1, rank1, rank1, rank2, rank2, scale, set, transABmult, transABmult, transABmultAdd, transABmultAdd, transAmult, transAmult, transAmultAdd, transBmult, transBmult, transBmultAdd, transBmultAdd, transMult, transMult, transMultAdd, transpose, transRank1, transRank2clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitforEach, spliteratorpublic LowerSPDDenseMatrix(int n)
n - Size of the matrix. Since the matrix must be square, this
equals both the number of rows and columnspublic LowerSPDDenseMatrix(Matrix A)
A - Matrix to copy. It must be a square matrix, and only the lower
triangular part is copiedpublic LowerSPDDenseMatrix(Matrix A, boolean deep)
A - Matrix to copy. It must be a square matrix, and only the lower
triangular part is copieddeep - False for a shallow copy, else it'll be a deep copy. For
shallow copies, A must be a dense matrixpublic LowerSPDDenseMatrix copy()
Matrixcopy in interface Matrixcopy in class LowerSymmDenseMatrixpublic Matrix solve(Matrix B, Matrix X)
MatrixX = A\B. Not all matrices support this operation, those that
do not throw UnsupportedOperationException. Note that it is
often more efficient to use a matrix decomposition and its associated
solverpublic Matrix multAdd(double alpha, Matrix B, Matrix C)
MatrixC = alpha*A*B + CmultAdd in interface MatrixmultAdd in class AbstractMatrixB - Matrix such that B.numRows() == A.numColumns()
and B.numColumns() == C.numColumns()C - Matrix such that C.numRows() == A.numRows() and
B.numColumns() == C.numColumns()public Matrix transAmultAdd(double alpha, Matrix B, Matrix C)
MatrixC = alpha*AT*B + CtransAmultAdd in interface MatrixtransAmultAdd in class AbstractMatrixB - Matrix such that B.numRows() == A.numRows() and
B.numColumns() == C.numColumns()C - Matrix such that C.numRows() == A.numColumns()
and B.numColumns() == C.numColumns()public Matrix rank1(double alpha, Vector x, Vector y)
MatrixA = alpha*x*yT + A. The matrix must be square,
and the vectors of the same lengthrank1 in interface Matrixrank1 in class AbstractMatrixpublic Matrix rank2(double alpha, Vector x, Vector y)
MatrixA = alpha*x*yT + alpha*y*xT + A. The
matrix must be square, and the vectors of the same lengthrank2 in interface Matrixrank2 in class AbstractMatrixpublic Vector multAdd(double alpha, Vector x, Vector y)
Matrixy = alpha*A*x + ymultAdd in interface MatrixmultAdd in class AbstractMatrixx - Vector of size A.numColumns()y - Vector of size A.numRows()public Vector transMultAdd(double alpha, Vector x, Vector y)
Matrixy = alpha*AT*x + ytransMultAdd in interface MatrixtransMultAdd in class AbstractMatrixx - Vector of size A.numRows()y - Vector of size A.numColumns()public Matrix rank1(double alpha, Matrix C)
MatrixA = alpha*C*CT + A. The matrices must be square
and of the same sizerank1 in interface Matrixrank1 in class AbstractMatrixpublic Matrix transRank1(double alpha, Matrix C)
MatrixA = alpha*CT*C + A The matrices must be square
and of the same sizetransRank1 in interface MatrixtransRank1 in class AbstractMatrixpublic Matrix rank2(double alpha, Matrix B, Matrix C)
MatrixA = alpha*B*CT + alpha*C*BT + A. This
matrix must be squarerank2 in interface Matrixrank2 in class AbstractMatrixB - Matrix with the same number of rows as A and the
same number of columns as CC - Matrix with the same number of rows as A and the
same number of columns as Bpublic Matrix transRank2(double alpha, Matrix B, Matrix C)
MatrixA = alpha*BT*C + alpha*CT*B + A. This
matrix must be squaretransRank2 in interface MatrixtransRank2 in class AbstractMatrixB - Matrix with the same number of rows as C and the
same number of columns as AC - Matrix with the same number of rows as B and the
same number of columns as Apublic Vector solve(Vector b, Vector x)
Matrixx = A\b. Not all matrices support this operation, those that
do not throw UnsupportedOperationException. Note that it is
often more efficient to use a matrix decomposition and its associated
solversolve in interface Matrixsolve in class AbstractMatrixb - Vector of size A.numRows()x - Vector of size A.numColumns()public Matrix transSolve(Matrix B, Matrix X)
MatrixX = AT\B. Not all matrices support this
operation, those that do not throw
UnsupportedOperationException. Note that it is often more
efficient to use a matrix decomposition and its associated transpose
solvertransSolve in interface MatrixtransSolve in class AbstractMatrixB - Matrix with a number of rows equal A.numColumns()
, and the same number of columns as XX - Matrix with the same number of rows as A, and the
same number of columns as Bpublic Vector transSolve(Vector b, Vector x)
Matrixx = AT\b. Not all matrices support this
operation, those that do not throw
UnsupportedOperationException. Note that it is often more
efficient to use a matrix decomposition and its associated solvertransSolve in interface MatrixtransSolve in class AbstractMatrixb - Vector of size A.numColumns()x - Vector of size A.numRows()public Matrix transpose()
Matrixtranspose in interface Matrixtranspose in class AbstractMatrixpublic double[] getData()
public Matrix set(Matrix B)
MatrixA=B. The matrices must be of the same sizeset in interface Matrixset in class AbstractMatrixpublic Matrix zero()
Matrixzero in interface Matrixzero in class AbstractMatrixpublic String toString()
toString in class AbstractMatrixCopyright © 2015. All Rights Reserved.