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Matrix
.Vector
.A(row,column) += value
A = B + A
.
A = alpha*B + A
.
x(index) += value
x = y + x
x = alpha*y + x
mult
and multAdd
mult
and multAdd
IterativeSolver.solve(Matrix, Vector, Vector)
.
transABmultAdd
and
transABmultAdd
transAmult
and
transAmultAdd
transBmult
and
transBmultAdd
transMult
and
transMultAdd
A = P * L * U
.xT*y
A(row,column)
x(index)
true
if the matrix is in array format, else
false
true
if the vector is in array format, else
false
true
if the matrix stores complex numbers, else
false
true
if the vector stores complex numbers, else
false
true
if the matrix is in coordinate format, else
false
true
if the vector is in coordinate format, else
false
true
if the matrix is in array format, else
false
true
if the vector is in array format, else
false
true
if the matrix form is general, else
false
true
if the matrix is Hermitian, else
false
true
if the matrix stores integers, else
false
true
if the vector stores integers, else
false
true
if the matrix does not store any numbers, else
false
true
if the vector does not store any numbers, else
false
true
if the matrix stores real numbers, else
false
true
if the vector stores real numbers, else
false
true
if the matrix is skew-symmetrical, else
false
true
if the matrix is in coordinate format, else
false
true
if the vector is in coordinate format, else
false
true
if the matrix is square, else
false
true
if the matrix is symmetrical, else
false
n x m
Matrix with z
elements that has a typical
O(z / m)
insertion / lookup cost and an iterator that traverses
columns then rows: a good fit for unstructured sparse matrices.y = A*x
y = alpha*A*x
C = A*B
C = alpha*A*B
y = A*x + y
y = alpha*A*x + y
C = A*B + C
C = alpha*A*B + C
System.err
.
A*P = Q*R
where
A(m,n)
, Q(m,m)
, and R(m,n)
, more generally:
A*P = [Q1 Q2] * [R11, R12; 0 R22]
and R22
elements are
negligible.A = x*xT + A
.
A = alpha*x*xT + A
.
A = x*yT + A
.
A = alpha*x*yT + A
.
A = C*CT + A
.
A = alpha*C*CT + A
.
A = x*yT + y*xT + A
.
A = alpha*x*yT + alpha*y*xT + A
.
A = B*CT + C*BT + A
.
A = alpha*B*CT + alpha*C*BT + A
.
A = alpha*A
x=alpha*x
A(row,column) = value
A=B
.
A=alpha*B
.
x(index) = value
x=y
x=alpha*y
A\B
, overwriting B
A\B
, overwriting B
B
, overwriting it on return
A\B
, overwriting B
x = A\b
.
X = A\B
.
B
, overwriting it on return
C = AT*BT
C = alpha*AT*BT
C = AT*BT + C
C = alpha*AT*BT + C
C = AT*B
C = alpha*AT*B
C = AT*B + C
C = alpha*AT*B + C
C = A*BT
C = alpha*A*BT
C = A*BT + C
C = alpha*A*BT + C
y = AT*x
y = alpha*AT*x
y = AT*x + y
y = alpha*AT*x + y
B
.
A = CT*C + A
The matrices must be square and of
the same size
A = alpha*CT*C + A
The matrices must be square
and of the same size
A = BT*C + CT*B + A
.
A = alpha*BT*C + alpha*CT*B + A
.
AT\B
, overwriting B
AT\B
, overwriting B
x = AT\b
.
X = AT\B
.
A
equal zero, and puts
diagonal
on the diagonal of those columns.
A
equal zero, and puts
diagonal
on the diagonal of those rows.
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